A hybrid vehicle multi-cost energy management strategy configuration method

Abstract

The application discloses a mixed power vehicle multi-cost energy management strategy construction method in the field of intelligent new energy utilization, which is jointly constituted by a driver module, a hierarchical cost function module, an ECMS adjustment module, a mixed power source module and an SOC prediction module. The hierarchical cost function coordinates driving comfort, fuel economy and mode switching cost. The driving comfort cost optimizes the discomfort loss caused by the transition of the speed chain in the driving process, forming a more gentle vehicle speed transition sequence. The fuel economy cost applies the basic principle of ECMS, and optimizes and manages the equivalent fuel consumption value according to the real-time obtained double energy source output power. The mode switching cost converts the energy loss in the switching of different gears into a mode switching cost model, so that the global energy management is more in line with the actual road operating conditions. The optimal total cost problem is solved through the PMP principle, thereby effectively improving the driving comfort and fuel economy of the mixed power vehicle.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent utilization of new energy, relates to new energy vehicles, and specifically to a strategic structure for efficiently managing the energy of new energy hybrid vehicles. Background Art

[0002] New energy and intelligent technologies have brought about significant changes in automotive powertrains and their control. New energy vehicles are seen as a key measure for achieving energy transition and alleviating the energy crisis. Among current pure electric vehicles, hybrid vehicles, and fuel cell vehicles, fuel cell vehicles use hydrogen instead of gasoline to generate electricity and drive the electric motor, and are considered the primary powertrain for future vehicles, but they are relatively expensive. Hybrid vehicles, on the other hand, are the most technologically mature and can meet requirements for driving range, energy conservation, and emission reduction. Current hybrid vehicle modular powertrains comprise both the powertrain and the control system. Because hybrid systems incorporate multiple power devices and transmission components such as clutches and transmissions, the design space for the physical system is large, increasing the complexity of the control system. Hybrid vehicle optimization design can generally be divided into three aspects: topology selection and optimization, powertrain parameter matching, and optimal design of the energy management controller. Most current hybrid energy management systems focus on fuel economy as the objective for control strategy design and optimization. For example, patent publication number CN 115214606 proposes an equivalent factor MAP based on trip completion and state of charge information. This method finds the optimal equivalent factor in real time based on a pre-set MAP. However, this method does not rationally design an adaptive adjustment scheme in ECMS (equivalent fuel consumption minimization), and therefore relies too much on the effectiveness of the fixed equivalent factor table mapped in the lookup table. Patent publication number CN 115140059 proposes a proximal strategy optimization energy management method that considers multi-objective optimization. This method uses fuel economy, SOC (remaining battery charge), and temperature as optimization targets for optimal strategy design. However, it does not consider issues such as driving comfort, gear shifting jerks, and driving mode switching that arise during driving. Summary of the Invention

[0003] The purpose of the present invention is to address the above-mentioned shortcomings of the existing technology and propose a method for constructing a multi-cost energy management strategy for hybrid vehicles. Based on SOC prediction, it takes into account factors such as driving comfort, gear shifting, and driving mode switching to perform layered energy management, thereby improving driver driving comfort while ensuring the fuel economy of hybrid vehicles as much as possible.

[0004] The technical solution adopted by the method for constructing a multi-cost energy management strategy for a hybrid vehicle of the present invention includes the following steps:

[0005] Step 1): The hybrid power source module of the hybrid vehicle is composed of the engine module, the motor module and the battery module, and its input is the engine power P e , motor power P m , the engine torque distribution value T allocated to the engine e , the motor torque distribution value T assigned to the motor m And the battery pack voltage U connected to the battery pack batt , whose output is the actual engine speed n e , actual motor speed n m , actual state of charge SOC actual And the actual engine torque T e1 And the actual motor torque T m1 The actual torque T actual ;

[0006] Step 2): The SOC prediction module is based on the actual motor torque T m1 The SOC drop curve is generated by the current state of charge to obtain the predicted state of charge SOC fore ;

[0007] Step 3): The driver module is composed of a speed controller, a power control module and a driving command module, and its input is the actual speed V of the vehicle. actual , actual vehicle speed V actual and the required vehicle speed V req The speed error V * , and the actual torque T actual , engine speed n e and motor speed n m , whose output is the driving instruction sign and the engine power P e And the motor power P m ;

[0008] Step 4): The driving comfort cost module, the fuel economy cost module and the mode switching cost module constitute a hierarchical cost function module; the input of the driving comfort cost module is the vehicle's driving instruction sign, the current actual vehicle speed V actual And predict the state of charge SOC fore The output is the driving comfort cost m1; the input of the fuel economy cost module is the engine power P e and motor power P m , the output is the fuel economy cost m2; the mode switching cost module converts the driving instruction sign and the actual vehicle speed V actual As input, the output is the mode switching cost m3; the driving comfort cost m1, fuel economy cost m2 and mode switching cost m3 are aggregated into the total cost m according to the weights. totaland serves as the output of the hierarchical cost function module;

[0009] Step 5): The constraint condition judgment module and the ECMS torque distribution module constitute the ECMS adjustment module, whose input is the actual torque T actual , total cost m total And predict the state of charge SOC fore , the output is the engine torque distribution value T e And the motor torque distribution value T m ; The constraint condition judgment module will input the actual torque T actual and predicted state of charge SOC fore Compare with the global torque constraint and the reference value of SOC to determine whether it exceeds the constraint range and output the judgment signal X: total The judgment signal X is input into the ECMS torque distribution module, and the ECMS torque distribution module iteratively solves the total cost m total The minimum value of the engine torque distribution value T e And the motor torque distribution value T m ;

[0010] Step 6): The driver module, the hierarchical cost function module, the ECMS adjustment module, and the SOC prediction module together constitute a hybrid vehicle multi-cost energy management strategy to control the hybrid power source module.

[0011] Furthermore, the driving comfort cost module determines the acceleration and deceleration of the hybrid vehicle after the current vehicle speed based on the input, and generates a speed sequence within the prediction horizon k = 1, 2, ..., n, where n is the number of sequences, as given by Acquisition comfort cost m1, V p (k+1)=V p (k)+a(k), a(k)=a1·sign(k)+a2·(SOC fore (k)-SOC(k)), V p (k) represents the actual vehicle speed V actual , driving command sign, predicted state of charge SOC fore The prediction speed within the jointly determined prediction range is V if and only if k = 1 p (k)=V actual , a(k) is the driving instruction sign and the predicted state of charge SOC fore The acceleration determined together, a1, a2 are the driving instruction sign and the predicted state of charge SOC fore Deviation weighting factors, a1 = 0.9 and a2 = 0.1;

[0012] The fuel economy cost module is based on the formula Get the fuel consumption cost m2, instantaneous fuel consumption Instantaneous power consumption B fuel is the fuel consumption rate, η m is the motor efficiency, σ is the oil-to-electricity conversion coefficient;

[0013] The mode switching cost module is based on the formula The mode switching cost m3 is obtained, where M is the vehicle mass, ξ is the proportional coefficient of the vehicle driving signal converted to acceleration, ranging from 0.7 to 1.0, and C m is the mechanical loss coefficient, V actual is the actual speed;

[0014] The total cost m total =a·m1+d c m2+β·m3, α is the comfort cost coefficient, ranging from 1 to 10, d c is the actual fuel price, β is the mode switching loss proportional coefficient, and is between 0.6 and 1.1.

[0015] The beneficial effects of the present invention after adopting the above technical solution are:

[0016] 1. The present invention coordinates and balances driving comfort, fuel economy and mode switching cost through the constructed hierarchical cost function. The driving comfort cost optimizes the discomfort loss caused by the transition of the speed chain during driving, forming a smoother vehicle speed transition sequence; the fuel economy cost applies the basic principle of ECMS (equivalent fuel consumption minimum), optimizes the equivalent fuel consumption value according to the real-time dual energy source output power, reasonably manages the battery charge state and ensures that the engine operates in the most efficient area; the mode switching cost converts the energy loss when switching between different gears into a mode switching cost model, and supplements the mode switching cost that has been ignored in this field into the cost function, making the global energy management more in line with actual road operating conditions.

[0017] 2. The present invention constructs a parallel hybrid stratified energy management strategy based on SOC prediction. Through reasonable prediction and planning of SOC, the original single economic cost function is hierarchically improved into three cost functions to form a total cost function. The optimization target is reconstructed and the corresponding parameters are optimized. After iterative solution, the optimization target difference obtained in the problem is lower than a certain threshold, thereby effectively improving the driving comfort and fuel economy of hybrid vehicles.

[0018] 3. The present invention only requires the input and output data of the adaptive energy management system, such as vehicle speed, torque, and position, to achieve predictive planning for future SOC and optimal torque distribution. No additional hardware is required, resulting in low cost and easy engineering implementation. The optimal total cost problem is solved using the PMP principle, a mature and adaptable method suitable for multi-objective optimization problems, which results in more reasonable torque distribution. The EMS in the present invention has high accuracy and strong adaptability in predicting the driving speed information of hybrid vehicles, further improving the reliability and overall economic efficiency of the energy management strategy.

[0019] 4. The hybrid power source in the present invention includes multiple power units. The energy management system of the power module controls the energy output of each power unit through a MAP diagram according to the allocated power, and feeds back the power unit operation information to the power flow control system in real time to achieve modularization. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a block diagram of the structure of the multi-cost energy management strategy of the hybrid vehicle of the present invention;

[0021] Figure 2 yes Figure 1 Equivalent structural block diagram of the hybrid power source module 4;

[0022] Figure 3 yes Figure 1 The structural block diagram of the driver module 1;

[0023] Figure 4 yes Figure 1 The structural block diagram of the hierarchical cost function module 2;

[0024] Figure 5 yes Figure 1 The structural block diagram of ECMS regulation module 3.

[0025] In the figure: 1. Driver module; 2. Hierarchical cost function module; 3. ECMS adjustment module; 4. Hybrid power source module; 5. SOC prediction module; 11. Vehicle speed controller; 12. Power control module; 13. Driving command module; 21. Driving comfort cost module; 22. Fuel economy cost module; 23. Mode switching cost module; 31. Constraint judgment module; 32. ECMS torque distribution module; 41. Engine module; 42. Motor module; 43. Battery pack module. DETAILED DESCRIPTION

[0026] like Figure 1As shown, the hybrid vehicle multi-cost energy management strategy is composed of a driver module 1, a layered cost function module 2, an ECMS adjustment module 3, and an SOC prediction module 5. This strategy controls the hybrid vehicle's built-in hybrid power source module 4. The output of the hybrid power source module 4 is connected to the driver module 1, the layered cost function module 2, the ECMS adjustment module 3, and the SOC prediction module 5. The driver module 1, the layered cost function module 2, and the ECMS adjustment module 3 are connected in sequence. The output of the SOC prediction module 5 is connected to the layered cost function module 2 and the ECMS adjustment module 3.

[0027] Combine Figure 2 As shown, the hybrid power source module 4 is composed of an engine module 41, a motor module 42, and a battery module 43. The engine module 41, the motor module 42, and the battery module 43 are the power sources of the entire hybrid vehicle. The engine module 41 has an engine, the motor module 42 has a motor, and the battery module 43 has a battery pack. The equivalent input of the hybrid power source 4 is the engine power P e , motor power P m , the engine torque distribution value T allocated to the engine e , the motor torque distribution value T assigned to the motor m And the battery pack voltage U connected to the battery pack batt , the equivalent output is the actual engine speed n e , actual torque T actual , actual motor speed n m And the actual state of charge SOC actual The engine module 41 outputs the actual engine speed n e and the actual engine torque T e1 The motor module 42 outputs the actual motor speed n m And the actual motor torque T m1 , the actual engine torque T e1 And the actual motor torque T m1 The actual torque T actual The battery module 43 outputs the actual state of charge SOC actual .

[0028] The engine module 41 and the motor module 42 are configured according to the input power P e and P m , torque distribution value T e and T m , combined with the working efficiency and energy loss of the actual working components, multiplied by the torque efficiency coefficient to obtain the actual engine torque T e1 and the actual motor torque T m1 , actual speed ne和 and n m It is determined by the relationship between power and actual torque, the formula is:

[0029] T e1 =T e ·η e ,

[0030] T m1 =T m ·η m ,

[0031] n e =60·P e / (2π·T e1 ),

[0032] n m =60·P m / (2π·T m1 ),

[0033] Where, T e1 , T m1 is the calculated actual torque of the engine and the actual torque of the motor, T e , T m is the input engine and motor torque distribution value, η e , η m is the torque efficiency coefficient of the engine and motor, n e , n m is the actual speed of the engine and motor, P e , P m Is the power of the engine and motor. The actual torque of the motor is T m1 The actual engine torque T e1 The actual torque T of the entire hybrid power source 4 actual .

[0034] The input of the battery module 43 is the battery voltage U batt , the actual state of charge SOC is calculated according to the formula actual , the output is the actual state of charge SOC of the vehicle battery actual :

[0035]

[0036] SOC actual =SOC0-∫I batt / Q batt ,

[0037] Where, I batt is the battery current, U batt is the battery pack voltage, P batt is the battery pack power, Rint is the internal resistance of the battery, SOC0 is the initial SOC value of the battery pack, Q batt is the battery capacitance.

[0038] The hybrid power source module 4 converts the actual torque T actual and actual state of charge SOC actual The input is sent to the SOC prediction module 5, which is based on the actual torque T actual The actual motor torque T m1 The current state of charge is calculated by the partial and current state of charge, and the SOC decline curve is generated to predict the future SOC and obtain the predicted state of charge SOC. fore As output:

[0039] SOC fore =SOC actual +(T re ·n w ·η re -T m1 ·n m ·η m ) / Q batt ,

[0040] Where, SOC fore The output predicted state of charge, SOC actual is the actual state of charge, T re is the regenerative braking torque, n w is the wheel speed, η re is the regenerative braking efficiency, T m1 Actual torque of the machine, n m is the motor speed, η m is the motor efficiency, Q batt is the battery capacitor.

[0041] The SOC prediction module 5 predicts the state of charge SOC fore These are input into the hierarchical cost function module 2 and the ECMS adjustment module 3. The SOC prediction module 5 performs a comprehensive prediction and planning of the future SOC based on information such as road conditions. This not only considers fuel and battery consumption, but also takes into account the frequency of speed changes and mode switching on the road section, ultimately selecting a comprehensive SOC prediction and planning method.

[0042] like Figure 3 As shown, the driver module 1 is composed of a vehicle speed controller 11, a power control module 12 and a driving instruction module 13. The output of the vehicle speed controller 11 is connected to the driving instruction module 13. The input of the driver module 1 is the actual vehicle speed V actual , actual vehicle speed V actual and the required vehicle speed V req The speed error V* , and the actual torque T input by the hybrid power source module 4 actual , engine speed n e and motor speed n m The output of driver module 1 is the driving command sign and engine power P e and motor power P m The purpose of a driving instruction sign is to give clear driving instructions.

[0043] The vehicle speed controller 11 receives the actual vehicle speed V actual and the required vehicle speed V req The speed error V * The vehicle speed controller 11 is provided with a PID controller. Based on the general PID control principle, the vehicle speed error V * Approaching 0, the output corresponding to the regulation process is the PID signal value μ containing acceleration or braking information. The PID signal value μ is given by the following formula:

[0044]

[0045] Where μ is the output PID signal value, K p , K i , K d They are the proportional gain, integral gain and differential gain of the PID controller respectively, and τ and t are the integral and differential operation time variables respectively.

[0046] The input to the power controller module 12 is the actual torque T actual and engine speed n e and motor speed n m The power controller module 12 calculates the power P corresponding to the engine and motor under the current vehicle speed and torque according to the following formula: e , P m :

[0047]

[0048] Where, P e is the engine power of the hybrid vehicle, T actual is the actual torque, n e is the engine speed, P m is the motor power of the hybrid vehicle, n m is the motor speed.

[0049] The input of the driving instruction module 13 is the PID signal value μ of acceleration or braking and the actual vehicle speed V actual, the driving instruction module 13 is built-in engine efficiency map, according to the efficiency characteristics of different speed and load in the engine efficiency map, a series of optimal working area table of throttle opening and vehicle speed combination is designed in advance, to ensure that the engine can run in the high efficiency area, so as to improve fuel economy and reduce emissions. Based on the mapping relationship of the optimal working area table built, the driving instruction module 13 will map the PID signal value μ signal and the actual vehicle speed V actual As input, find the throttle opening that matches the current demand, that is, the hybrid vehicle driving signal sign.

[0050] As shown in Figure 4 , the hierarchical cost function module 2 is composed of driving comfort cost module 21, fuel economy cost module 22 and mode switching cost module 23. The input of the whole hierarchical cost function module 2 is the actual vehicle speed V actual , the vehicle driving signal sign input by the driver module 1, the corresponding power P e , P m of the motor and engine and the predicted state of charge SOC fore input by the SOC prediction module 5, and the output is the total cost m total composed of the driving comfort cost m1, the fuel economy cost m2 and the mode switching cost m3 solved, and input to the ECMS adjustment module 3 for iterative solution.

[0051] Among them, the input of the driving comfort cost module 21 is the driving instruction sign of the vehicle, the current actual vehicle speed V actual and the predicted state of charge SOC fore , and the output is the driving comfort cost m1. The driving comfort cost module 21 judges the acceleration and deceleration of the hybrid vehicle after the current speed is input, generates the speed sequence in the prediction horizon k = 1, 2, …, n, n is the sequence number, and judges the comfort cost m1 of the vehicle according to the following formula:

[0052] V p (k+1) = V p (k) + a(k),

[0053] a(k) = a1·sign(k) + a2·(SOC fore (k) - SOC(k)),

[0054]

[0055] In the formula, m1 is the comfort cost output by the driving comfort cost module 21, k is the simulation step in the cost prediction horizon, V p (k) represents the actual vehicle speed V actual, driving command sign, predicted state of charge SOC fore The prediction speed within the jointly determined prediction range is V if and only if k = 1 p (k)=V actual , a(k) is the driving instruction sign and the predicted state of charge SOC fore The acceleration is determined jointly, where a1 and a2 are the driving instruction sign and the predicted state of charge SOC respectively. fore The weighting factors of the deviation are usually set to a1=0.9 and a2=0.1.

[0056] The input of the fuel economy cost module 22 is the engine power P e and motor power P m , the output is the fuel economy cost m2. Its instantaneous fuel consumption and power consumption The fuel consumption cost m2 in a fixed period is obtained by the integration module as given by the following formula:

[0057]

[0058] Where: is the instantaneous fuel consumption, P e is the engine power, B fuel is the fuel consumption rate, is the instantaneous power consumption, P m is the motor power, η m is the motor efficiency, and σ is the oil-to-electricity conversion coefficient.

[0059] The mode switching cost module 23 is different from the above two modules. It converts the vehicle driving instruction sign and the actual vehicle speed V actual As input, the output is the mode switching cost m3. This module mainly takes the energy loss when switching between different gears into consideration and establishes a mode switching cost model. Its purpose is to add the previously ignored mode switching process into the cost function, which is more consistent with the actual situation. It can be expressed as:

[0060]

[0061] Where m3 is the mode switching cost, M is the vehicle mass, ξ is the proportional coefficient of the vehicle driving signal converted to acceleration, ranging from 0.7 to 1.0, sign is the driving command, and C m is the mechanical loss coefficient, V actual is the actual speed.

[0062] The hierarchical cost function module 2 aggregates the driving comfort cost m1, fuel economy cost m2 and mode switching cost m3 into a total cost m according to a certain weight relationship. total, specifically expressed as:

[0063] m total =a·m1+d c ·m2+β·m3,

[0064] Where m total is the total cost, α is the comfort cost coefficient, which determines the proportion of comfort in the total cost and is generally between 1 and 10. m1 is the driving comfort cost, d c is the actual fuel price, m2 is the fuel economy cost, β is the mode switching loss proportional coefficient, which is generally set to 0.6~1.1, and m3 is the mode switching cost.

[0065] like Figure 5 As shown, the constraint condition judgment module 31 and the ECMS (equivalent fuel consumption minimum) torque distribution module 32 together constitute the ECMS adjustment module 3. The ECMS adjustment module 3 is the most important part of the entire hybrid vehicle energy management strategy construction method. Its input is the actual torque T actual , total cost m total And the predicted state of charge SOC obtained by tracking fore , the output is the engine torque distribution value T obtained after reasonable distribution e And the motor torque distribution value T m The ECMS adjustment module 3 sets the target cost function and obtains its minimum value, and adjusts the motor torque T when the minimum value is obtained. e1 , engine torque T m1 This is recorded and output as the ideal torque distribution between the engine and the motor.

[0066] The constraint condition judgment module 31 takes the input actual torque T actual and predicted state of charge SOC fore Compare with the global torque constraint and SOC reference value to determine whether it exceeds the constraint range and output the judgment signal X:

[0067]

[0068] ΔSOC=|SOC fore -SOC ref |,

[0069] X=g(T actual ,SOC fore ,ΔSOC),

[0070] Where, T actual_min and T actual_max are the minimum and maximum torque limits, SOC l and SOC hThey are the upper and lower limits of SOC set for the entire driving cycle, T actual is the actual torque, SOC ref It is the SOC reference value, which can be solved offline by DP algorithm (Dynamic Programming algorithm). ΔSOC is the predicted state of charge SOC fore Compared with SOC reference value SOC ref The difference between the two values, X is the output judgment signal, g(…) is the judgment function, which is used to judge the actual torque T actual , judge whether the SOC exceeds the constraint. If it does not exceed the constraint, the output is 0. Generally, if it does not exceed the constraint, then X = (0, 0, ΔSOC). If it exceeds the constraint, then X = (torque overshoot, SOC overshoot, ΔSOC). If it exceeds the constraint, the output is the difference.

[0071] Total cost m total The ECMS torque distribution module 32 is inputted with the judgment signal X. The ECMS torque distribution module 32 establishes the hybrid vehicle driving cost function based on the input variables and parameters and with reference to the Pontryagin Minimum Principle (PMP). Therefore, the entire ECMS torque distribution module 32 solves the total cost m through iteration. total The minimum value of the engine torque distribution value T e And the motor torque distribution value T m , this process can be described by the basic principle of PMP:

[0072]

[0073] Where u * Represents the engine torque distribution value T at a specific time t e And the motor torque distribution value T m The sequence of optimal solutions for distribution is composed of x, which is the state function in the PMP process, and its essence is the SOC value. u is the control function in the PMP process, and its essence is the engine torque distribution value T at the corresponding moment. e And the motor torque distribution value T m distribution of .

[0074] Combine Figure 1 , the engine torque distribution value T e And the motor torque distribution value T m The input is sent to the hybrid power source module 4, thereby forming a multi-cost energy management strategy for hybrid vehicles, effectively improving the poor driving comfort of the hybrid power source of the hybrid vehicle and the problems of mode switching. At the same time, without affecting the driving function of the hybrid vehicle, it reduces its driving cost, improves its economy, and effectively improves driving comfort. When working, the driver module 1 receives the actual vehicle speed Vactual and demand vehicle speed V req , after being adjusted by vehicle speed controller 11, power controller module 12 and travel instruction module 13, output automobile travel instruction sign, power P corresponding to engine and motor e , P m When the signal enters hierarchical cost function module 2, combined with actual vehicle speed V actual and predicted state of charge amount SOC fore , build total hierarchical cost containing three kinds of sub-cost functions. In ECMS adjustment module 3, according to the principle of minimum equivalent fuel consumption, combined with input total cost m total , actual vehicle speed V actual and predicted state of charge amount SOC fore , carry out torque distribution of next moment, iteratively solve the cost function, improve driving comfort, reduce mode switching and gear shifting frequency on the premise of guaranteeing basic driving performance and fuel economy, so as to realize efficient control of energy management system of hybrid and power automobile system.

Claims

1. A method for constructing a multi-cost energy management strategy for a hybrid vehicle, characterized in that The following steps are involved: Step 1): The hybrid power source module (4) of the hybrid vehicle is composed of an engine module (41), a motor module (42) and a battery module (43), and its input is the engine power P e , motor power P m , the engine torque distribution value T allocated to the engine e , the motor torque distribution value T assigned to the motor m And the battery pack voltage U connected to the battery pack batt , the output is the actual engine speed n e , actual motor speed n m , actual state of charge SOC actual And the actual engine torque T e1 And the actual motor torque T m1 The actual torque T actual ; Step 2): The SOC prediction module (5) calculates the actual torque T of the motor according to the actual torque T of the motor. m1 The SOC drop curve is generated by the current state of charge to obtain the predicted state of charge SOC fore ; Step 3): The driver module (1) is composed of a vehicle speed controller (11), a power control module (12) and a driving instruction module (13), and its input is the actual vehicle speed V actual , actual vehicle speed V actual and the required vehicle speed V req The speed error V * , and the actual torque T actual , engine speed n e and motor speed n m , whose output is the driving instruction sign and the engine power P e And the motor power P m ; Step 4): The driving comfort cost module (21), the fuel economy cost module (22) and the mode switching cost module (23) constitute a hierarchical cost function module (2); the input of the driving comfort cost module (21) is the vehicle's driving instruction sign, the current actual vehicle speed V actual And predict the state of charge SOC fore , the output is the driving comfort cost m1; the input of the fuel economy cost module (22) is the engine power P e and motor power P m , the output is the fuel economy cost m2; the mode switching cost module (23) takes the driving instruction sign and the actual vehicle speed V actual As input, the output is the mode switching cost m3; the driving comfort cost m1, fuel economy cost m2 and mode switching cost m3 are aggregated into the total cost m according to the weights. total and serves as the output of the hierarchical cost function module (2); Step 5): The constraint condition judgment module (31) and the ECMS torque distribution module (32) constitute the ECMS adjustment module (3), whose input is the actual torque T actual , total cost m total And predict the state of charge SOC fore , the output is the engine torque distribution value T e And the motor torque distribution value T m ; The constraint condition judgment module (31) inputs the actual torque T actual and predicted state of charge SOC fore Compare with the global torque constraint and the reference value of SOC to determine whether it exceeds the constraint range and output the judgment signal X: total The judgment signal X is input into the ECMS torque distribution module (32), and the ECMS torque distribution module (32) iteratively solves the total cost m total The minimum value of the engine torque distribution value T e And the motor torque distribution value T m ; Step 6): The driver module (1), the hierarchical cost function module (2), the ECMS adjustment module (3), and the SOC prediction module (5) together constitute a hybrid vehicle multi-cost energy management strategy to control the hybrid power source module (4).

2. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The driving comfort cost module (21) judges the acceleration and deceleration of the hybrid vehicle after the current vehicle speed based on the input, and generates a speed sequence within the prediction horizon k=1, 2, ..., n, where n is the number of sequences, and is given by the formula Acquisition comfort cost m1, V p (k+1)=V p (k)+a(k), a(k)=a1·sign(k)+a2·(SOC fore (k)-SOC(k)), V p (k) represents the actual vehicle speed V actual , driving command sign, predicted state of charge SOC fore The prediction speed within the jointly determined prediction range is V if and only if k = 1 p (k)=V actual , a(k) is the driving instruction sign and the predicted state of charge SOC fore The acceleration determined together, a1, a2 are the driving instruction sign and the predicted state of charge SOC fore Deviation weighting factors, a1 = 0.9 and a2 = 0.1; The fuel economy cost module (22) is based on the formula Get the fuel economy cost m2, instantaneous fuel consumption Instantaneous power consumption B fuel is the fuel consumption rate, η m is the motor efficiency, σ is the oil-to-electricity conversion coefficient; The mode switching cost module (23) is based on the formula The mode switching cost m3 is obtained, where M is the vehicle mass, ξ is the proportional coefficient of the vehicle driving signal converted to acceleration, ranging from 0.7 to 1.0, and C m is the mechanical loss coefficient, V actual is the actual speed; The total cost m total =α·m1+d c m2+β·m3, α is the comfort cost coefficient, ranging from 1 to 10, d c is the actual fuel price, β is the mode switching loss proportional coefficient, and is between 0.6 and 1.

1.

3. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The actual engine torque T e1 =T e ·η e , the actual torque of the motor T m1 =T m ·η m , the actual engine speed n e =60·P e / (2π·T e1 ), the motor speed n m =60·P m / (2π·T m1 ), η e , η m They are the torque efficiency coefficients of the engine and motor, P e , P m They are the power of the engine and the motor respectively.

4. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The actual state of charge SOC actual =SOC0-∫I batt / Q batt , I batt is the battery current, U batt is the battery pack voltage, P batt is the battery pack power, R int is the internal resistance of the battery, SOC0 is the initial SOC value of the battery pack, Q batt is the battery capacitor.

5. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The predicted state of charge SOC fore =SOC actual +(T re ·n w ·η re -T m1 ·n m ·η m ) / Q batt , SOC actual is the actual state of charge, T re is the regenerative braking torque, n w is the wheel speed, η re is the regenerative braking efficiency, η m is the motor efficiency, Q batt is the battery capacitance.

6. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The driving instruction module (13) has an internal engine efficiency map. According to the efficiency characteristics under different speeds and loads in the engine efficiency map, an optimal working area table of throttle opening and vehicle speed combination is designed. Based on the mapping relationship of the optimal working area table, the PID signal value μ signal and the actual vehicle speed V are searched. actual As input, find the throttle opening that matches the current demand, that is, the driving signal sign, according to the optimal working area table; the PID signal value K p , K i , K d They are the proportional gain, integral gain and differential gain of the PID controller respectively, and τ and t are the integral and differential operation time variables respectively.

7. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The input of the power control module (12) is the actual torque T actual and engine speed n e and motor speed n m , according to the formula and Calculate the power P of the engine and motor corresponding to the current vehicle speed and torque e and P m .

8. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The judgment signal X=g(T actual ,SOC fore ,ΔSOC), T actual_min ≤T actual ≤T actual_max , SOC l ≤SOC fore ≤SOC h , T actual_min and T actual_max are the minimum and maximum torque limits, SOC l and SOC h They are the upper and lower limits of SOC set for the entire driving cycle, T actual is the actual torque, SOC ref is the SOC reference value, ΔSOC is the predicted state of charge SOC fore Compared with SOC reference value SOC ref The difference between the actual torque T and the actual torque T is the difference between the actual torque T and the actual torque T. actual , judge whether SOC exceeds the constraint.

9. The method for constructing a multi-cost energy management strategy for a hybrid vehicle according to claim 1, characterized in that: The ECMS torque distribution module (32) is expressed using the PMP basic principle as follows: u * Represents the engine torque distribution value T at a specific time t e And the motor torque distribution value T m The sequence of optimal solutions for the distribution is composed of x, which is the state function in the PMP process and the SOC value, u, which is the control function in the PMP process and the engine torque distribution value T at the corresponding moment. e And the motor torque distribution value T m .

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