Energy management method and system of plug-in hybrid power vehicle

Abstract

The invention discloses an energy management method and system for a plug-in hybrid power vehicle, and the method comprises the steps: obtaining the multi-dimensional data of the vehicle in real time in 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 accelerator opening degree change rate; establishing an optimized objective function composed of fuel economy, battery health degree and dynamic responsiveness, and solving the optimized 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 an engine and a motor; and the torque of the engine and the torque of the motor are distributed according to the torque distribution result of the engine and the torque distribution result of the motor so that the vehicle can be controlled. The problem that in the prior art, the energy management effect of the plug-in hybrid electric vehicle is poor is solved.

Description

Technical Field

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

[0002] The current energy management strategies of plug-in hybrid electric vehicles mainly rely on fixed rules or static optimization algorithms. For example, threshold control based on battery power (such as pure electric driving when the power is above 30%, and starting the engine to charge when it is below 20%), or torque distribution strategy based on the engine efficiency curve. These methods have significant defects: they only focus on a single goal and ignore the coordination of multiple goals, resulting in poor energy management effects. Summary of the invention

[0003] In view of this, an object of the present invention is to provide an energy management method and system for a plug-in hybrid electric vehicle, aiming to solve the problem of poor energy management effect of plug-in hybrid electric vehicles in the prior art.

[0004] The present invention is achieved in that:

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

[0006] Acquire multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determine the actual required power of the vehicle according to the multi-dimensional data, and obtain the predicted required power according to the current throttle opening change rate;

[0007] Establish an optimization objective function consisting of fuel economy, battery health, and power responsiveness. Use the model predictive control algorithm to solve the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle to obtain the torque distribution result between the engine and the motor.

[0008] The torque of the engine and the motor are distributed according to the torque distribution results of the engine and the motor respectively to control the vehicle.

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

[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 a sudden acceleration demand is detected, the power response weight is increased, allowing the engine to temporarily deviate from the high-efficiency zone to improve acceleration performance.

[0012] Furthermore, the energy management method for the plug-in hybrid vehicle further comprises:

[0013] If the average power demand 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 lowered to avoid motor overload under high power;

[0014] If the vehicle is frequently in a low-speed creeping condition, the lower limit of the battery power balance range will be adjusted upward to reduce the number of engine start and stop times.

[0015] Furthermore, the energy management method for the plug-in hybrid vehicle further comprises:

[0016] According to the historical distribution of the engine's operating points, 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 area;

[0017] Count the frequency of sudden acceleration and deceleration and identify the driver type, including aggressive and conservative types. For aggressive drivers, increase the motor torque distribution ratio and shorten the power response time. For conservative drivers, prioritize maintaining battery power balance and extend the pure electric range.

[0018] Furthermore, in the energy management method for the plug-in hybrid vehicle, before the step of distributing the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle, the method further includes:

[0019] When the battery power is less than the power threshold and the required 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;

[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] Furthermore, in the energy management method for the above-mentioned plug-in hybrid vehicle, the multi-dimensional data includes vehicle speed, throttle opening, battery charge (SOC), engine speed, motor speed, slope angle, acceleration, and ambient temperature.

[0022] Furthermore, in the energy management method for the plug-in hybrid vehicle, if a slope sensor for collecting the slope angle fails, the slope angle is estimated based on historical data of vehicle speed and acceleration.

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

[0024] An acquisition module, used to acquire multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determine the actual required power of the vehicle according to the multi-dimensional data, and obtain the predicted required power according to the current throttle opening change rate;

[0025] The optimization module is used to establish an optimization objective function consisting of fuel economy, battery health, and power responsiveness. According to the predicted power demand of the vehicle, the optimization objective function is solved in the rolling time domain using the model predictive control algorithm to obtain the torque distribution result between the engine and the motor.

[0026] The control module is used to distribute the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle.

[0027] Another object of the present invention 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 any of the methods described above.

[0028] Another object of the present invention 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 implements the steps of any of the above-described methods when executing the program.

[0029] The present invention obtains multi-dimensional data of the vehicle in real time during the driving process, determines the actual power demand of the vehicle according to the multi-dimensional data, and obtains the predicted power demand according to the current throttle opening change rate; establishes an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solves the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle using the model predictive control algorithm to obtain the torque distribution result of the engine and the motor; distributes the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle, calculates the vehicle power demand in real time, replaces the traditional fixed threshold control, solves the problem of inefficient operation of the engine, incorporates fuel economy, battery health, and power into a unified optimization framework, and achieves the best comprehensive performance through model prediction multi-objective collaborative control. The problem of poor energy management effect of plug-in hybrid vehicles in the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of an energy management method for a plug-in hybrid vehicle in a first embodiment of the present invention;

[0031] Figure 2 FIG. 4 is a structural block diagram of an energy management system for a plug-in hybrid vehicle in a third embodiment of the present invention.

[0032] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0033] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0034] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed types.

[0036] Embodiment 1

[0037] See also Figure 1 , which shows an energy management method for a plug-in hybrid vehicle in a first embodiment of the present invention, and the method includes steps S10 to S12.

[0038] Step S10, acquiring multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determining the actual required power of the vehicle according to the multi-dimensional data, and obtaining the predicted required power according to the current throttle opening change rate.

[0039] The system collects multi-dimensional data in real time through the vehicle 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 IMU), acceleration, ambient temperature, etc. After obtaining the multi-dimensional data, the data is pre-processed to provide a basis for subsequent analysis. The sliding average filter algorithm is used in the data pre-processing stage to eliminate noise interference. For example, the vehicle speed signal is processed by a 5-point mean filter, and the slope angle is dynamically corrected by the Kalman filter to ensure that the accuracy is controlled within ±0.5°. For the battery SOC signal, the system additionally introduces the charge and discharge current integral correction to avoid long-term cumulative errors.

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

[0041] Step S11, establish an optimization objective function composed of fuel economy, battery health, and power responsiveness, and use the model predictive control algorithm to solve the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle to obtain the torque distribution result of the engine and the motor.

[0042] Among them, the core of the optimization adopts the model predictive control (MPC) algorithm. Establish the optimization goals consisting of fuel economy, battery health, and power responsiveness, and solve the following goals in the rolling time domain (the prediction time domain is set to 5 seconds, and the control cycle is 50ms): Fuel economy: give priority to letting the engine work in the high-efficiency zone (such as speed 1500-2500rpm, torque 40-120Nm), and obtain the lowest fuel consumption point in the engine universal characteristic diagram through the table lookup method; Battery health: limit the battery charge and discharge rate (such as maximum 2C) to avoid SOC fluctuations of more than 10% in a single cycle; Power responsiveness: with the throttle opening change rate as a reference, the response delay of the torque distribution command is required to be less than 200ms. During 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 at the same time; when a sudden 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 acceleration performance.

[0043] Step S12, respectively distributing the torque of the engine and the motor according to the torque distribution results of the engine and the motor to control the vehicle.

[0044] Among them, the torque distribution of the engine and the motor is dynamically adjusted through the real-time torque distribution results. Specifically, it is sent to the engine ECU, motor controller and clutch actuator through the CAN bus: Engine target torque: set the injection amount and throttle opening according to the optimization results; Motor target torque: dynamically adjust the output capacity in combination with the battery temperature limit, and automatically reduce the rating by 10%-20% at high temperatures; Clutch state: when switching between pure electric mode and hybrid mode, impact-free engagement is achieved based on speed synchronization control. The system monitors the actual fuel consumption rate, battery current and power response delay in real time, and compares them with the predicted values. If the deviation continues to exceed the threshold (such as fuel consumption deviation>5%), the parameter self-correction process is triggered to recalibrate the drag coefficient or optimization weight in the dynamic model.

[0045] In summary, the energy management method for plug-in hybrid vehicles in the above embodiment of the present invention obtains multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determines the actual power demand of the vehicle according to the multi-dimensional data, and obtains the predicted power demand according to the current throttle opening change rate; establishes an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solves the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle using the model predictive control algorithm to obtain the torque distribution result of the engine and the motor; distributes the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle, calculates the vehicle power demand in real time, replaces the traditional fixed threshold control, solves the problem of inefficient operation of the engine, incorporates fuel economy, battery health, and power into a unified optimization framework, and achieves the best comprehensive performance through model prediction multi-objective collaborative control. The problem of poor energy management effect of plug-in hybrid vehicles in the prior art is solved.

[0046] Embodiment 2

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

[0048] The method further comprises:

[0049] If the average power demand 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 lowered to avoid motor overload under high power;

[0050] If the vehicle is frequently in a low-speed creeping condition, the lower limit of the battery power balance range will be adjusted upward to reduce the number of engine start and stop times;

[0051] According to the historical distribution of the engine's operating points, 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 area;

[0052] Count the frequency of sudden acceleration and deceleration and identify the driver type, including aggressive and conservative types. For aggressive drivers, increase the motor torque distribution ratio and shorten the power response time. For conservative drivers, prioritize maintaining battery power balance and extend the pure electric range.

[0053] Among them, the adaptive parameter correction logic is set: the system has a built-in self-learning module, which dynamically optimizes the control parameters according to historical driving data: 1) SOC balance interval adjustment: If the average power demand in the past 30 minutes is 20% higher than the calibration value, the upper limit of the SOC balance interval is lowered from 80% to 75% to avoid motor overload under high power; if the vehicle is frequently in low-speed creeping conditions, the SOC lower limit is raised from 20% to 25% to reduce the number of engine starts and stops. 2) Engine torque margin correction: According to the historical distribution of engine operating points, if it is found that the actual torque utilization rate of a certain speed range (such as 1800rpm) is lower than 50% for a long time, the torque upper limit of the range is reduced by 10%, prompting the operating point to concentrate in the high-efficiency area. 3) Driving style adaptation: By counting the frequency of rapid acceleration and deceleration, the driver type (such as aggressive and conservative) is identified. For aggressive drivers, the motor torque distribution ratio is expanded to shorten the power response time; for conservative drivers, priority is given to maintaining SOC balance and extending the pure electric cruising range.

[0054] In addition, in some optional embodiments of the present invention, before the step of distributing the torque of the engine and the motor according to the torque distribution results of the engine and the motor respectively to control the vehicle, the step further includes:

[0055] When the battery power is less than the power threshold and the required 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;

[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.

[0057] Specifically, the abnormal operating condition handling strategy is set: low battery forced charging: when SOC <15% and the required 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; high temperature protection: when the battery temperature is >45°C, 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 based on the historical data of vehicle speed and acceleration, and the accuracy loss is controlled within 10%.

[0058] In summary, the energy management method for plug-in hybrid vehicles in the above embodiment of the present invention obtains multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determines the actual power demand of the vehicle according to the multi-dimensional data, and obtains the predicted power demand according to the current throttle opening change rate; establishes an optimization objective function composed of fuel economy, battery health, and power responsiveness, and solves the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle using the model predictive control algorithm to obtain the torque distribution result of the engine and the motor; distributes the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle, calculates the vehicle power demand in real time, replaces the traditional fixed threshold control, solves the problem of inefficient operation of the engine, incorporates fuel economy, battery health, and power into a unified optimization framework, and achieves the best comprehensive performance through model prediction multi-objective collaborative control. The problem of poor energy management effect of plug-in hybrid vehicles in the prior art is solved.

[0059] Embodiment 3

[0060] See also Figure 2 , which shows an energy management system for a plug-in hybrid vehicle proposed in a third embodiment of the present invention, the system comprises:

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

[0062] The optimization module 200 is used 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 using a model predictive control algorithm according to the predicted power demand of the vehicle to obtain a torque distribution result between the engine and the motor;

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

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

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

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

[0067] Furthermore, the energy management system of the plug-in hybrid vehicle mentioned above, wherein the system further comprises:

[0068] If the average power demand 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 lowered to avoid motor overload under high power;

[0069] If the vehicle is frequently in a low-speed creeping condition, the lower limit of the battery power balance range will be adjusted upward to reduce the number of engine start and stop times.

[0070] Furthermore, the energy management system of the plug-in hybrid vehicle mentioned above, wherein the system further comprises:

[0071] According to the historical distribution of the engine's operating points, 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 area;

[0072] Count the frequency of sudden acceleration and deceleration and identify the driver type, including aggressive and conservative types. For aggressive drivers, increase the motor torque distribution ratio and shorten the power response time. For conservative drivers, prioritize maintaining battery power balance and extend the pure electric range.

[0073] Furthermore, in the energy management system of the plug-in hybrid vehicle, before the step of distributing the torque of the engine and the motor according to the torque distribution results of the engine and the motor respectively to control the vehicle, the step further includes:

[0074] When the battery power is less than the power threshold and the required 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;

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

[0077] Furthermore, in the energy management system of the plug-in hybrid electric vehicle, if a slope sensor for collecting the slope angle fails, the slope angle is estimated based on historical data of vehicle speed and acceleration.

[0078] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be repeated here.

[0079] Embodiment 4

[0080] Another aspect of the present invention further provides a readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps of the method described in any one of the above-mentioned embodiments 1 to 2 are implemented.

[0081] Embodiment 5

[0082] Another aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the above-mentioned embodiments one to two when executing the program.

[0083] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0085] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0086] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0087] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0088] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for energy management of a plug-in hybrid vehicle, characterized in that: The method comprises: Acquire multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determine the actual required power of the vehicle according to the multi-dimensional data, and obtain the predicted required power according to the current throttle opening change rate; Establish an optimization objective function consisting of fuel economy, battery health, and power responsiveness. Use the model predictive control algorithm to solve the optimization objective function in the rolling time domain according to the predicted power demand of the vehicle to obtain the torque distribution result between the engine and the motor. The torque of the engine and the motor are distributed according to the torque distribution results of the engine and the motor respectively to control the vehicle.

2. The energy management method for a plug-in hybrid vehicle according to claim 1, characterized in that: In the step of establishing an optimization objective function consisting of fuel economy, battery health, and power responsiveness, and solving the optimization objective function in a rolling time domain using a model predictive control algorithm according to the predicted required power of the vehicle to obtain the torque distribution result 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 a sudden acceleration demand is detected, the power response weight is increased, allowing the engine to temporarily deviate from the high-efficiency zone to improve acceleration performance.

3. The energy management method for a plug-in hybrid vehicle according to claim 1, characterized in that: The method further comprises: If the average power demand 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 lowered to avoid motor overload under high power; If the vehicle is frequently in a low-speed creeping condition, the lower limit of the battery power balance range will be adjusted upward to reduce the number of engine start and stop times.

4. The energy management method for a plug-in hybrid vehicle according to claim 3, characterized in that: The method further comprises: According to the historical distribution of the engine's operating points, 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 operating point to concentrate in the high-efficiency area; The frequency of sudden acceleration and deceleration is counted to identify the driver type, including aggressive and conservative types. For aggressive drivers, the motor torque distribution ratio is increased and the power response time is shortened. For conservative drivers, priority is given to maintaining battery power balance and extending the pure electric range.

5. The energy management method for a plug-in hybrid vehicle according to claim 4, characterized in that: Before the step of distributing the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle, the step further includes: When the battery power is less than the power threshold and the required 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.

6. The energy management method for a plug-in hybrid vehicle according to claim 1, characterized in that: The multi-dimensional data includes vehicle speed, throttle opening, battery charge (SOC), engine speed, motor speed, slope angle, acceleration, and ambient temperature.

7. The energy management method for a plug-in hybrid vehicle according to claim 6, characterized in that: If the slope sensor for collecting the slope angle fails, the slope angle is estimated based on the historical data of vehicle speed and acceleration.

8. An energy management system for a plug-in hybrid vehicle, characterized in that: The system comprises: An acquisition module, used to acquire multi-dimensional data of the vehicle in real time during the driving process of the vehicle, determine the actual required power of the vehicle according to the multi-dimensional data, and obtain the predicted required power according to the current throttle opening change rate; The optimization module is used to establish an optimization objective function consisting of fuel economy, battery health, and power responsiveness. According to the predicted power demand of the vehicle, the optimization objective function is solved in the rolling time domain using the model predictive control algorithm to obtain the torque distribution result between the engine and the motor. The control module is used to distribute the torque of the engine and the motor respectively according to the torque distribution results of the engine and the motor to control the vehicle.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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