CN113901575A - Method and device for adjusting self-adaptive SOC balance point after working condition identification - Google Patents
Method and device for adjusting self-adaptive SOC balance point after working condition identification Download PDFInfo
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- CN113901575A CN113901575A CN202111038108.6A CN202111038108A CN113901575A CN 113901575 A CN113901575 A CN 113901575A CN 202111038108 A CN202111038108 A CN 202111038108A CN 113901575 A CN113901575 A CN 113901575A
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- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
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Abstract
The invention relates to a method and a device for adjusting a self-adaptive SOC balance point after a working condition is identified. Belonging to the technical field of auxiliary control of energy management of hybrid electric vehicles. The device comprises an information acquisition and processing layer, a statistical analysis layer, a scene recognition layer and a final decision layer, wherein the method is used for acquiring external information and internal information of the vehicle and confirming the current vehicle state by using a time statistical method; setting a reasonable mapping relation by combining signals of the acquisition layer, performing unified correction on the mapping result to the working condition, and dividing the final scene score into scene grades; after the identification is completed based on the scene grading, a new SOC balance point is determined after the correction value is compensated on the basis of the existing system balance point. According to the method, a reasonable SOC balance point is calculated in real time based on the vehicle running state and the road scene, and the hybrid power control main program adjusts the SOC use window in real time by calling the value.
Description
Technical Field
The invention relates to a method and a device for adjusting a balance point of a hybrid vehicle, in particular to a method and a device for adjusting a self-adaptive SOC balance point after a working condition is identified. Belonging to the technical field of auxiliary control of energy management of hybrid electric vehicles.
Background
The oil-electricity hybrid vehicle is divided into a plug-in hybrid vehicle and a non-plug-in hybrid vehicle according to whether an external charging design exists, wherein the plug-in hybrid vehicle mostly adopts a high-voltage battery system with large capacity, so that the SOC balance point mostly adopts a fixed type, namely, a fixed SOC balance point is selected according to the capacity of a battery pack, a power capacity meter, the attribute requirements of the whole vehicle and the like. The non-plug-in hybrid electric vehicle has the advantages that the power battery system has the characteristics of small capacity, fast SOC change, large influence of SOC on power capacity and the like, the fixed SOC balance point method is difficult to adapt to the consistency of the whole vehicle performance under the condition of multiple scenes, for example, the SOC is 50 percent, the charging and discharging capacities of battery packs under different temperature conditions are different, if the same SOC balance point is adopted in summer and winter, the vehicle is easy to have power limitation, insufficient power, deteriorated oil consumption and the like in winter, and the phenomenon that the influence is not obvious in summer occurs. To summarize, the fixed SOC balance point approach has two disadvantages: firstly, the performance difference of the whole vehicle cannot be guaranteed after the external environment of the vehicle changes (temperature, altitude and the like); secondly, the performance difference of the whole vehicle cannot be guaranteed after the internal state of the vehicle changes (the engine capacity is limited and the vehicle speed state changes).
Disclosure of Invention
The invention provides a device for self-adaptive SOC balance point adjustment after working condition identification, which summarizes and analyzes the running state and road scene of a vehicle based on information such as a driving component, power battery information, vehicle running information and environmental signals collected in a controller, calculates a reasonable SOC balance point in real time, and adjusts the use window of SOC in real time by calling the balance point value in a hybrid power control main program to ensure that the performance index of the whole vehicle can meet the expectation of a driver.
The invention relates to a device for adjusting a self-adaptive SOC balance point after identifying a working condition, which comprises:
information acquisition and processing layer: the system comprises a vehicle signal acquisition module, a vehicle signal processing module and a vehicle signal processing module, wherein the vehicle signal acquisition module is used for acquiring external information and internal information of a vehicle and calculating and processing a vehicle signal by combining the external information and the internal information;
statistical analysis layer: the system is used for confirming the current vehicle state by utilizing a time statistical method based on the information of the acquisition processing layer;
scene recognition layer: the system comprises a signal acquisition layer, a statistical analysis layer, a mapping layer and a mapping layer, wherein the signal acquisition layer is used for setting a reasonable mapping relation according to the influence weight of the whole vehicle, and then the mapping result is subjected to unified correction of working conditions by combining with the working conditions of the statistical analysis layer, so that the final scene score is divided into scene grades;
and a final decision layer: the method is used for determining a new SOC balance point after finishing identification based on scene grading and compensating a correction value on the basis of the existing system balance point.
Further, the external information includes ambient temperature, altitude, rainfall, GPS; the internal information includes vehicle speed, hill, pedal, air conditioning status, and status information inside the hybrid controller.
Further, it is characterized in that: the calculation processing vehicle signal comprises the vehicle speed, the acceleration, the gradient, the load mass and the running mode of the vehicle.
Further, the current vehicle state includes mountain driving, high-speed driving, and plateau driving.
The invention also provides a method for adjusting the self-adaptive SOC balance point after the working condition is identified, which is characterized by comprising the following steps: the method specifically comprises the following steps: collecting external information and internal information of a vehicle, and calculating and processing vehicle signals by combining the external information and the internal information; confirming the current vehicle state by using a time statistical method based on the information of the acquisition processing layer; setting a reasonable mapping relation according to the influence weight of the whole vehicle by combining the signal of the acquisition layer, and performing unified correction of the working condition on the mapping result by combining the working condition of the statistical analysis layer to divide the final scene score into scene grades; after the identification is completed based on the scene grading, a new SOC balance point is determined after the correction value is compensated on the basis of the existing system balance point.
Further, in the method, the external information includes ambient temperature, altitude, rainfall, GPS; the internal information comprises the vehicle speed, the ramp, the pedal, the air conditioner state and the state information inside the hybrid power controller; the calculation processing vehicle signal comprises the vehicle speed, the acceleration, the gradient, the load mass and the running mode of the vehicle.
Further, in the method, the scene classification specifically includes: the impact factors for the scene hierarchy definition include: the signals of the information acquisition layer comprise a driving mode, an air conditioner state, a load quality, a rainfall state, a mountain road running condition mark, a plateau running condition mark, a high-speed running condition mark and an idle speed power generation condition mark which are processed by the statistical analysis layer, a weight coefficient is set based on the whole vehicle influence evaluation of each influence factor, the current scene division of the vehicle is finally obtained in a numerical value statistical mode, and five scene gradient grades are divided according to the scene division.
Further, in the method, the calculation processing vehicle signal comprises vehicle speed, acceleration, gradient, load mass and running mode of the vehicle.
Compared with the scheme in the prior art, the method and the device for self-adaptive SOC balance point adjustment after the working condition is identified have the following advantages that:
1. dynamically adjusting the SOC balance point of the vehicle to ensure that the dynamic property and the economical efficiency of the vehicle meet the expectation of a driver;
2. the decision is intelligently made according to the current SOC state and the reference balance point after scene recognition, the actual experience of customers is improved, and the decision basis of the idle speed power generation working condition is offset correction based on the current SOC point instead of the reference balance point.
3. According to the method, a reasonable SOC balance point is calculated in real time based on the vehicle running state and the road scene, and the hybrid power control main program adjusts the SOC use window in real time by calling the value.
Drawings
FIG. 1 is a schematic diagram of an apparatus for adaptive SOC balance point adjustment after identifying operating conditions according to the present invention.
FIG. 2 is a schematic diagram of a method for adaptive SOC balance point adjustment after identifying operating conditions according to the present invention.
Fig. 3 is a diagram illustrating the rainfall signal calibration.
Fig. 4 is a schematic diagram of GPS signal calibration.
Fig. 5 is a schematic diagram of determination of the mountain road running condition.
Fig. 6 is a schematic diagram of determination of the high-speed running condition.
FIG. 7 is a schematic diagram illustrating the determination of the plateau driving condition.
Fig. 8 is a schematic view of scene division.
Fig. 9 is a schematic diagram of an adaptive SOC balance point determination process.
Detailed Description
As shown in fig. 1 and fig. 2, the algorithm according to the present invention includes an information acquisition and processing layer, a statistical analysis layer, a scene recognition layer, and a final decision layer. The SOC balance point of the vehicle is adjusted in real time by deconstructing the running state and the environment of the vehicle, and the functions and the realization of the specific modules are as follows;
1. information acquisition and processing layer: collecting external and internal information of a vehicle, wherein the external information comprises information such as ambient temperature, altitude, rainfall, GPS and the like; the vehicle internal information includes vehicle speed, hill, pedal, air conditioning state, and state information (such as running mode state, driving force state, failure diagnosis, etc.) inside the hybrid controller. Calculating and processing signals of the vehicle such as speed, acceleration, gradient, load mass, running mode and the like by combining the relevant information;
signals such as vehicle speed, acceleration, pedal change rate, running mode, air conditioner state, gradient, load mass, ambient temperature and altitude are all necessary signals of the whole vehicle control system, and the signal processing module of control system software has independent processing, wherein the gradient and load mass information signals depend on whether the vehicle is provided with an ESP or not, and if the vehicle is not provided with the ESP, the signal processing module of the whole vehicle control system cannot provide the signals. The signals are not further processed in the information acquisition and processing layer of the invention and are only referred to by the signals;
the rainfall signal is processed by comprehensively considering the feedback state of the windscreen wiper, the feedback state of the window, the feedback state of the skylight and the like, and the basic processing logic refers to the following steps as shown in FIG. 3:
calibration principle: timing and setting time is recommended for 10-30 s;
as shown in fig. 4, the GPS signal processing method determines approximate altitude and air temperature information of the vehicle environment according to the longitude and latitude information and the system date information, in combination with the database:
calibration principle: the database is used for dividing GPS boundaries and historical monthly weather of each region;
2. statistical analysis layer: based on the information of the acquisition processing layer, determining the current vehicle state such as mountain road driving, high-speed driving, plateau driving and the like by using a time statistical method;
the main body of the judgment processing of the mountain road running condition comprises the judgment of three conditions, and the three conditions are in an OR relationship. Firstly, in a certain time, the counting times of the ramp and the vehicle speed exceeding a set value exceed a certain value; secondly, in a certain time, the gradient value is large, and the vehicle speed meets the requirement; finally, the change in altitude exceeds a certain value over a certain time. The specific processing logic diagram is shown in fig. 5:
calibration principle: the counting timing calibration time is recommended to be 200-300 s, the timing counting calibration value is 100-150, and the slope calibration value is 2-5%; the vehicle speed is calibrated to be 30-50 kph; setting the timing and setting suggestions for 50-100 s when the large gradient is 10-15% in a rated value and the vehicle speed is 20-40 kph; the delay altitude time is recommended to be 200-300 s, and the altitude change calibration value is 300-600 s;
the determination process of the high-speed driving condition refers to that the vehicle speed continuously exceeds a certain value within a certain time, wherein the vehicle speed considers the correction of the gradient, and the processing diagram is shown in fig. 6:
calibration principle: the principle of a calibration curve of the gradient and the vehicle speed is that the larger the gradient value is, the smaller the vehicle speed is, the timing and setting are recommended for 200-300 s
The judgment processing of the plateau driving condition refers to the average altitude information obtained by the GPS and the situation of the altitude within a certain time, and the processing diagram is as shown in fig. 7:
calibration principle: the high-altitude calibration value is recommended to be 2500-3000 m, the high-altitude timing setting time is recommended to be 100-200 s, the average altitude calibration value is recommended to be 1500-2000 m, and the average altitude timing setting time is 100-200 s;
3. scene recognition layer: as shown in fig. 8, a reasonable mapping relationship is set according to the influence weight of the entire vehicle by combining with the partial signals of the acquisition layer, and then the mapping result is uniformly corrected according to the working condition of the statistical analysis layer, so that the final scene score is classified into the scene grade. The premise of the hierarchical definition of the scene is that firstly, the influence quantity of the hierarchical key input of the scene needs to be determined, and in the invention, the influence factors for the hierarchical definition of the scene comprise: the signals of the information acquisition layer comprise driving modes, air conditioner states, load quality and rainfall states, and mountain road running condition signs, plateau running condition signs, high-speed running condition signs and idling power generation condition signs which are processed by the statistical analysis layer. And setting a weight coefficient based on the evaluation of the influence of the whole vehicle on each influence factor, finally obtaining the current scene score of the vehicle in a numerical statistic mode, and dividing five scene gradient grades aiming at the scene score.
Calibration principle: driving mode severity is Eco/Nor/Pwr from low to high respectively, and the SOC correction principle of driving mode mapping is that the more severe the driving mode is, the larger the SOC correction value is; the larger the air conditioner and the load power is, the larger the SOC correction value is; the larger the load mass is, the larger the SOC correction value is; the larger the rainfall indicated by the rainfall state is, the larger the SOC correction value is; the principle of working condition correction is as follows: the correction coefficient of the mountain road running condition is recommended to be 1.3-1.5, the correction coefficient of the plateau running condition is recommended to be 1.1-1.3, and the correction coefficient of the high-speed running condition is recommended to be 1.1-1.2; the scene grading division principle suggests upgrading one grade every 3 points;
4. and a final decision layer: after the recognition is completed based on the scene classification as shown in fig. 9, a new SOC balance point is determined after compensating the correction value based on the existing system balance point. The SOC correction value at the moment is expressed as a numerical result of weight influence under the working condition, the numerical result is universal and needs to be adapted by combining the capacity of a battery pack of a vehicle, so that a layer of capacity-based correction needs to be added on the basis of the correction value, and the sum of the corrected final result and the SOC balance point of the system is the adaptive SOC balance point after scene recognition;
calibration principle: scene grading is defined by referring to the identification layer; the principle of the battery capacity correction curve is that the larger the capacity is, the smaller the correction coefficient is;
example (c): by collecting the vehicle speed state and performing time statistics, the vehicle is confirmed to be in high-speed running and the vehicle speed fluctuation is not large, and meanwhile, the energy consumption states of the air conditioner and the DCDC are collected, the working condition of high-speed running and air conditioner starting is defined as an application scene with moderate SOC requirements, and a reference SOC balance point is provided according to the application scene with moderate requirements; and considering that the vehicle is in a high-speed running state, the sensitivity of a driver to starting and stopping is greatly reduced, and the engine does not have flameout requirement under a high-speed working condition by combining the current system SOC balance point, and finally the system makes a decision by taking the reference SOC balance point as the current system balance point target.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (8)
1. The utility model provides a device of self-adaptation SOC balance point adjustment after discernment operating mode which characterized in that: the device comprises:
information acquisition and processing layer: the system comprises a vehicle signal acquisition module, a vehicle signal processing module and a vehicle signal processing module, wherein the vehicle signal acquisition module is used for acquiring external information and internal information of a vehicle and calculating and processing a vehicle signal by combining the external information and the internal information;
statistical analysis layer: the system is used for confirming the current vehicle state by utilizing a time statistical method based on the information of the acquisition processing layer;
scene recognition layer: the system comprises a signal acquisition layer, a statistical analysis layer, a mapping layer and a mapping layer, wherein the signal acquisition layer is used for setting a reasonable mapping relation according to the influence weight of the whole vehicle, and then the mapping result is subjected to unified correction of working conditions by combining with the working conditions of the statistical analysis layer, so that the final scene score is divided into scene grades;
and a final decision layer: the method is used for determining a new SOC balance point after finishing identification based on scene grading and compensating a correction value on the basis of the existing system balance point.
2. The device for identifying adaptive SOC balance point adjustments after conditions of claim 1, wherein: the external information comprises ambient temperature, altitude, rainfall and GPS; the internal information includes vehicle speed, hill, pedal, air conditioning status, and status information inside the hybrid controller.
3. The device for identifying adaptive SOC balance point adjustments after conditions of claim 1, wherein: the calculation processing vehicle signal comprises the vehicle speed, the acceleration, the gradient, the load mass and the running mode of the vehicle.
4. The device for identifying adaptive SOC balance point adjustments after conditions of claim 1, wherein: the current vehicle state comprises mountain road running, high-speed running and plateau running.
5. A method for adjusting a self-adaptive SOC balance point after identifying a working condition is characterized by comprising the following steps: the method specifically comprises the following steps: collecting external information and internal information of a vehicle, and calculating and processing vehicle signals by combining the external information and the internal information; confirming the current vehicle state by using a time statistical method based on the information of the acquisition processing layer; setting a reasonable mapping relation according to the influence weight of the whole vehicle by combining the signal of the acquisition layer, and performing unified correction of the working condition on the mapping result by combining the working condition of the statistical analysis layer to divide the final scene score into scene grades; after the identification is completed based on the scene grading, a new SOC balance point is determined after the correction value is compensated on the basis of the existing system balance point.
6. The method for adaptive SOC balance point adjustment after identifying conditions of claim 1, wherein: in the method, the external information comprises ambient temperature, altitude, rainfall and GPS; the internal information comprises the vehicle speed, the ramp, the pedal, the air conditioner state and the state information inside the hybrid power controller; the calculation processing vehicle signal comprises the vehicle speed, the acceleration, the gradient, the load mass and the running mode of the vehicle.
7. The method for adaptive SOC balance point adjustment after identifying conditions of claim 1, wherein: in the method, the scene grading specifically comprises the following steps: the impact factors for the scene hierarchy definition include: the signals of the information acquisition layer comprise a driving mode, an air conditioner state, a load quality, a rainfall state, a mountain road running condition mark, a plateau running condition mark, a high-speed running condition mark and an idle speed power generation condition mark which are processed by the statistical analysis layer, a weight coefficient is set based on the whole vehicle influence evaluation of each influence factor, the current scene division of the vehicle is finally obtained in a numerical value statistical mode, and five scene gradient grades are divided according to the scene division.
8. The method for adaptive SOC balance point adjustment after identifying conditions of claim 1, wherein: in the method, the calculation processing vehicle signal comprises the vehicle speed, the acceleration, the gradient, the load mass and the running mode of the vehicle.
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| CN114460226A (en) * | 2022-01-10 | 2022-05-10 | 上海工程技术大学 | Scene gas memory and recognition method and application thereof |
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| CN114460226A (en) * | 2022-01-10 | 2022-05-10 | 上海工程技术大学 | Scene gas memory and recognition method and application thereof |
| CN114460226B (en) * | 2022-01-10 | 2024-02-06 | 上海工程技术大学 | Scene gas memorizing and identifying method and application thereof |
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