CN115876494B - Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop - Google Patents
Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop Download PDFInfo
- Publication number
- CN115876494B CN115876494B CN202310133131.6A CN202310133131A CN115876494B CN 115876494 B CN115876494 B CN 115876494B CN 202310133131 A CN202310133131 A CN 202310133131A CN 115876494 B CN115876494 B CN 115876494B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- energy management
- management strategy
- pedal
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000005540 biological transmission Effects 0.000 claims abstract description 64
- 238000012795 verification Methods 0.000 claims abstract description 10
- 239000012634 fragment Substances 0.000 claims description 67
- 230000014509 gene expression Effects 0.000 claims description 57
- 238000005265 energy consumption Methods 0.000 claims description 47
- 239000000446 fuel Substances 0.000 claims description 22
- 230000008859 change Effects 0.000 claims description 18
- 238000012360 testing method Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 abstract description 14
- 230000000694 effects Effects 0.000 abstract description 4
- 230000000007 visual effect Effects 0.000 description 13
- 230000001133 acceleration Effects 0.000 description 12
- 238000009826 distribution Methods 0.000 description 12
- 230000008569 process Effects 0.000 description 11
- 238000011084 recovery Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 6
- 230000033001 locomotion Effects 0.000 description 6
- 230000006399 behavior Effects 0.000 description 5
- 238000010219 correlation analysis Methods 0.000 description 4
- 102100029469 WD repeat and HMG-box DNA-binding protein 1 Human genes 0.000 description 2
- 101710097421 WD repeat and HMG-box DNA-binding protein 1 Proteins 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008846 dynamic interplay Effects 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002459 sustained effect Effects 0.000 description 1
Images
Landscapes
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention relates to a system and a method for evaluating an energy management strategy of a hybrid electric vehicle with a driver in a loop, belonging to the field of energy management strategies of hybrid transmission systems. According to the system and the method for evaluating the energy management strategy of the hybrid electric vehicle with the driver in the ring, the main influence of the driver on the speed control under different driving scenes is considered, the influence of the driver on the speed is used as the important input of the energy management strategy decision, the problem that the energy management strategy with the optimization capability at present ignores the key effect of the driver in the speed control and the torque demand is solved, meanwhile, the real-time verification of the energy management strategy in a real controller is focused, and meanwhile, the optimization direction can be provided for the evaluated energy management strategy according to the evaluation result.
Description
Technical Field
The invention belongs to the field of energy management strategies of hybrid transmission systems, and particularly relates to a system and a method for evaluating an energy management strategy of a hybrid electric vehicle with a driver in a loop.
Background
The hybrid electric vehicle has become an important technical route for realizing energy conservation and emission reduction of the vehicle industry, and an energy management strategy of the hybrid electric vehicle plays an important role in the development of the hybrid electric vehicle, so that on one hand, the energy management strategy with reasonable design can reasonably utilize fuel oil and electric energy under different driving conditions; on the other hand, through reasonable configuration scheme optimization and design parameter matching, not only can provide the excellent control platform of performance for hybrid transmission system energy management, but also can effectively satisfy the demand of driver to car speed and moment of torsion. Such as Chinese invention patent: CN107697063B and CN109131350B.
At present, researchers have proposed instantaneous optimization, rolling optimization, global optimization and learning-based energy management strategies in the field of hybrid transmission system energy management, and different energy management strategies have obvious energy conservation and emission reduction advantages. However, the energy management strategy with the optimization capability can only be verified in an offline simulation environment at present, and cannot be applied to a real vehicle of the vehicle, wherein one important reason is that key roles of a driver in the process of vehicle speed control and torque demand are ignored, the controllability of the driver on the vehicle speed is an important premise for ensuring the driving safety, in addition, the real-time performance of the energy management strategy directly influences the safety of the driving process, and the safety is the first priority of an energy management target.
In addition, the real-vehicle assessment of the vehicle on the energy management strategy with the optimization capability is a preferred verification scheme for verifying the capability of the vehicle, but due to the fact that the direct control of the power transmission system to the vehicle speed is involved, the power transmission cannot meet the speed requirement of a driver in time without the real-time verification of the energy management strategy, and serious safety risks and repeatability verification capability are caused. The simple driving simulator is combined with virtual scene simulation software to control the speed of the vehicle, but the driving experience of a driver is difficult to obtain.
Disclosure of Invention
In view of the above problems, the invention provides a system and a method for evaluating energy management strategies of a hybrid electric vehicle with drivers in a loop, which consider the main influence of the drivers on speed control in different driving scenes, take the influence of the drivers on the speed as an important input of energy management strategy decisions, solve the problem that the energy management strategies with optimizing capability ignore the key roles of the drivers in vehicle speed control and torque requirements, simultaneously focus on real-time verification of the energy management strategies in a real controller, and simultaneously can provide optimizing directions for the evaluated energy management strategies according to the evaluation results.
On one hand, the energy management strategy evaluation system of the hybrid electric vehicle with the driver in the ring comprises virtual scene simulation software, a multi-degree-of-freedom driving simulator, a controller, a vehicle hybrid transmission system and a test bench;
the virtual scene simulation software is used for constructing a long-time airspace virtual driving scene and a vehicle dynamics model and transmitting vehicle state signals;
the multi-degree-of-freedom driving simulator is respectively connected with the virtual scene simulation software and the controller and is used for inputting driving operation control signals and receiving vehicle dynamics signal feedback in the virtual scene simulation software and speed signal feedback in the controller;
The multi-degree-of-freedom driving simulator comprises an accelerator pedal, a brake pedal and a steering wheel;
the controller is also connected with the vehicle hybrid transmission system and is used for receiving driving operation control signals and/or information required by the energy management strategy decision to be evaluated of vehicle state signals in real time, and controlling the vehicle hybrid transmission system by analyzing the energy management strategy to be evaluated and deciding to generate a control command;
the vehicle hybrid transmission system is arranged in the test bench, and is used for executing the input of the control command in the controller and feeding back the vehicle state performance of the current period into the controller.
Further, the energy management policy under test is stored in the controller.
On the other hand, the method for evaluating the energy management strategy of the hybrid electric vehicle with the driver in the ring provided by the invention uses the evaluation system, and comprises the following specific steps:
step 1, constructing a long-time airspace virtual driving scene and a vehicle dynamics model in virtual scene simulation software;
step 2: leading the evaluation peak map to be tested and the evaluation energy management strategy to be tested into a controller; carrying out real-time verification on the real-time performance of the energy management strategy to be evaluated and the computing capacity of the controller, and outputting a real-time evaluation result;
Step 3: operating the multi-degree-of-freedom driving simulator according to the long-time airspace virtual driving scene to obtain driving operation control signals, vehicle state signals and vehicle state expression; obtaining a vehicle demand torque based on the evaluation peak map to be tested, the driving operation control signal and the vehicle state information; determining an operating mode of the vehicle hybrid powertrain based on the vehicle demand torque, the driving operation control signal, and the vehicle status signal;
step 4: analyzing driving intention changing moments according to driving operation control signals, and dividing pedal operation continuous fragments of an accelerator pedal and a brake pedal according to the driving intention changing moments; adding pedal operation fragment labels to the pedal operation continuous fragments; recording to-be-evaluated state expression data sets in pedal operation continuous fragments of an accelerator pedal and a brake pedal in sequence according to a time sequence order, and obtaining to-be-evaluated state expression data set sets of an to-be-evaluated energy management strategy;
step 5: acquiring the optimal energy consumption state expression of the hybrid transmission system of the vehicle according to the global optimal energy management strategy; dividing pedal operation continuous fragments represented by the optimal energy consumption state of the hybrid transmission system of the vehicle by using the pedal continuous fragment labels in the step 4; sequentially recording a global optimal energy management strategy data set in a global optimal pedal operation continuous segment according to a time sequence order, and obtaining an optimal energy consumption state expression data set of the global optimal energy management strategy;
Step 6: according to the to-be-evaluated state expression data set of the to-be-evaluated energy management strategy with time sequence arrangement in the step 4 and the optimal energy consumption state expression data set of the global optimal energy management strategy in the step 5, performing comparison evaluation of time sequence pedal operation fragment state expression to obtain a state expression evaluation result;
step 7: and outputting the real-time evaluation result of the step 2 and the state performance evaluation result of the step 6 as evaluation results of the energy management strategy to be evaluated.
Further, the to-be-tested evaluation state expression data set comprises a working mode of a vehicle hybrid transmission system of the to-be-tested evaluation state in the pedal continuous segment, rotating speeds and torques of different power sources in different working modes, a battery SoC change state, instantaneous fuel consumption and pedal operation continuous segment labels; the optimal energy consumption state expression data set comprises an operating mode of the vehicle hybrid transmission system with the optimal energy consumption state in the pedal continuous segment, rotating speeds and torques of different power sources in different operating modes, a battery SoC change state, an optimal state of instantaneous fuel consumption and a pedal operation continuous segment label.
Further, the comparison evaluation of the state expression of the time-series pedal operation section is performed, including the comparison evaluation of the pedal operation section using the same operation mode and the pedal operation section using different operation modes.
Compared with the prior art, the invention has at least the following beneficial effects:
(1) The evaluation system utilizes virtual scene simulation software, a multi-degree-of-freedom driving simulator, a real or prototype controller and a vehicle hybrid transmission system to perform virtual-real combined simulation, so that on one hand, the instantaneity and the energy consumption performance of an energy management strategy can be effectively verified, and on the other hand, the problem that the randomness influence of a driver on speed control and torque demand is ignored in the development process of the existing energy management strategy is solved.
(2) The evaluation system has the advantages of modularization, platformization and strong expansibility, can rapidly transplant different energy management strategies and replace hybrid transmission systems with different configurations, and can verify whether the calculation power of the controller adapted by the energy management strategies to be evaluated can meet the real-time calculation requirement.
(3) The evaluation method fully considers the influence of the driver on the speed control under different driving scenes, and can effectively avoid the driving safety problem caused by the difficulty in ensuring the real-time control of the energy management strategy in the real-vehicle verification process by utilizing the evaluation system.
(4) The evaluation method can obtain the individuality and commonality difference between the actual energy consumption state of the energy management strategy to be evaluated and the optimal energy consumption state of the global optimal energy management strategy, thereby indicating the direction for iterative optimization of the energy management strategy to be evaluated and real vehicle application of the vehicle.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention.
Fig. 1 is a schematic diagram of the evaluation system according to the present invention.
Fig. 2 is a flowchart of the evaluation method of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other. In addition, the invention may be practiced otherwise than as specifically described and thus the scope of the invention is not limited by the specific embodiments disclosed herein.
In one embodiment of the present invention, as shown in fig. 1, a system for evaluating energy management strategies of a hybrid electric vehicle with a driver in a loop is provided, which comprises virtual scene simulation software, a multi-degree-of-freedom driving simulator, a controller, a vehicle hybrid transmission system and a test bench;
the virtual scene simulation software is used for constructing a long-time airspace virtual driving scene and a vehicle dynamics model;
the multi-degree-of-freedom driving simulator is respectively connected with the virtual scene simulation software and the controller and comprises an accelerator pedal, a brake pedal and a steering wheel; the controller is also connected with a vehicle hybrid transmission system; the vehicle hybrid transmission system is arranged in the test bench; the driver operates the accelerator pedal, the brake pedal and the steering wheel to enable the vehicle dynamics model to run in the long-time airspace virtual driving scene.
Optionally, the controller is a real controller or a prototype controller; the energy management strategy to be evaluated is stored in the controller, and preferably, the energy management strategy to be evaluated is generated through codes and burnt into the controller; the controller is used for receiving information required by energy management strategy decisions such as driving operation control signals (including accelerator pedal signals, brake pedal signals and steering wheel signals) and/or vehicle state signals (including vehicle speed signals, vehicle acceleration signals and the like) in real time, and controlling the vehicle hybrid transmission system by analyzing the energy management strategy and deciding to generate control commands. And acquiring information required by the energy management strategy decision to be evaluated according to the requirement of the energy management strategy to be evaluated.
As shown in fig. 1, the signal transmission flow among the multi-degree-of-freedom driving simulator, the controller, the vehicle hybrid transmission system and the virtual scene simulation software is as follows: constructing a long-time airspace virtual driving scene and a vehicle dynamics model in virtual scene simulation software; the method comprises the steps that a driver operates an accelerator pedal, a brake pedal and a steering wheel of a multi-degree-of-freedom driving simulator according to a long-time airspace virtual driving scene to generate driving operation control signals, the multi-degree-of-freedom driving simulator transmits the driving operation control signals (the accelerator pedal signals, the brake pedal signals and the steering wheel signals) to virtual scene simulation software and a controller, the virtual scene simulation software receives the driving operation control signals to carry out dynamic control on a vehicle dynamics model, and a visual animation (visual animation including information such as vehicle position, driving state and traffic information) vehicle state signals and vehicle dynamics signals are generated in the virtual scene simulation software, so that the vehicle motion and the expression state of the long-time airspace virtual driving scene in the virtual scene simulation software are displayed in a visual animation mode, and the driver adjusts driving operation by observing the visual animation; meanwhile, the virtual scene simulation software feeds back vehicle dynamics signals (such as characteristic signals of a vehicle yaw angle, a pitch angle and a roll angle) generated by driving operation to the multi-degree-of-freedom driving simulator, the multi-degree-of-freedom driving simulator simulates the real motion gesture of the vehicle according to the vehicle dynamics signals, and a driver feels the real motion gesture of the vehicle through the multi-degree-of-freedom driving simulator to form a closed-loop control flow of the driving operation; the virtual scene simulation software generates a vehicle state signal, and inputs the vehicle state signal (including a vehicle speed signal, a vehicle acceleration signal and the like) to the controller; the controller analyzes the vehicle state signals, generates power source control signals according to an energy management strategy to control the vehicle hybrid transmission system, and simultaneously feeds back the vehicle speed and vehicle acceleration signals to the multi-degree-of-freedom driving simulator and stores the vehicle speed change state; the vehicle hybrid transmission system controls the rotating speeds and the torques of different power sources according to the power source control signals, outputs the vehicle state expression of the vehicle hybrid transmission system in the current period in the test rack (the vehicle state expression at the current moment comprises the working modes of the vehicle hybrid transmission system, the rotating speeds and the torques of different power sources in different working modes, the change state of a battery SoC and the instantaneous fuel consumption), records the vehicle state expression in the current period, and feeds back the vehicle state expression to the controller to be used as the decision and judgment condition of the energy management strategy of the hybrid transmission system in the next period.
The speed output of the multi-degree-of-freedom driving simulator is equivalent to that of a vehicle hybrid transmission system in a test bench, so that the evaluation system can quickly replace and evaluate vehicle hybrid transmission systems with different energy management strategies and different configurations.
The evaluation system can store and record the working mode of the hybrid transmission system, the rotating speeds and the rotating torques of different power sources in different working modes, the change state of the battery SoC and the instantaneous fuel consumption, and can store and record driving operation control signals and vehicle states of a driver in different driving scenes.
In another embodiment of the present invention, as shown in fig. 2, a method for evaluating energy management strategies of a hybrid electric vehicle with a driver in a loop is disclosed, and the method for evaluating energy management strategies of a hybrid electric vehicle with a driver in a loop includes the following steps:
step 1, constructing a long-time airspace virtual driving scene and a vehicle dynamics model in virtual scene simulation software;
the method for constructing the long-time airspace virtual driving scene in the virtual scene simulation software mainly comprises two modes, namely, acquiring a high-precision map of a vehicle in a real long-time airspace virtual driving scene by utilizing a vehicle-mounted vision and radar sensor, directly importing the high-precision map into the virtual scene simulation software through format conversion, randomly generating a dynamic traffic state by utilizing a scene editor built in the virtual scene simulation software, and generating a visual scene image of the long-time airspace virtual driving scene based on the traffic state; and secondly, directly constructing a static road scene and a dynamic traffic scene through a scene editor in virtual scene simulation software, and generating a visual scene image of the long-time airspace virtual driving scene. Meanwhile, inputting target vehicle type parameters in the virtual scene simulation software to construct a vehicle dynamics model.
Optionally, the driving duration of the constructed long-time airspace virtual driving scene should exceed 30min or the driving mileage should exceed 20km, and the constructed long-time airspace virtual driving scene information includes traffic signal lamp states, traffic jam states, random front car states and the like, so that a complex random long-time airspace virtual driving scene is constructed, and the efficiency of evaluating the energy utilization performance of the hybrid transmission system of the vehicle is improved.
Further, the generated visual scene image of the long-time airspace virtual driving scene can be projected into a triple screen or a surrounding curtain to form a vivid visual driving scene, so that a driver can adjust driving operation behaviors according to driving scene changes. The constructed long-time airspace virtual driving scene information can be transmitted to the controller in real time in a data table form, such as timing states (red light timing, green light timing and the like) of traffic signals at different positions at each moment, traffic jam states (smooth, light jam, medium jam, heavy jam and the like) at different positions at each moment, and information of front vehicle states (front vehicle positions, front vehicle speeds and the like) of different positions relative to a self vehicle at each moment. The long-time airspace virtual driving scene information is information needed by different to-be-tested energy evaluation management strategy decisions.
Step 2: leading the evaluation peak map to be tested and the evaluation energy management strategy to be tested into a controller; carrying out real-time verification on the real-time performance of the energy management strategy to be evaluated and the computing capacity of the controller, and outputting a real-time evaluation result;
generating and burning the evaluation peak map to be tested and the evaluation energy management strategy to be tested to a controller through codes; the energy management strategy to be evaluated comprises an energy management strategy based on instantaneous optimization, rolling optimization, global optimization and learning, is not limited to the energy management strategy with optimization capability, and can be evaluated based on rules; the controller selects a real controller or a prototype controller operated for the verification code according to the vehicle type of the vehicle; the controller is used for receiving a vehicle state signal of the multi-degree-of-freedom driving simulator to generate a power source control signal, inputting the power source control signal into the vehicle hybrid transmission system, controlling the rotating speeds and the torques of different power sources (such as an engine, a driving motor and/or a generator) in the test bench, and also feeding back and receiving a working mode of the vehicle hybrid transmission system, a battery SoC change state signal and instantaneous fuel consumption; the to-be-evaluated peak map is a map of the relation between the vehicle required torque and the accelerator opening and the vehicle speed, and the power assembly output torque provides power to meet the vehicle required torque.
Further, in the operation process of the driver, the real-time performance of the energy management strategy to be evaluated and the computing capability of the controller are verified in real time, and the real-time performance evaluation result is output, and the specific steps are as follows: the method comprises the steps that a controller receives information required by an energy management strategy decision to be evaluated and sends a control command (a power source control signal), when the time difference between the time for receiving the information (a driving operation control signal and a vehicle state signal) and the time for sending the control command by the controller is smaller than a preset time threshold, the energy management strategy to be evaluated meets the real-time requirement, preferably, the preset time threshold is within 3-5 signal sampling intervals, and the signal sampling intervals are set to be 10ms; if the energy management strategy to be evaluated meets the real-time requirement, outputting a practical evaluation result of the maximum value, the minimum value and the average value of the time delay between the driving operation control signal and the vehicle state signal; if the energy management strategy to be evaluated does not meet the real-time requirement, outputting an evaluation result that the computing capacity of the controller does not meet the real-time performance of the energy management strategy to be evaluated.
Step 3: the driver operates the multi-degree-of-freedom driving simulator according to the long-time airspace virtual driving scene to obtain driving operation control signals, vehicle state signals and vehicle state expression; obtaining a vehicle demand torque based on the evaluation peak map to be tested, the driving operation control signal and the vehicle state information; an operating mode of the vehicle hybrid powertrain is determined based on the vehicle demand torque, the driving operation control signal, and the vehicle status signal.
The driver operates an accelerator pedal, a brake pedal and a steering wheel of the multi-degree-of-freedom driving simulator according to the visual scene image of the long-time airspace virtual driving scene generated in the step 1 to generate driving operation control signals, wherein the driving operation control signals comprise information such as accelerator pedal opening signals, brake pedal opening signals and steering wheel angles, and therefore a vehicle dynamics model constructed in virtual scene simulation software forms dynamic interaction with the long-time airspace virtual driving scene, and driving safety and collision prevention of a vehicle in the virtual scene simulation software are guaranteed; the multi-degree-of-freedom driving simulator records driving operation control signals according to time sequence; the multi-degree-of-freedom driving simulator transmits driving operation control signals to virtual scene simulation software, the virtual scene simulation software performs dynamic control on a vehicle dynamics model based on the driving operation control signals, visual animation (visual animation including information such as vehicle position, running state and traffic information) and vehicle dynamics signals (such as vehicle yaw angle, pitch angle and roll angle characteristic signals) are generated in the virtual scene simulation software, and therefore vehicle movement and the expression state of a long-time airspace virtual driving scene in the virtual scene simulation software are displayed in a visual animation mode, and a driver adjusts driving operation by observing the visual animation; meanwhile, the virtual scene simulation software feeds back vehicle dynamics signals (such as characteristic signals of a vehicle yaw angle, a pitch angle and a roll angle) generated by driving operation to the multi-degree-of-freedom driving simulator, the multi-degree-of-freedom driving simulator simulates the real motion gesture of the vehicle according to the vehicle dynamics signals, and a driver feels the real motion gesture of the vehicle through the multi-degree-of-freedom driving simulator to form a closed-loop control flow of the driving operation; the multi-degree-of-freedom driving simulator generates a vehicle state signal based on the vehicle dynamics signal, and inputs the vehicle state signal to the controller; the controller analyzes the vehicle state signals, generates a power source control signal according to an energy management strategy to be evaluated to control the vehicle hybrid transmission system, and simultaneously feeds back the vehicle speed and the vehicle acceleration signals to the multi-degree-of-freedom driving simulator and stores the vehicle speed change state; the vehicle hybrid transmission system controls the rotating speeds and the torques of different power sources according to the power source control signals, and outputs the vehicle state expression of the hybrid transmission system at the current moment in the test rack (the vehicle state expression at the current moment comprises the working modes of the vehicle hybrid transmission system, the rotating speeds and the torques of different power sources in different working modes, the change state of a battery SoC and the instantaneous fuel consumption), records the state expression at the current moment, and feeds back the state expression to the controller to be used as the decision and judgment condition of the energy management strategy of the vehicle hybrid transmission system at the next moment.
Wherein the vehicle status signal includes a vehicle speed signal, a vehicle acceleration signal, and the like.
The vehicle demand torque is obtained based on the to-be-evaluated peak map, the vehicle speed signal and the accelerator pedal opening signal in the step 2, and is used for determining the vehicle demand torque of the driving working mode in the to-be-evaluated energy management strategy, and the specific steps are as follows: the method comprises the steps of obtaining vehicle required torque corresponding to a vehicle speed signal and an accelerator pedal opening signal through table lookup in a pedal map, and inputting the vehicle required torque into an energy evaluation management strategy to be tested;
and (3) obtaining the vehicle sliding energy demand torque based on the to-be-evaluated peak map to be tested and the brake pedal opening degree signal in the step (2), wherein the vehicle sliding energy demand torque is used for determining the vehicle demand torque of a brake energy recovery working mode in an energy management strategy to be tested, and the specific steps are as follows: judging whether the vehicle is in a sliding energy recovery stage or a braking deceleration stage based on a brake pedal opening signal, wherein the vehicle is in the sliding energy recovery stage when the brake pedal opening is smaller than or equal to a brake pedal opening threshold value, and the vehicle is in the braking deceleration stage when the brake pedal opening is larger than the brake pedal opening threshold value, and the brake pedal opening threshold value is preferably 10%; if the vehicle is in the sliding energy recovery stage, obtaining the vehicle sliding energy demand torque of the vehicle in the sliding energy recovery stage (sliding process) corresponding to the opening signal of the brake pedal by looking up a table in the evaluation peak map to be tested, and inputting the vehicle sliding energy demand torque information into an energy management strategy to be evaluated; the main purpose of the braking deceleration stage is to ensure driving safety, and the energy management evaluation in the stage is not considered.
Further, driving operation control signals, vehicle demand torque signals and vehicle state signals are transmitted to an energy management strategy to be evaluated in a controller, the working mode of the vehicle hybrid transmission system is determined, and meanwhile, the vehicle hybrid transmission system records the rotating speed and the torque of a corresponding power source of each working mode in a steady state process and a switching process; the determination of the working mode depends on the energy management strategies to be evaluated, and the working modes generated by different energy management strategies under the same driving scene are different.
The working modes of the vehicle hybrid transmission system determined based on the energy management strategy to be evaluated in the step 2 comprise a pure electric working mode, an engine direct-drive working mode, a series range-extending working mode and/or a parallel hybrid working mode, and the rotating speeds and the torques of corresponding power sources of the vehicle hybrid transmission system in the steady-state processes of different working modes and in the switching processes of the different working modes are recorded; wherein, for the working mode that only a single power source operates, the vehicle demand torque is completely provided by the single power source; for the parallel hybrid working mode, calculating torque distribution and rotation speed distribution of an engine, a driving motor and/or a generator according to the analyzed vehicle required torque and the energy evaluation management strategy to be tested; and for the working mode of the series range extension, determining the battery charge and discharge state of the series range extension mode according to the analyzed vehicle required torque and the battery SoC state.
Step 4: analyzing driving intention changing moments according to driving operation control signals, and dividing pedal operation continuous fragments of an accelerator pedal and a brake pedal according to the driving intention changing moments; adding pedal operation fragment labels to the pedal operation continuous fragments; recording to-be-evaluated state expression data sets in pedal operation continuous fragments of an accelerator pedal and a brake pedal in sequence according to a time sequence order, and obtaining to-be-evaluated state expression data set sets of an to-be-evaluated energy management strategy;
wherein, the range of the values of the accelerator pedal signal and the brake pedal signal of the driving operation control signal is 0-100 percent.
The driving intention change means that the driving operation behavior of the pedal by the driver is obviously changed, the peak value and the valley value of the opening degree of the pedal are determined based on the pedal operation control signal, and the determined time corresponding to the peak value and the valley value is the driving intention change time. The method comprises the steps of judging whether the pedal operation is performed at the same time, wherein the accelerator pedal operation and the brake pedal operation do not occur simultaneously at the same time, taking a peak value and a valley value of the pedal operation process as driving intention changing time, and taking continuous fragments among different driving intention changing time as pedal operation continuous fragments, classifying corresponding driving fragments by judging driving characteristics of the pedal operation continuous fragments, and labeling, wherein the classes of labels comprise a starting acceleration fragment, a speed maintaining fragment, a steady acceleration fragment, a sudden acceleration fragment, a sliding deceleration fragment and a brake energy recovery fragment.
The specific steps of determining the peak value and the valley value of the pedal opening are as follows: firstly, a plurality of accelerator pedal segments and brake pedal segments are distinguished according to a driving operation control signal in time sequence, and the specific steps are as follows: sequentially judging pedal opening values in the driving operation control signals according to time sequences, wherein when the positive state of the pedal opening values does not change, the corresponding driving operation control signals belong to the same pedal segment, and when the positive state of the pedal opening values changes, the changed driving operation control signals belong to a new pedal segment; the pedal segment is an accelerator pedal segment if accelerator pedal opening values of driving operation control signals corresponding to a plurality of consecutive times are positive in time sequence, and is a brake pedal segment if brake pedal opening values of driving operation control signals are positive in time sequence; secondly, arranging pedal opening values in each pedal segment according to time sequence, and extracting peak values and valley values of corresponding pedal opening values in each pedal segment; the corresponding moments of all the determined peaks and valleys are driving intention changing moments; the pedal operation duration segments of the accelerator pedal and the brake pedal are divided by the driving intention change timing.
Further, analyzing peaks and valleys of all accelerator pedal openings, classifying pedal operation continuous fragments corresponding to driving characteristics of the accelerator pedal operation continuous fragments by judging and adding an accelerator pedal operation continuous fragment label, wherein the accelerator pedal operation fragment label comprises a starting accelerator fragment, a speed maintaining fragment, a steady accelerator fragment, a rapid accelerator fragment and a sliding speed reducing fragment, and the starting accelerator fragment is a starting accelerator fragment when starting from a vehicle, and the duration time when the accelerator pedal is continuously stepped from 0 accelerator pedal opening to the recorded first peak point is the starting accelerator fragment; the speed maintaining segment is used for maintaining the speed when the absolute value of the difference value between the adjacent peaks and valleys of the pedal opening is smaller than a difference threshold value and the speed change of the vehicle is smaller than a preset speed; the steady acceleration segment is the steady acceleration segment when the difference value between the adjacent peak value and the valley value of the pedal opening is larger than a difference value threshold value and the duration time is larger than the preset time; the rapid acceleration segment is the rapid acceleration segment when the difference value between the adjacent peak value and the valley value of the pedal opening is larger than a difference value threshold value and the duration time is smaller than the preset time; the coasting deceleration segment is a pedal operation continuation segment when the accelerator pedal opening dip is reduced to 0 when the driver releases the accelerator pedal.
Further, the peak-to-valley value of the brake pedal opening is analyzed, and the brake pedal operation duration fragments are classified and labeled by judging the pedal operation duration fragments corresponding to the driving characteristics of the brake pedal operation duration fragments, wherein the brake pedal operation duration fragments comprise a brake energy recovery fragment and a brake deceleration fragment. The braking energy recovery segment is a segment of which the peak value and the valley value of the opening degree of the brake pedal are smaller than a difference threshold value; the braking deceleration segment is a segment with a brake pedal opening peak value larger than a difference threshold value, so that the energy management is not involved in the pedal operation link for deceleration avoidance or deceleration parking.
Preferably, the difference threshold is 10; the preset speed is 5km/h; the preset time is 2s.
Further, the pedal operation continuous fragments of the accelerator pedal and the brake pedal are recorded sequentially according to the time sequence to obtain a to-be-evaluated state expression data set of the to-be-evaluated energy management strategyLExpression ofThe method comprises the following steps:L={L i |i=1,2,3,…,nand } wherein,L i is the firstiThe evaluation state to be tested of the evaluation energy management strategy in the pedal continuous segment of the time period represents a data group,na total number of moments for the pedal operation duration segment data set; to-be-evaluated state expression data set of to-be-evaluated energy management strategy LCorresponding time setT L The method comprises the following steps:T L ={T Li |i=1,2,3,…,n-a }; first, theiTo-be-evaluated state expression data set of to-be-evaluated energy management strategy in pedal continuous segment of time periodL i Includes the firstiVehicle state representation of a vehicle hybrid transmission system in pedal duration segments of a time period, including a working mode, rotational speeds and torques of different power sources in different working modes, battery SoC change states, instantaneous fuel consumption and pedal operation duration segment labels, the vehicle state representation of the vehicle hybrid transmission system to be evaluated forms a set of to-be-evaluated state representation data sets of an to-be-evaluated energy management strategyL。
Step 5: acquiring the optimal energy consumption state expression of the hybrid transmission system of the vehicle according to the global optimal energy management strategy; dividing pedal operation continuous segments corresponding to the optimal energy consumption state expression of the hybrid transmission system of the vehicle by using the pedal continuous segment labels in the step 4; sequentially recording a global optimal energy management strategy data set in a global optimal pedal operation continuous segment according to a time sequence order, and obtaining an optimal energy consumption state expression data set of the global optimal energy management strategy;
the output speed of the vehicle hybrid transmission system is equivalent to the speed feedback of the multi-degree-of-freedom driving simulator in the test bench, and after the vehicle completes the long-time domain virtual driving scene task, the vehicle speed information of the vehicle speed changing along with the time sequence, namely the driving global working condition, is recorded; calculating the optimal state expression of the hybrid transmission system of the vehicle by utilizing a global optimal energy management strategy based on the obtained driving global working condition, wherein the optimal state expression of the hybrid transmission system of the vehicle comprises the optimal states of the working modes of the hybrid transmission system of the vehicle, the rotating speeds and the torques of different power sources under different working modes, the SoC change state of a battery and instantaneous fuel consumption; and obtaining the global optimal state representation of the global optimal energy management strategy by the optimal state representation of the hybrid transmission system of the vehicle, and comparing the global optimal state representation of the global optimal energy management strategy with the state representation of the corresponding time sequence of the energy management strategy to be evaluated.
The global optimal energy management strategy adopts a dynamic programming method, and the dynamic programming method determines the optimal state performance of the hybrid transmission system of the vehicle through reverse traversal and forward searching of the driving global working condition information. Because the speed output of the hybrid transmission system in the test bench is consistent with that of the multi-degree-of-freedom driving simulator, the controller acquires and stores driving global working condition information, takes the driving global working condition information as the input of a global optimization energy management strategy, and further calculates the optimal state performance of the hybrid transmission system of the vehicle.
Further, since the global optimal state representation of the global optimal energy management strategy is consistent with the vehicle demand torque corresponding to the to-be-evaluated state representation of the to-be-evaluated energy management strategy at each moment, only the energy consumption states are inconsistent. Therefore, the evaluation state to be evaluated according to the energy management policy to be evaluated of step 4 represents the data setLTime sequence division is carried out on the optimal energy consumption state expression of the vehicle hybrid transmission system of the global optimal energy management strategy according to the division moments of the global optimal energy management strategy, and the optimal energy consumption state expression data set of the global optimal energy management strategy is obtained by sequentially recording in the continuous segment of the global optimal pedal operation according to the time sequence GThe expression is:G={G i |i=1,2,3,…,nand } wherein,G i is the firstiAn optimal energy consumption state representation data set within the pedal duration segment of the time period; optimal energy consumption state representation data set of global optimal energy management strategyGCorresponding time setT G The method comprises the following steps:T G ={T Gi |i=1,2,3,…,n-a }; first, theiTime period global optimal energy management policy data setG i Includes the firstiWorking mode of vehicle hybrid transmission system in pedal continuous segment of time period and different power sources in different working modesOptimal state of rotation speed and torque, battery SoC change state and instantaneous fuel consumption, and pedal operation sustained segment tags for optimal state, optimal state representation of a hybrid powertrain system of a vehicle forms an optimal energy consumption state representation dataset set of a global optimal energy management strategyG。
It can be understood that the number and duration of pedal operation segments corresponding to the to-be-evaluated state performance of the to-be-evaluated energy management strategy and the global optimal state performance of the global optimal energy management strategy are consistent, namely:T L1 =T G1 ,T L2 =T G2 ,T L3 =T G3 ,…,T Ln =T Gn therefore, the evaluation state performance to be evaluated of each pedal operation section is compared and evaluated with the global optimum state performance.
Step 6: performing comparative evaluation of the state performance of the time sequence pedal operation segment;
to-be-evaluated state expression data set with time sequence arrangement to-be-evaluated energy management strategy according to step 4 LAnd step 5, an optimal energy consumption state expression data set of the global optimal energy management strategyGTo-be-evaluated state performance data set of to-be-evaluated energy management strategyLAnd an optimal energy consumption state representation dataset of a global optimal energy management strategyGAnd comparing the to-be-evaluated state performance of each pedal operation segment with the optimal energy consumption state performance.
Further, comparing and evaluating the working modes of the hybrid transmission system of the vehicle, and extracting pedal operation fragments adopting the same working mode and pedal operation fragments adopting different working modes for comparison;
and analyzing the similarity degree of the rotation speed and the torque distribution of the power source under the pedal operation fragments of the same working mode by using the information divergence, wherein the expression is as follows:
wherein,,S i (n L ‖n G ) Is the firstiThe pedal of the time period continues the degree of similarity of the rotational speed distribution in the segment,S i (T L ‖T G ) Is the firstiThe degree of similarity of torque distribution in the pedal duration segment at the moment;tis shown in the firstiStarting time and ending time in the pedal operation section of the time period;n L (t) Is the firstiThe power source rotating speed distribution corresponding to the energy management strategy to be evaluated in the pedal operation section of the time period at different moments in the pedal continuous section of the time period,T L (t) Is the first iThe power source torque distribution of different moments in the pedal continuous segment of the time period, which corresponds to the energy management strategy to be evaluated in the pedal operation segment of the time period;n G (t) Is the firstiThe global energy management strategy in the pedal operation segment of the time period corresponds to the power source rotating speed distribution at different moments in the pedal continuous segment,T G (t) Is the firstiThe power source torque distribution at different moments in the pedal continuous segment corresponding to the global energy management strategy in the pedal operation segment of the time period;tis shown in the firstiThe starting time and the ending time within the pedal operation segment of the time period.
If the distribution and the value are identical, the information divergence is 0, and when the two distributions are dissimilar, the corresponding information divergence will increase.
Finally, extracting pedal operation fragments with information divergence larger than a divergence threshold value, and carrying out correlation analysis on influence characteristics of the pedal operation fragments by using a correlation coefficient analysis method, wherein the expression is as follows:
wherein,,R xy representation ofThe variable correlation coefficient, preferably, the value interval of the variable correlation coefficient is between 1 and-1, 1 represents the complete linear correlation of the variables, and-1 represents the uncorrelation among the variables;S xy the covariance of the samples is represented and,S x representing energy consumption performance characteristicsxIs set to be a standard deviation of a sample of (c), S y Representing driver and driving scene impact featuresyIs set to be a standard deviation of a sample of (c),X i is the firstiThe energy consumption within the pedal operating segment of the time period is characterized,Xas an average of the energy consumption performance of the pedal operation segment,Y i is the firstiThe driver and driving scene corresponding to the pedal operation segment of the time period influence the characteristics,Ythe average value of the influence characteristics of the driver and the driving scene corresponding to the pedal operation segment; energy consumption performance characteristicsxIncluding instantaneous fuel consumption values; influencing characteristicsyThe method comprises influencing characteristics of drivers and driving scenes, such as the characteristics of labels of pedal operation fragments, corresponding driver operation behaviors, driving scene information and the like.
Preferably, the divergence threshold is 0.5.
And performing similarity analysis and characteristic correlation analysis on pedal operation fragments adopting different working modes:
the method comprises the steps of analyzing the change state of a battery SoC and instantaneous fuel consumption under pedal operation fragments in different working modes, calculating the energy consumption performance similarity, and measuring the energy consumption performance difference of different pedal operation fragments according to the energy consumption performance similarity method, wherein the specific method comprises the following steps:
calculating the instantaneous fuel consumption and the battery SoC state at each moment under different pedal operation fragments, and equivalent the battery SoC state to an equivalent instantaneous fuel consumption value m f (i,t) The expression is:
m f (i,t)=m f0 (i,t)+βabs（△SoC）；
wherein,,m f0 (i,t) Is the firstiInstantaneous fuel consumption value, deltaS, of a time periodThe oC is the equivalent conversion of the battery SoC state to fuel consumption,βand (5) equivalently converting the state of the battery SoC into a fitting factor of the fuel consumption.
Calculate the firstiThe energy consumption difference value of the pedal operation segment in the time period is expressed as follows:
wherein,,D i is the first of the time sequence pedal operation fragmentsiThe energy consumption of the pedal operation segment of the time period represents a difference value,F t L to evaluate the energy management strategy to be tested in the first placeiIn pedal operation section of time periodtThe total fuel consumption value of the moment comprises actual fuel consumption and battery SoC equivalent fuel consumption;F t G on the first hand, for globally optimal energy management strategyiIn the time pedal operating segmenttThe total fuel consumption value at the moment in time,t 1 is the firstiThe moment of start of the pedal operation segment,t end is the firstiThe termination time of the pedal operation section of the period.
Finally, clustering and analyzing the similarity of the energy consumption difference of each pedal operation segment by using a K-means method to obtain a category K1 with high energy consumption performance similarity and a category K2 with low energy consumption performance similarity, and further evaluating the clustering effect by using a contour coefficient, wherein the expression is as follows:
wherein,,Cas the profile factor is used,afor the average distance between the energy consumption difference value and other samples of the class K1 with high similarity of energy consumption, bFor the average distance of all samples of class K2 where the energy consumption difference value and the energy consumption show low similarity,Cthe larger the value is, the better the clustering effect is, and the dividing effect of the energy consumption expression similarity category can be effectively embodied.
Further, pedal operation feature distribution under different energy consumption performance categories is extracted for analysis, such as analysis of categories with high energy consumption performance similarity (pedal operation fragments with small energy consumption difference), and correlation analysis is performed on influence features of the pedal operation fragments by using a correlation coefficient analysis method, wherein driver and driving scene influence features are such as tags of the pedal operation fragments, corresponding driver operation behaviors, driving scene information and the like.
Step 7: outputting an evaluation result of the energy management strategy to be evaluated;
outputting whether the controller can meet the real-time requirement of the energy management strategy to be evaluated or not according to the real-time evaluation result of the step 2, and outputting the maximum value, the minimum value and the average value of the time delay between the driving operation control signal and the vehicle state signal on the premise of meeting the real-time requirement; and (3) carrying out similarity analysis on pedal operation fragments adopting the same working mode and adopting different working modes according to the judgment result of the step (6), outputting a result of carrying out correlation analysis on corresponding characteristics of the pedal operation fragments (such as correlation coefficients of different characteristics, sequencing of the correlation coefficients and the like), outputting corresponding driver and driving scene influence characteristics, optimizing the operation behaviors of the driver on the pedal operation fragments with the same label, or providing basis for developing working condition sensing functions of predicting driving scene information in advance.
In addition, in order to ensure that the set peer map and the energy management strategy have universality and generalization, the peer map and the energy management strategy to be evaluated can be adjusted according to the evaluation result, the evaluation steps 1-6 are repeated, and when the low similarity segment ratio in the step 6 is less than 10%, the state expression of the peer map and the energy management strategy to be evaluated can be considered to be close to the optimal state expression of the global full-scale energy management strategy.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (4)
1. The method for evaluating the energy management strategy of the hybrid electric vehicle with the driver in the ring uses an energy management strategy evaluation system of the hybrid electric vehicle with the driver in the ring, and comprises virtual scene simulation software, a multi-degree-of-freedom driving simulator, a controller, a vehicle hybrid transmission system and a test bench; the virtual scene simulation software is used for constructing a long-time airspace virtual driving scene and a vehicle dynamics model and transmitting vehicle state signals; the multi-degree-of-freedom driving simulator is respectively connected with the virtual scene simulation software and the controller and is used for inputting driving operation control signals and receiving vehicle dynamics signal feedback in the virtual scene simulation software and speed signal feedback in the controller; the multi-degree-of-freedom driving simulator comprises an accelerator pedal, a brake pedal and a steering wheel; the controller is also connected with the vehicle hybrid transmission system and is used for receiving driving operation control signals and/or vehicle state signals in real time, namely, information required by the decision of the energy management strategy to be evaluated, and controlling the vehicle hybrid transmission system by analyzing the energy management strategy to be evaluated and deciding to generate a control command; the vehicle hybrid transmission system is arranged in the test bench, is used for executing the input of a control command in the controller and feeding back the vehicle state performance of the current period into the controller, and is characterized by comprising the following specific steps:
Step 1, constructing a long-time airspace virtual driving scene and a vehicle dynamics model in virtual scene simulation software;
step 2: leading the evaluation peak map to be tested and the evaluation energy management strategy to be tested into a controller; carrying out real-time verification on the real-time performance of the energy management strategy to be evaluated and the computing capacity of the controller, and outputting a real-time evaluation result;
step 3: operating the multi-degree-of-freedom driving simulator according to the long-time airspace virtual driving scene to obtain driving operation control signals, vehicle state signals and vehicle state expression; obtaining a vehicle demand torque based on the evaluation peak map to be tested, the driving operation control signal and the vehicle state signal; determining an operating mode of the vehicle hybrid powertrain based on the vehicle demand torque, the driving operation control signal, and the vehicle status signal;
step 4: analyzing driving intention changing moments according to driving operation control signals, and dividing pedal operation continuous fragments of an accelerator pedal and a brake pedal according to the driving intention changing moments; adding a pedal operation duration fragment tag to the pedal operation duration fragment; recording to-be-evaluated state expression data sets in pedal operation continuous fragments of an accelerator pedal and a brake pedal in sequence according to a time sequence order, and obtaining to-be-evaluated state expression data set sets of an to-be-evaluated energy management strategy;
Step 5: acquiring the optimal energy consumption state expression of the hybrid transmission system of the vehicle according to the global optimal energy management strategy; dividing pedal operation continuous fragments represented by the optimal energy consumption state of the hybrid transmission system of the vehicle by using the pedal operation continuous fragment labels in the step 4; sequentially recording a global optimal energy management strategy data set in a global optimal pedal operation continuous segment according to a time sequence order, and obtaining an optimal energy consumption state expression data set of the global optimal energy management strategy;
step 6: according to the to-be-evaluated state expression data set of the to-be-evaluated energy management strategy with time sequence arrangement in the step 4 and the optimal energy consumption state expression data set of the global optimal energy management strategy in the step 5, performing comparison evaluation of time sequence pedal operation fragment state expression to obtain a state expression evaluation result;
step 7: and outputting the real-time evaluation result of the step 2 and the state performance evaluation result of the step 6 as evaluation results of the energy management strategy to be evaluated.
2. The hybrid vehicle energy management strategy evaluation method of claim 1, wherein the energy management strategy to be evaluated is stored in the controller.
3. The hybrid vehicle energy management strategy evaluation method according to claim 1, wherein the to-be-evaluated state expression data set includes an operation mode of a vehicle hybrid transmission system of to-be-evaluated states in a pedal duration segment, rotation speeds and torques of different power sources in different operation modes, a battery SoC variation state, instantaneous fuel consumption, and pedal operation duration segment labels; the optimal energy consumption state expression data set comprises an operating mode of a vehicle hybrid transmission system with the optimal energy consumption state in the pedal continuous segment, rotating speeds and torques of different power sources in different operating modes, a battery SoC change state, an optimal state of instantaneous fuel consumption and a pedal operation continuous segment label.
4. The hybrid vehicle energy management strategy evaluation method according to claim 3, wherein the comparison evaluation of the state expressions of the time-series pedal operation fragments includes the comparison evaluation of the pedal operation fragments in the same operation mode and the pedal operation fragments in different operation modes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310133131.6A CN115876494B (en) | 2023-02-20 | 2023-02-20 | Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310133131.6A CN115876494B (en) | 2023-02-20 | 2023-02-20 | Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115876494A CN115876494A (en) | 2023-03-31 |
| CN115876494B true CN115876494B (en) | 2023-05-05 |
Family
ID=85761310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310133131.6A Active CN115876494B (en) | 2023-02-20 | 2023-02-20 | Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115876494B (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017023898A1 (en) * | 2015-08-03 | 2017-02-09 | Cummins, Inc. | Systems and methods of energy management and control of an electrified powertrain |
| CN105416086B (en) * | 2015-12-29 | 2017-11-21 | 北京理工大学 | Plug-in hybrid-power automobile energy management strategies hardware-in-loop simulation platform |
| CN108803377A (en) * | 2018-07-19 | 2018-11-13 | 重庆大学 | A kind of plug-in hybrid-power automobile energy management experiment porch |
| CN110780605A (en) * | 2019-10-29 | 2020-02-11 | 同济大学 | Hardware-in-loop simulation test platform for closed loop system of hybrid electric vehicle |
| CN111923897B (en) * | 2020-09-29 | 2021-03-23 | 北京航空航天大学 | Intelligent energy management method for plug-in hybrid electric vehicle |
| CN114475566B (en) * | 2022-03-01 | 2024-01-30 | 重庆科技学院 | Intelligent network allies oneself with inserts electric hybrid vehicle energy management real-time control strategy |
| CN115472013B (en) * | 2022-09-08 | 2024-05-17 | 江苏大学 | New energy automobile energy management test platform based on virtual traffic scene |
-
2023
- 2023-02-20 CN CN202310133131.6A patent/CN115876494B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN115876494A (en) | 2023-03-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guo et al. | Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle | |
| US20200031370A1 (en) | Driver Behavior Based Propulsion Control Strategy Using Artificial Intelligence | |
| CN110936949B (en) | Energy control method, equipment, storage medium and device based on driving condition | |
| CN107628029B (en) | A kind of energy-saving stability motion control method of net connection automobile queue | |
| Luo et al. | Green light optimal speed advisory for hybrid electric vehicles | |
| Tang et al. | Visual detection and deep reinforcement learning-based car following and energy management for hybrid electric vehicles | |
| US20090150016A1 (en) | Vehicle hybrid power system and method for creating simulated equivalent fuel consumption multidimensional data applicable thereto | |
| CN111775925B (en) | Working mode decision method and device for power split hybrid electric vehicle | |
| CN104748981A (en) | New energy vehicle testing system based on driver-vehicle-road closed-loop control | |
| CN111959492B (en) | HEV energy management hierarchical control method considering lane change behavior in internet environment | |
| CN111459995B (en) | Multi-mode vehicle speed prediction method based on driving data | |
| CN116946107B (en) | Hybrid system mode decision and power distribution method under energy track following | |
| CN112849119A (en) | Multivariable torque optimizing control distribution method for engine and motor of hybrid electric vehicle | |
| CN108556846A (en) | The control method and control system of energy regenerating are slided for carrying out vehicle braking | |
| CN115876494B (en) | Energy management strategy evaluation system and method for hybrid electric vehicle with drivers in-loop | |
| CN115071712A (en) | Intelligent control method for energy recovery intensity of new energy automobile under sliding working condition | |
| Jeong et al. | Online implementation of optimal control with receding horizon for eco-driving of an electric vehicle | |
| CN111891109B (en) | Hybrid electric vehicle energy optimal distribution control method based on non-cooperative game theory | |
| Mohebbi et al. | Adaptive neuro control of parallel hybrid electric vehicles | |
| CN113781770A (en) | Double-clutch simulation type cooperative control system for vehicle roads in confluence area | |
| Zhang et al. | Control and optimisation of a dual-motor coupling drive system of pure electric vehicle based on multi-island genetic algorithm | |
| Pan et al. | Energy-optimized adaptive cruise control strategy design at intersection for electric vehicles based on speed planning | |
| Wickramasinghe et al. | Intelligent driver system for improving fuel efficiency in vehicle fleets | |
| Chen et al. | The vehicle speed strategy with double traffic lights based on reinforcement learning | |
| Cheng et al. | Deep Learning Approach for Driver Speed Intention Recognition Based on Naturalistic Driving Data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |