Energy management method and system for plug-in hybrid electric vehicle

Abstract

The application discloses a kind of energy management method and system of plug-in hybrid electric vehicle, the method includes: in the process of vehicle driving, the multidimensional data of vehicle is acquired in real time, according to the multidimensional data, the actual demand power of vehicle is determined and the predicted demand power is obtained according to current throttle opening rate of change;Established by fuel economy, battery health, power responsiveness Composed of optimization objective function, according to the predicted demand power of vehicle, the torque distribution result of engine and motor is obtained by solving optimization objective function in rolling time domain using model predictive control algorithm according to the predicted demand power of vehicle;The torque of engine and motor is distributed by the torque distribution result of engine and motor respectively, to control vehicle.The application solves the problem of poor energy management effect of plug-in hybrid electric vehicle in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle management, in particular to an energy management method and system for a plug-in hybrid power vehicle. BACKGROUND

[0002] The current energy management strategy of plug-in hybrid electric vehicles mainly relies on fixed rules or static optimization algorithms. For example, threshold control based on battery power (such as pure electric drive when the power is higher than 30%, and the engine is started to charge when the power is lower than 20%), or torque distribution strategy based on engine efficiency curve. These methods have significant defects: only focusing on a single target, ignoring multi-objective coordination, resulting in poor energy management effect. SUMMARY

[0003] Therefore, the purpose of the present application is to provide an energy management method and system for a plug-in hybrid power vehicle, which aims to solve the problem of poor energy management effect in the prior art.

[0004] The present application is implemented as follows:

[0005] An energy management method for a plug-in hybrid power vehicle, the method comprising:

[0006] Real-time acquisition of multi-dimensional data of the vehicle during vehicle driving, determination of the actual demand power of the vehicle according to the multi-dimensional data, and obtaining of the predicted demand power according to the current throttle opening rate;

[0007] Establishing an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solving the optimization objective function in a rolling time domain to obtain the torque distribution results of the engine and the motor by using a model predictive control algorithm according to the predicted demand power of the vehicle;

[0008] Distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor, respectively, to control the vehicle.

[0009] Further, the energy management method for the plug-in hybrid power vehicle, wherein the step of establishing an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solving the optimization objective function in a rolling time domain to obtain the torque distribution results of the engine and the motor by using a model predictive control algorithm according to the predicted demand power of the vehicle:

[0010] When the battery power is lower than the power threshold, the weight of fuel economy is increased, and the motor output power is limited;

[0011] When the rapid acceleration demand is detected, the power response weight is increased, and the engine is allowed to temporarily deviate from the high efficiency area to improve the acceleration performance.

[0012] Further, the energy management method of the plug-in hybrid power vehicle, wherein the method further comprises:

[0013] If the average demand power in the past preset time period is higher than the preset percentage of the calibration value, the upper limit of the balance interval of the battery power is adjusted downward to avoid motor overload at high power;

[0014] If the vehicle is frequently in the low-speed crawling working condition, the lower limit of the balance interval of the battery power is adjusted upward to reduce the number of engine start-stop.

[0015] Further, the energy management method of the plug-in hybrid power vehicle, wherein the method further comprises:

[0016] According to the historical working point distribution of the engine, if the actual torque utilization rate in a certain speed interval is lower than a preset percentage, the torque upper limit of the speed interval is reduced by a preset percentage to promote the working point to concentrate in the high-efficiency area;

[0017] The frequency of sudden acceleration and sudden deceleration is counted to identify the driver type, including aggressive type and conservative type, for the aggressive driver, the torque distribution proportion of the motor is expanded, and the power response time is shortened, and for the conservative driver, the battery power balance is preferentially maintained, and the pure electric cruising range is extended.

[0018] Further, the energy management method of the plug-in hybrid power vehicle, wherein the step of distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor to control the vehicle further comprises:

[0019] When the battery power is less than the power threshold and the demand power exceeds the motor capacity, the engine is forced to start and enter the series mode, and the generator is driven by the engine to charge the battery;

[0020] When the battery temperature is greater than the temperature threshold, the charging and discharging power is limited to a preset percentage of the rated value, and the cooling system is activated.

[0021] Further, the energy management method of the plug-in hybrid power vehicle, wherein the multi-dimensional data includes vehicle speed, throttle opening, battery power (SOC), engine speed, motor speed, slope angle, acceleration, and ambient temperature.

[0022] Further, the energy management method of the plug-in hybrid power vehicle, wherein if the slope sensor for collecting the slope angle fails, the slope angle is estimated according to the historical data of the vehicle speed and acceleration.

[0023] Another object of the present application is to provide an energy management system for a plug-in hybrid power vehicle, the system comprising:

[0024] An acquisition module is configured to acquire multi-dimensional data of the vehicle in real time during driving of the vehicle, determine actual demand power of the vehicle according to the multi-dimensional data, and obtain predicted demand power according to a current accelerator opening degree change rate;

[0025] An optimization module is configured to establish an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solve the optimization objective function in a rolling time domain by using a model predictive control algorithm according to the predicted demand power of the vehicle to obtain a torque distribution result of the engine and the motor.

[0026] A control module is configured to distribute the torque of the engine and the motor by using the torque distribution result of the engine and the motor, respectively, to control the vehicle.

[0027] Another object of the present application is to provide a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method according to any one of the above.

[0028] Another object of the present application is to provide an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method according to any one of the above.

[0029] The present application acquires multi-dimensional data of the vehicle in real time during driving of the vehicle, determines actual demand power of the vehicle according to the multi-dimensional data, and obtains predicted demand power according to a current accelerator opening degree change rate; establishes an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solves the optimization objective function in a rolling time domain by using a model predictive control algorithm according to the predicted demand power of the vehicle to obtain a torque distribution result of the engine and the motor; and distributes the torque of the engine and the motor by using the torque distribution result of the engine and the motor, respectively, to control the vehicle. The present application realizes real-time calculation of demand power of the vehicle, replaces traditional fixed threshold control, solves the problem of inefficient operation of the engine, integrates fuel economy, battery health, and power performance into a unified optimization framework, and realizes optimal comprehensive performance by model predictive multi-objective collaborative control. The present application solves the problem of poor energy management effect of the plug-in hybrid electric vehicle in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flowchart of the energy management method of the plug-in hybrid electric vehicle in the first embodiment of the present application;

[0031] Figure 2 A structural block diagram of the energy management system of the plug-in hybrid electric vehicle in the third embodiment of the present application.

[0032] The following specific embodiments will further illustrate the present application in combination with the above drawings. Detailed Implementation

[0033] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0034] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed types.

[0036] Example One

[0037] Please see Figure 1 The figure shows an energy management method for a plug-in hybrid vehicle according to the first embodiment of the present invention, the method including steps S10 to S12.

[0038] Step S10: Acquire multi-dimensional data of the vehicle in real time during the vehicle's operation, determine the actual power demand of the vehicle based on the multi-dimensional data, and obtain the predicted power demand based on the current throttle opening change rate.

[0039] The system collects multi-dimensional data in real time via the vehicle's CAN bus and sensor network, including vehicle speed, throttle opening, battery charge (SOC), engine speed, motor speed, slope angle (calculated by the inertial measurement unit), acceleration, and ambient temperature. After acquiring the multi-dimensional data, preprocessing is performed to provide a foundation for subsequent analysis. The data preprocessing stage employs a moving average filtering algorithm to eliminate noise interference; for example, the vehicle speed signal undergoes a 5-point mean filter, and the slope angle is dynamically corrected using a Kalman filter to ensure accuracy within ±0.5°. For the battery SOC signal, the system additionally incorporates charge / discharge current integral correction to avoid long-term accumulated errors.

[0040] Specifically, based on the vehicle longitudinal dynamics model, the current demand power is calculated in real time. The demand power is composed of the following four resistances: slope resistance, air resistance, acceleration resistance, and rolling resistance. Among them, in the calculation process, the transmission efficiency is set as the default value 0.92, the wind resistance coefficient and the windward area are determined according to the vehicle parameters, the rotational mass coefficient is related to the gearbox speed ratio, and the system predicts the demand power trend in the next 3 seconds according to the current throttle opening rate. For example, when the throttle opening increases by more than 50% in 0.5 seconds, it is determined as an urgent acceleration intention, and the motor torque margin is reserved in advance.

[0041] Step S11, an optimization objective function composed of fuel economy, battery health, and power responsiveness is established, and the torque distribution results of the engine and the motor are obtained by solving the optimization objective function in the rolling time domain using model predictive control algorithm according to the predicted demand power of the vehicle.

[0042] Among them, the core of optimization adopts model predictive control (MPC) algorithm. The optimization objective function composed of fuel economy, battery health and power responsiveness is established, and the following objectives are solved in the rolling time domain (the prediction time domain is set to 5 seconds, and the control period is 50 ms): fuel economy: prefer to make the engine work in the high efficiency zone (such as speed 1500-2500 rpm, torque 40-120 Nm), and obtain the lowest fuel consumption point in the engine universal characteristic diagram by table lookup method; battery health: limit the battery charge and discharge rate (such as maximum 2C), avoid SOC fluctuation more than 10% in single cycle; power responsiveness: take the throttle opening rate as the reference, and require that the response delay of torque distribution instruction is less than 200 ms. In the optimization process, the system can also dynamically adjust the target weight. For example, when the SOC is lower than 25%, the fuel economy weight is increased from 60% to 70%, and the motor output power is limited; when the urgent acceleration demand is detected, the power response weight is temporarily increased to 30%, allowing the engine to temporarily deviate from the high efficiency zone to improve the acceleration performance.

[0043] Step S12, the torque of the engine and the motor is distributed respectively through the torque distribution results of the engine and the motor to control the vehicle.

[0044] Wherein, the torque distribution of the engine and the motor is dynamically adjusted by the real-time obtained torque distribution result. Specifically, the engine target torque, the motor target torque and the clutch state are issued to the engine ECU, the motor controller and the clutch actuator through the CAN bus: the engine target torque is set according to the optimization result to set the fuel injection amount and the throttle opening; the motor target torque is dynamically adjusted in combination with the battery temperature limit to automatically reduce the output capacity by 10%-20% at high temperature; the clutch state is based on the synchronous control of the rotating speed to realize the shockless engagement when switching between the pure electric mode and the hybrid mode. The system monitors the actual fuel consumption rate, the battery current and the power response delay in real time, and compares them with the predicted values. If the deviation continuously exceeds the threshold value (such as the fuel consumption deviation > 5%), the parameter self-correction process is triggered to recalibrate the resistance coefficient in the dynamics model or the optimization weight.

[0045] In summary, the energy management method of the plug-in hybrid power vehicle in the above embodiment of the application, by obtaining multi-dimensional data of the vehicle in real time during vehicle driving, determining the actual demand power of the vehicle according to the multi-dimensional data, and obtaining the predicted demand power according to the current throttle opening rate; an optimization objective function composed of fuel economy, battery health and power responsiveness is established, and the torque distribution result of the engine and the motor is obtained by solving the optimization objective function in the rolling time domain using the model predictive control algorithm according to the predicted demand power of the vehicle; the torque of the engine and the motor is distributed by the torque distribution result of the engine and the motor respectively, so as to control the vehicle, calculate the demand power of the vehicle in real time, replace the traditional fixed threshold control, solve the problem of inefficient operation of the engine, integrate the fuel economy, the battery health and the power performance into a unified optimization framework, and realize the optimal comprehensive performance through the model predictive multi-objective collaborative control. The problem of poor energy management effect in the prior art is solved.

[0046] Example Two

[0047] The embodiment also provides an energy management method of a plug-in hybrid power vehicle. The energy management method of the plug-in hybrid power vehicle provided in the embodiment is different from the energy management method of the plug-in hybrid power vehicle provided in the embodiment in that:

[0048] The method further comprises:

[0049] If the average demand power in the past preset time period is higher than the preset percentage of the calibration value, the upper limit of the balance interval of the battery power is adjusted downward to avoid the motor overload at high power;

[0050] If the vehicle frequently operates in the low-speed crawling condition, the lower limit of the balance interval of the battery power is adjusted upward to reduce the number of engine start-stop;

[0051] According to the engine historical working point distribution, if the actual torque utilization rate of a certain speed interval is lower than a preset percentage, the torque upper limit of the speed interval is reduced by the preset percentage, so as to promote the working point to concentrate in the high-efficiency area;

[0052] The frequency of sudden acceleration and sudden deceleration is counted to identify the driver type, including aggressive type and conservative type, the torque distribution proportion of the motor is expanded and the power response time is shortened for the aggressive driver, and the battery power balance is preferentially maintained and the pure electric cruising range is prolonged for the conservative driver.

[0053] The adaptive parameter correction logic is set, the self-learning module is built in the system, and the control parameters are dynamically optimized according to the historical driving data: 1) SOC balance interval adjustment: if the average demand power in the past 30 minutes is higher than the calibration value by 20%, the upper limit of the SOC balance interval is lowered from 80% to 75%, so as to avoid the motor overload under high power; if the vehicle is frequently in the low-speed crawling working condition, the lower limit of the SOC is raised from 20% to 25%, so as to reduce the engine start-stop times. 2) Engine torque margin correction: according to the engine historical working point distribution, if it is found that the actual torque utilization rate of a certain speed interval (such as 1800 rpm) is lower than 50% for a long time, the torque upper limit of the interval is reduced by 10%, so as to promote the working point to concentrate in the high-efficiency area. 3) Driving style adaptation: the frequency of sudden acceleration and sudden deceleration is counted to identify the driver type (such as aggressive type and conservative type). The torque distribution proportion of the motor is expanded and the power response time is shortened for the aggressive driver, and the battery power balance is preferentially maintained and the pure electric cruising range is prolonged for the conservative driver.

[0054] In addition, in some optional embodiments of the present application, the step of distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor respectively to control the vehicle further comprises:

[0055] When the battery power is less than the power threshold and the demand power exceeds the motor capacity, the engine is forced to start and enter the series mode, and the generator is driven by the engine to charge the battery;

[0056] When the battery temperature is greater than the temperature threshold, the charging and discharging power is limited to a preset percentage of the rated value, and the cooling system is activated at the same time.

[0057] Specifically, the abnormal working condition processing strategy is set: low power forced charging: when SOC<15% and demand power exceeds the motor capacity, the engine is forced to start and enter the series mode, and the generator is driven by the engine to charge the battery; high temperature protection: when the battery temperature is greater than 45℃, the charging and discharging power is limited to 80% of the rated value, and the cooling system is activated at the same time; sensor fault tolerance: if the slope sensor fails, the system estimates the slope according to the historical data of vehicle speed and acceleration, and the accuracy loss is controlled within 10%.

[0058] In summary, the energy management method of the plug-in hybrid power vehicle in the above-mentioned embodiments of the present application, by acquiring the multi-dimensional data of the vehicle in real time during the vehicle driving process, determining the actual demand power of the vehicle according to the multi-dimensional data and obtaining the predicted demand power according to the current throttle opening rate; establishing an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solving the optimization objective function in the rolling time domain by using the model predictive control algorithm according to the predicted demand power of the vehicle to obtain the torque distribution results of the engine and the motor; the torque of the engine and the motor is distributed respectively through the torque distribution results of the engine and the motor, so as to control the vehicle, calculate the demand power of the vehicle in real time, replace the traditional fixed threshold control, solve the problem of inefficient operation of the engine, and integrate the fuel economy, the battery health, and the power into a unified optimization framework, so as to realize the optimal comprehensive performance through the model predictive multi-objective collaborative control. The problem of poor energy management effect in the prior art is solved.

[0059] Example Three

[0060] Please refer to Figure 2 , which is an energy management system of a plug-in hybrid power vehicle proposed in the third embodiment of the present application, the system comprises:

[0061] The acquisition module 100 is configured to acquire the multi-dimensional data of the vehicle in real time during the vehicle driving process, determine the actual demand power of the vehicle according to the multi-dimensional data, and obtain the predicted demand power according to the current throttle opening rate.

[0062] The optimization module 200 is configured to establish an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solve the optimization objective function in the rolling time domain by using the model predictive control algorithm according to the predicted demand power of the vehicle to obtain the torque distribution results of the engine and the motor.

[0063] The control module 300 is configured to distribute the torque of the engine and the motor respectively through the torque distribution results of the engine and the motor, so as to control the vehicle.

[0064] Further, the energy management system of the plug-in hybrid power vehicle, wherein the step of establishing an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solving the optimization objective function in the rolling time domain by using the model predictive control algorithm according to the predicted demand power of the vehicle to obtain the torque distribution results of the engine and the motor comprises:

[0065] When the battery power is lower than the power threshold, the weight of the fuel economy is improved, and the motor output power is limited;

[0066] When detecting the demand for rapid acceleration, the power response weight is increased, allowing the engine to temporarily deviate from the high-efficiency zone to improve acceleration performance.

[0067] Further, the energy management system of the plug-in hybrid power vehicle, wherein the system further comprises:

[0068] If the average demand power in the past preset time period is higher than the preset percentage of the calibration value, the upper limit of the balance range of the battery power is adjusted downward to avoid motor overload at high power;

[0069] If the vehicle is frequently in the low-speed crawling working condition, the lower limit of the balance range of the battery power is adjusted upward to reduce the number of engine start-stop.

[0070] Further, the energy management system of the plug-in hybrid power vehicle, wherein the system further comprises:

[0071] According to the historical working point distribution of the engine, if the actual torque utilization rate in a certain speed range is lower than a preset percentage, the torque upper limit of the speed range is reduced by a preset percentage to promote the working point to concentrate in the high-efficiency zone;

[0072] The frequency of rapid acceleration and rapid deceleration is counted to identify the driver type, including aggressive type and conservative type. For aggressive drivers, the torque distribution ratio of the motor is increased and the power response time is shortened. For conservative drivers, the battery power balance is preferentially maintained and the pure electric range is extended.

[0073] Further, the energy management system of the plug-in hybrid power vehicle, wherein the step of distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor to control the vehicle further comprises:

[0074] When the battery power is less than the power threshold and the demand power exceeds the motor capacity, the engine is forced to start and enter the series mode, and the generator is driven by the engine to charge the battery;

[0075] When the battery temperature is greater than the temperature threshold, the charging and discharging power is limited to a preset percentage of the rated value, and the cooling system is activated.

[0076] Further, the energy management system of the plug-in hybrid power vehicle, wherein the multi-dimensional data includes vehicle speed, throttle opening, battery power (SOC), engine speed, motor speed, slope angle, acceleration, and ambient temperature.

[0077] Further, the energy management system of the plug-in hybrid power vehicle, wherein if the slope sensor for collecting the slope angle fails, the slope angle is estimated according to the historical data of the vehicle speed and acceleration.

[0078] The functions or operation steps realized when the above modules are executed are substantially the same as the method embodiments, and will not be described herein again.

[0079] Example Four

[0080] Another aspect of the present application further provides a readable storage medium, which stores a computer program, and the program realizes the steps of the method according to any one of the above embodiments 1 to 2 when executed by a processor.

[0081] Example Five

[0082] Another aspect of the present application further provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor realizes the steps of the method according to any one of the above embodiments 1 to 2 when executing the program.

[0083] The technical features of the above embodiments can be combined in any manner, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0084] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for realizing the logic function, which can be embodied in any computer readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution system, apparatus or device. For the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport the program for use by or in conjunction with the instruction execution system, apparatus or device.

[0085] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic conversion, interpretation or processing, if necessary, in other suitable manner, and then stored in a computer memory.

[0086] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the embodiments described above, the various steps or methods can be implemented, in part, or in whole, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), or other programmable logic device, etc.

[0087] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0088] The above-described embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the patent scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A method of energy management for a plug-in hybrid electric vehicle, the method comprising: The method comprises: acquiring multi-dimensional data of the vehicle in real time during vehicle driving, determining actual demand power of the vehicle according to the multi-dimensional data, and obtaining predicted demand power according to a current accelerator opening degree change rate; establishing an optimization objective function composed of fuel economy, battery health and power responsiveness, and solving the optimization objective function in a rolling time domain by using a model predictive control algorithm according to the predicted demand power of the vehicle to obtain torque distribution results of the engine and the motor; distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor respectively to control the vehicle; The method further comprises: if the average demand power in the past preset time period is higher than the preset percentage of the calibration value, the upper limit of the balance interval of the battery power is adjusted downward to avoid motor overload at high power; if the vehicle is frequently in a low-speed crawling working condition, the lower limit of the balance interval of the battery power is adjusted upward to reduce the number of engine start-stop; The method further comprises: according to the historical working point distribution of the engine, if the actual torque utilization rate of a certain speed interval is lower than a preset percentage, the torque upper limit of the speed interval is reduced by a preset percentage to promote the working point to concentrate in the high-efficiency area; statistically analyzing the frequency of sudden acceleration and sudden deceleration to identify the driver type, including aggressive type and conservative type, for aggressive drivers, the motor torque distribution ratio is expanded and the power response time is shortened, and for conservative drivers, the battery power balance is preferentially maintained and the pure electric cruising range is extended.

2. The energy management method for a plug-in hybrid electric vehicle according to claim 1, characterized by, In the step of establishing an optimization objective function composed of fuel economy, battery health and power responsiveness, and solving the optimization objective function in a rolling time domain by using a model predictive control algorithm according to the predicted demand power of the vehicle to obtain torque distribution results of the engine and the motor: when the battery power is lower than the power threshold, the weight of fuel economy is increased, and the motor output power is limited; when sudden acceleration demand is detected, the power response weight is increased, and the engine is temporarily allowed to deviate from the high-efficiency area to improve the acceleration performance.

3. The energy management method for a plug-in hybrid electric vehicle of claim 1, wherein, Before the step of distributing the torque of the engine and the motor through the torque distribution results of the engine and the motor respectively to control the vehicle, the system further comprises: when the battery power is less than the power threshold and the demand power exceeds the motor capacity, the engine is forced to start and enter the series mode, and the engine drives the generator to charge the battery; when the battery temperature is greater than the temperature threshold, the charging and discharging power is limited to a preset percentage of the rated value, and the cooling system is activated.

4. The energy management method for a plug-in hybrid electric vehicle of claim 1, wherein, The multi-dimensional data includes vehicle speed, accelerator opening degree, battery power (SOC), engine speed, motor speed, slope angle, acceleration, and ambient temperature.

5. The energy management method for a plug-in hybrid electric vehicle according to claim 4, wherein, If the slope sensor for collecting the slope angle fails, the slope angle is estimated according to the historical data of the vehicle speed and the acceleration.

6. An energy management system for a plug-in hybrid electric vehicle, the system comprising: The system for implementing the energy management method of the plug-in hybrid vehicle according to any one of claims 1 to 5 comprises: an acquisition module for acquiring multi-dimensional data of the vehicle in real time during vehicle driving, determining actual demand power of the vehicle according to the multi-dimensional data, and obtaining predicted demand power according to a current accelerator opening degree change rate; An optimization module is configured to establish an optimization objective function composed of fuel economy, battery health, and power responsiveness, and to solve the optimization objective function in a rolling time domain by using a model predictive control algorithm according to a predicted demand power of the vehicle to obtain a torque distribution result of the engine and the motor. A control module is configured to distribute the torque of the engine and the motor according to the torque distribution result of the engine and the motor, respectively, to control the vehicle.

7. A readable storage medium, having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1-5.

8. An electronic device, comprising: A computer program product includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method of any one of claims 1-5.

Images