CN116502339A - Parameter optimization method for driving system of hybrid electric vehicle - Google Patents
Parameter optimization method for driving system of hybrid electric vehicle Download PDFInfo
- Publication number
- CN116502339A CN116502339A CN202310677179.3A CN202310677179A CN116502339A CN 116502339 A CN116502339 A CN 116502339A CN 202310677179 A CN202310677179 A CN 202310677179A CN 116502339 A CN116502339 A CN 116502339A
- Authority
- CN
- China
- Prior art keywords
- parameters
- mode
- sensitive
- hybrid
- sensitive parameters
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000011217 control strategy Methods 0.000 claims abstract description 18
- 238000004088 simulation Methods 0.000 claims abstract description 11
- 238000005265 energy consumption Methods 0.000 claims abstract description 8
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 239000002245 particle Substances 0.000 claims abstract description 6
- 230000001133 acceleration Effects 0.000 claims abstract description 4
- 230000009194 climbing Effects 0.000 claims abstract description 4
- 238000010206 sensitivity analysis Methods 0.000 claims abstract description 4
- 239000003638 chemical reducing agent Substances 0.000 claims description 11
- 238000011084 recovery Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The parameter optimization method of the electric drive system of the hybrid electric vehicle comprises the following steps: s1: establishing a whole vehicle physical model of the hybrid electric vehicle; s2: establishing a control strategy of the whole hybrid electric vehicle, wherein the control strategy comprises a motion mode and a switching strategy; s3: carrying out joint simulation on the whole vehicle physical model established in the step S1 and the control strategy established in the step S2 to obtain a joint simulation model; s4: determining optimization indexes of hundred-meter energy consumption, hundred-meter acceleration time, highest speed, highest climbing gradient and NEDC (net DC) circulation working condition energy consumption of an automobile, and determining sensitive parameters by a sensitivity analysis method, wherein the sensitive parameters comprise mechanical parameters and control parameters; s5: optimizing in the global range of the sensitive parameters by using a particle swarm optimization algorithm to obtain the optimized matching parameters of each sensitive parameter, so as to form an optimal solution set of the sensitive parameters; s6: and determining the parameters with the most proper comprehensive performance in the optimal solution set of the sensitive parameters through expert judgment.
Description
Technical Field
The invention relates to the field of hybrid electric vehicles, in particular to a parameter optimization method for a driving system of a hybrid electric vehicle.
Background
The driving system parameter matching has great influence on the dynamic property and economical efficiency of the hybrid electric vehicle. The proper parameter design can greatly improve the energy utilization efficiency of the vehicle, reduce the failure rate and improve the comprehensive performance and service life of the vehicle.
Currently, commercial software for dynamic and economic calculations on vehicles is typically applied to achieve dynamic and economic design goals by selecting appropriate parameters through iterative manual iterative calculations using conventional methods. However, such methods require significant time and labor costs, and the dynamic and economic goals of the design are difficult to achieve overall optimization; meanwhile, as the power sources of the hybrid electric vehicle are increased, the control strategy is more complex, the comprehensive performance requirement of the whole vehicle is higher, the iteration of the whole vehicle product is faster and faster, the parameter optimization matching is very difficult, and the conventional design optimization matching method cannot meet the requirement.
How to solve the problems, a method for optimizing parameters of a driving system of a hybrid electric vehicle is needed, which realizes optimization of multiple target parameters and rapidly completes optimization of comprehensive performance of dynamic performance and economy.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method for optimizing parameters of a driving system of a hybrid electric vehicle, which solves the problems of difficult optimization and matching of the parameters of the hybrid electric vehicle and high time and labor cost by simultaneously adjusting a plurality of sensitive parameters and selecting optimal design and optimization matching parameters to meet the expected dynamic performance and economy of the vehicle.
The invention is realized by adopting the following scheme: the parameter optimization method of the electric drive system of the hybrid electric vehicle comprises the following steps:
s1: establishing a whole vehicle physical model of the hybrid electric vehicle;
s2: establishing a control strategy of the whole hybrid electric vehicle, wherein the control strategy comprises a motion mode and a switching strategy;
s3: carrying out joint simulation on the whole vehicle physical model established in the step S1 and the control strategy established in the step S2 to obtain a joint simulation model;
s4: determining optimization indexes of hundred-meter energy consumption, hundred-meter acceleration time, highest speed, highest climbing gradient and NEDC (net DC) circulation working condition energy consumption of an automobile, and determining sensitive parameters by a sensitivity analysis method, wherein the sensitive parameters comprise mechanical parameters and control parameters;
s5: optimizing in the global range of the sensitive parameters by using a particle swarm optimization algorithm to obtain the optimized matching parameters of each sensitive parameter, so as to form an optimal solution set of the sensitive parameters;
s6: and determining the parameters with the most proper comprehensive performance in the optimal solution set of the sensitive parameters through expert judgment.
The whole vehicle physical model comprises a whole vehicle body, an engine, a clutch, a driving motor, a generator, a power battery, a main speed reducer, a differential mechanism, a braking system, an anti-skid system, a control console, a monitor, an engine characteristic curve and a motor characteristic curve.
The motion mode comprises a pure electric mode, a pure electric braking mode, a range-extending driving mode, an engine starting mode, an engine stopping mode, a range-extending Cheng Zhidong mode, an engine direct-drive mode, a hybrid driving mode, a hybrid braking mode, a parking charging mode, an engine direct-drive braking mode and a parallel hybrid mode.
The specific steps for obtaining the optimal solution set of the sensitive parameters in the step S5 are as follows:
s5-1, randomly initializing each sensitive parameter;
s5-2, introducing the sensitive parameters into a joint simulation model, calculating economy and dynamic property, and judging whether the sensitive parameters accord with optimization indexes;
and S5-3, returning to the current sensitive parameters if the optimization indexes are met, updating the sensitive parameters if the optimization indexes are not met, and returning to S5-2.
By adopting the technical scheme, the dynamic performance and the economical efficiency of the hybrid electric vehicle are calculated through the whole vehicle physical model and the control strategy, a plurality of performance targets are comprehensively considered by utilizing the particle swarm optimization algorithm, an optimal solution set of the sensitive parameters of the hybrid electric vehicle is obtained, the motor model is quickly selected, the optimal transmission ratio is selected, the dynamic performance and the economical efficiency of the hybrid electric vehicle are optimal, the problems that the parameter optimization and the matching efficiency of a driving system of the hybrid electric vehicle are low, and the multi-target parameter optimization and the matching are difficult are solved, and the targets of high multi-target parameter optimization and the matching efficiency and good comprehensive performance of the driving system of the hybrid electric vehicle are realized.
The invention is further described below with reference to the drawings and specific examples.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a physical model of the whole vehicle according to the present invention;
FIG. 3 is a schematic diagram of a control strategy according to the present invention;
FIG. 4 is a strategy diagram of the pure braking energy recovery mode of the present invention;
FIG. 5 is a schematic strategy diagram of the parallel hybrid mode of the present invention;
FIG. 6 is a schematic diagram of a differential rotation speed cancellation strategy according to the present invention;
FIG. 7 is a flow chart of a particle optimization algorithm of the present invention.
Detailed Description
Referring to fig. 1 to 7, a method for optimizing parameters of an electric drive system of a hybrid electric vehicle includes the steps of:
s1: establishing a whole vehicle physical model of the hybrid electric vehicle through software AVL Cruise; the whole vehicle physical model comprises a whole vehicle body, an engine, a clutch, a driving motor, a generator, a power battery, a low-power consumption component, a main speed reducer, a differential mechanism, a braking system, an anti-skid system, a control console, a monitor, an engine characteristic curve and a motor characteristic curve, and further comprises a first speed reducer, a second speed reducer, a constant module and a function module. The connection relation of all parts in the whole vehicle physical model is that a right front wheel of a whole vehicle body is mechanically connected with a right front brake, a right rear wheel of the whole vehicle body is mechanically connected with a right rear brake, a left front wheel of the whole vehicle body is mechanically connected with a left front brake, a left rear wheel of the whole vehicle body is mechanically connected with a left rear brake, an engine is mechanically connected with a clutch and a first speed reducer, the first speed reducer is mechanically connected with a generator, the clutch, the main speed reducer and a second speed reducer are mechanically connected with each other in pairs, the second speed reducer is mechanically connected with a driving motor, the main speed reducer is mechanically connected with a differential mechanism, the differential mechanism is respectively mechanically connected with the left rear brake and the right rear brake, the generator is connected with a power battery through a cable, the power battery is connected with a low-power consumption part through the cable, and the power battery is connected with a driving motor through the cable. In the whole vehicle physical model, each component transmits signals through a CAN bus.
S2: establishing a control strategy of the whole hybrid electric vehicle through software Matlab, wherein the control strategy comprises a motion mode and a switching strategy; the motion mode comprises a pure electric driving mode, a pure electric braking mode, a range-extending driving mode, a range-extending Cheng Zhidong mode, an engine starting mode, an engine direct-drive braking mode, an engine stopping mode, a hybrid electric driving mode, a hybrid electric braking mode, a parking charging mode, a driving charging mode, an idle charging mode, a parking power generation mode and a parallel hybrid mode. In the pure electric driving mode, all driving power of the vehicle comes from a power battery, and the power of the power battery is converted into mechanical energy through a driving motor and then is transmitted to each wheel end of the vehicle body through a power transmission system of the vehicle; the driving power in the driving charging mode is from an engine, the output power of the engine is converted into electric energy through a generator, one part of the electric energy is converted into mechanical energy through a driving motor, the mechanical energy is transmitted to each wheel end of a vehicle body through an automobile transmission system, and the other part of the electric energy is input into a power battery for storage; in the idle speed charging mode, the transmission system is disconnected from the wheel end, the vehicle is in a static state, the engine works to drive the generator to generate electricity, and electric energy is input into the power battery to be stored. As shown in fig. 3, the control strategy determines an optimal working mode according to the current running speed of the vehicle, the battery charge of the current power battery, the required power and torque of the vehicle and the intention of the driver, and outputs a control signal corresponding to the optimal working mode to the whole vehicle model. The control strategy can realize the functions of data type conversion, power calculation, gear calculation, switching of different motion modes of the hybrid power system, switching of a hybrid power system mode state machine, torque limitation and signal output.
In the pure electric braking mode, the power battery does not need to recover electric energy when the electric quantity is sufficient, and the recovery efficiency is lower when the vehicle speed is lower, so that the driving motor recovers energy according to the vehicle speed and the battery capacity, and redundant braking force is provided by a mechanical brake. In this embodiment, as shown in fig. 4, the pure electric braking energy recovery mode strategy is that the driving motor request torque TmCmdTorque is negative, the driving motor is in a power generation state, and in order to prevent the situation that the battery is overcharged or the energy of the power battery is too low to be timely replenished, the electric quantity SOC of the power battery is considered in the energy recovery strategy - Act, when the electric quantity SOC-Act is lower, the value of the coefficient a is close to 1, so that the electric quantity of the power battery is timely supplemented; when the electric quantity SOC-Act is higher, the value of the coefficient a is close to 0, the charging quantity is smaller, and the purpose of electric quantity supplement is achieved through the interpolation function T (u). When the vehicle speed is higher, the braking recovery effect is better, the vehicle speed is lower, the energy recovery effect is poor, and the target is realized through the interpolation function correction coefficient b; when the sum of the braking torque front wheel streaminbrktorq and the braking torque rear wheel rearreamincttq is greater than the torque provided by the drive motor, the excess braking torque is provided by the mechanical brake.
The range-extending mode is that the generator performs self-adaptive power generation according to the electric quantity condition of the power battery and the optimal torque of the engine, and the driving motor provides corresponding torque according to the driving requirement torque of the vehicle.
The control strategy in the parallel hybrid mode of this embodiment is shown in fig. 5, and when the required Torque DrvReqTorque is greater than the sum of the engine optimum Torque EngineOptTorq and the drive motor maximum Torque tm_max_torque, the drive motor is operated in the maximum drive Torque state, with the remaining Torque being provided by the engine. When the required Torque drvreqtorq is smaller than the sum of the engine optimal Torque EngineOptTorq and the driving motor maximum Torque tm_max_torq and the required Torque drvreqtorq is larger than the engine optimal Torque EngineOptTorq, the engine operates in the optimal Torque region and the remaining Torque is supplied by the driving motor. When the required torque drvreqtorq is smaller than the engine optimal torque EngineOptTorq, the engine is operated near the optimal torque region.
When the vehicle speed and the engine speed are not matched, the mode switching generates impact, so that a strategy of eliminating the speed difference is needed, in the embodiment, the generator drives the engine to eliminate the speed difference, the strategy is shown in fig. 6, the corresponding engine speed is calculated according to the vehicle speed, the calculated engine speed is compared with the actual engine speed engineering, and the speed of the engine is regulated through the generator GmCmdToque, so that the aim of eliminating the speed error is fulfilled.
S3: carrying out joint simulation on the physical model of the whole vehicle established in the step S1 and the control strategy established in the step S2 to obtain a joint simulation model which is used for calculating the dynamic property and the economical efficiency of the whole vehicle;
s4: determining optimization indexes of hundred-meter energy consumption, hundred-meter acceleration time, highest vehicle speed, highest climbing gradient and NEDC (net DC) circulation working condition energy consumption of the automobile; confirming sensitive parameters by a sensitivity analysis method, wherein the sensitive parameters comprise mechanical parameters and control parameters, the mechanical parameters comprise rated power and reduction ratio of a motor, and the control parameters comprise a low electric quantity switching threshold value, a high vehicle speed switching threshold value and a low vehicle speed switching threshold value; in the embodiment, the rated power of the motor can be selected from 50kW, 55 kW and 55 kW, and the reduction ratio is 8<i<14, low battery switching threshold 8%< SOC Low and low <14%, high power switching threshold 90%< SOC Low and low <98%, high vehicle speed switching threshold 60km/h< V High height <70km/h, low vehicle speed switching threshold 20km/h< V High height <30km/h。
S5: optimizing in the global range of the sensitive parameters by using a particle swarm optimization algorithm to obtain the optimized matching parameters of each sensitive parameter, so as to form an optimal solution set of the sensitive parameters; the specific steps for obtaining the optimal solution set of the sensitive parameters in the step S5 are as follows:
s5-1, randomly initializing each sensitive parameter;
s5-2, introducing the sensitive parameters into a joint simulation model, calculating economy and dynamic property, and judging whether the sensitive parameters accord with optimization indexes;
and S5-3, returning to the current sensitive parameters if the optimization indexes are met, updating the sensitive parameters if the optimization indexes are not met, and returning to S5-2.
S6: and determining the parameters with the most proper comprehensive performance in the parameter optimal solution set through expert judgment.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, and those skilled in the art will appreciate that the modifications made to the invention fall within the scope of the invention without departing from the spirit of the invention.
Claims (4)
1. The parameter optimization method for the electric drive system of the hybrid electric vehicle is characterized by comprising the following steps of:
s1: establishing a whole vehicle physical model of the hybrid electric vehicle;
s2: establishing a control strategy of the whole hybrid electric vehicle, wherein the control strategy comprises a motion mode and a switching strategy;
s3: carrying out joint simulation on the whole vehicle physical model established in the step S1 and the control strategy established in the step S2 to obtain a joint simulation model;
s4: determining optimization indexes of hundred-meter energy consumption, hundred-meter acceleration time, highest speed, highest climbing gradient and NEDC (net DC) circulation working condition energy consumption of an automobile, and determining sensitive parameters by a sensitivity analysis method, wherein the sensitive parameters comprise mechanical parameters and control parameters;
s5: optimizing in the global range of the sensitive parameters by using a particle swarm optimization algorithm to obtain the optimized matching parameters of each sensitive parameter, so as to form an optimal solution set of the sensitive parameters;
s6: and determining the parameters with the most proper comprehensive performance in the optimal solution set of the sensitive parameters through expert judgment.
2. The method for optimizing parameters of an electric drive system of a hybrid vehicle according to claim 1, characterized in that: the whole vehicle physical model comprises a whole vehicle body, an engine, a clutch, a driving motor, a generator, a power battery, a main speed reducer, a differential mechanism, a braking system, an anti-skid system, a control console, a monitor, an engine characteristic curve and a motor characteristic curve.
3. The method for optimizing parameters of an electric drive system of a hybrid vehicle according to claim 1, characterized in that: the motion mode comprises a pure electric mode, a pure electric braking mode, a range-extending driving mode, an engine starting mode, an engine stopping mode, a range-extending Cheng Zhidong mode, an engine direct-drive mode, a hybrid driving mode, a hybrid braking mode, a parking charging mode, an engine direct-drive braking mode and a parallel hybrid mode.
4. The method for optimizing parameters of an electric drive system of a hybrid electric vehicle according to claim 1, wherein the specific step of obtaining the optimal solution set of the sensitive parameters in S5 is:
s5-1, randomly initializing each sensitive parameter;
s5-2, introducing the sensitive parameters into a joint simulation model, calculating economy and dynamic property, and judging whether the sensitive parameters accord with optimization indexes;
and S5-3, returning to the current sensitive parameters if the optimization indexes are met, updating the sensitive parameters if the optimization indexes are not met, and returning to S5-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310677179.3A CN116502339A (en) | 2023-06-08 | 2023-06-08 | Parameter optimization method for driving system of hybrid electric vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310677179.3A CN116502339A (en) | 2023-06-08 | 2023-06-08 | Parameter optimization method for driving system of hybrid electric vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116502339A true CN116502339A (en) | 2023-07-28 |
Family
ID=87323239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310677179.3A Pending CN116502339A (en) | 2023-06-08 | 2023-06-08 | Parameter optimization method for driving system of hybrid electric vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116502339A (en) |
-
2023
- 2023-06-08 CN CN202310677179.3A patent/CN116502339A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110304044B (en) | ECMS-based PHEV four-wheel-drive torque distribution method | |
| CN110254416B (en) | System and method for energy management during a regenerative mode of a hybrid electric vehicle | |
| CN106080585B (en) | Double-planet-row type hybrid electric vehicle nonlinear model prediction control method | |
| da Silva et al. | Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle | |
| CN110341687B (en) | Torque distribution method and system for dual-motor range-extending driving hybrid vehicle | |
| Zulkifli et al. | Operation and control of split-parallel, through-the-road hybrid electric vehicle with in-wheel motors | |
| CN110667395A (en) | Multi-target optimization method for gear shifting rule of pure electric vehicle of two-gear automatic gearbox based on braking working condition | |
| CN103072461A (en) | Dual-motor multi-mode hybrid power driving system and control method thereof | |
| CN104760591B (en) | Hybrid power complex control system | |
| Zhang et al. | Powertrain design and energy management of a novel coaxial series-parallel plug-in hybrid electric vehicle | |
| CN109240125B (en) | Method for calculating two-shaft required torque of gearbox of hybrid vehicle | |
| CN112026531A (en) | Driving torque distribution method and system for front-rear axle double-motor driven electric automobile | |
| CN108215747B (en) | The torque optimization method of bi-motor arrangement and convex optimized algorithm based on pure electric automobile | |
| CN113147412A (en) | Brake energy recovery strategy for rear-drive pure electric commercial vehicle | |
| CN101898558A (en) | Method for controlling drive modes of four-wheel drive strong hybrid automobile | |
| CN110834624B (en) | Full hybrid vehicle energy efficiency optimization control method based on adaptive genetic algorithm | |
| Ruan et al. | Delayed deep deterministic policy gradient-based energy management strategy for overall energy consumption optimization of dual motor electrified powertrain | |
| CN109624977B (en) | Cruise mode control method of hybrid electric vehicle | |
| CN108583293B (en) | Brake feedback torque distribution method of new energy automobile and four-wheel drive control system thereof | |
| CN203697987U (en) | Driving system of electric-electric hybrid power automobile | |
| CN106994904B (en) | Energy recovery system and method of electric automobile based on brake-by-wire | |
| CN110386149B (en) | Fault-tolerant control method of high-voltage power distribution system for distributed driving vehicle | |
| CN111539125A (en) | Integrated framework building and braking energy recovery system for combined simulation modeling electric automobile | |
| CN111688497A (en) | Feedback braking system of electric vehicle battery in high-charge state and control method | |
| Bapodra et al. | A review on hybrid electric vehicle and simulation on hybrid electric vehicle drivetrain |
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 |