Multi-cost energy management strategy construction method for hybrid vehicle

Abstract

The invention discloses a hybrid vehicle multi-cost energy management strategy construction method in the field of new energy intelligent utilization, and the method is composed of a driver module, a hierarchical cost function module, an ECMS adjustment module, a hybrid power source module and an SOC prediction module. Driving comfort, fuel economy and mode switching cost are coordinated and considered through a hierarchical cost function, discomfort loss caused by transition of a speed chain in the driving process is optimized through the driving comfort cost, and a smoother vehicle speed transition sequence is formed; the ECMS basic principle is applied to the fuel economy cost, the equivalent fuel consumption value is optimized and managed according to the double-energy-source output power obtained in real time, the mode switching cost converts the energy loss during switching of different gears into a mode switching cost model, global energy management better conforms to the actual road operation condition, and the energy efficiency is improved. And the optimal total cost problem is solved through a PMP principle, so that the driving comfort and the fuel economy of the hybrid electric vehicle are effectively improved.

Description

Technical Field

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

[0002] New energy, intelligent and other technologies have brought great changes to the power system and control of automobiles. New energy vehicles have been regarded as an important measure to achieve energy transformation and alleviate the energy crisis. Among the current pure electric vehicles, hybrid vehicles and fuel cell vehicles, fuel cell vehicles use hydrogen instead of gasoline to generate electricity and drive motors, and are regarded as the main power system of future vehicles, but the cost is relatively high. The technology level of hybrid vehicles is the most mature, and they can meet the requirements of driving range, energy saving and emission reduction. The modular power system of current hybrid vehicles includes power system and control system. Since the hybrid system is equipped with a variety of power equipment and also includes transmission devices such as clutch and transmission, the design space of the physical system is large, and the complexity of the control system is also increased. Generally, the optimization design problem of hybrid vehicles can be divided into three aspects, namely, the selection optimization of topological structure, the matching of powertrain parameters and the optimization design of energy management controller. At present, most hybrid energy management designs control strategies and optimizes with fuel economy as the target problem. For example, the document with patent publication number CN 115214606 proposes an equivalent factor MAP diagram based on trip completion and state of charge information, and finds the optimal equivalent factor in real time according to the pre-set MAP diagram, but this method does not reasonably design the adaptive adjustment scheme in ECMS (equivalent fuel consumption minimum), so it relies too much on the effectiveness of the fixed equivalent factor table of the lookup table mapping. The document with patent publication number CN 115140059 proposes a proximal strategy optimization energy management method considering multi-objective optimization, and takes fuel economy, SOC (remaining battery power) state and temperature as optimization targets for optimal strategy design, but does not consider the comfort, gear shifting frustration and driving mode switching problems that occur in driving conditions. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned shortcomings of the prior art 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 the driver's driving comfort while ensuring the fuel economy of the hybrid vehicle 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 comprises the following steps:

[0005] Step 1): The hybrid power source module of the hybrid vehicle is composed of an engine module, a motor module and a 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 allocated 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 decline 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 command 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 driving instruction sign of the car, the current actual 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 allocation 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 a 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 according to the input, and generates a speed sequence within the prediction horizon k=1, 2, ..., n, where n is the number of sequences, which 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 prediction range is determined jointly, if and only if k = 1 V 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 command sign and the predicted state of charge SOC respectively fore The weighting factors for the deviations are a1=0.9 and a2=0.1;

[0012] The fuel economy cost module is based on 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, which 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 takes into account driving comfort, fuel economy and mode switching cost through the constructed hierarchical cost function, wherein the driving comfort cost optimizes the discomfort loss caused by the transition of the speed chain during driving to form 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 runs 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 ignored in this field into the cost function, so that the global energy management is more in line with the actual road operation 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 stratified and 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 needs to use the input and output data of the adaptive energy management system such as vehicle speed, torque, position, etc. to realize the prediction and planning of future SOC and optimal torque distribution. No other hardware equipment needs to be added, the cost is low, and it is easy to implement in engineering. The optimal total cost problem is solved by the PMP principle. This method is suitable for multi-objective optimization problems and is quite mature, and the torque distribution is more reasonable. The EMS in the present invention has high prediction accuracy and strong adaptability for the driving speed information of hybrid vehicles, which further improves the reliability and overall economy 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 is a structural block diagram of a multi-cost energy management strategy for a 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 Schematic diagram of the structure 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 driver module 1, the hierarchical cost function module 2, the ECMS adjustment module 3, and the SOC prediction module 5 together constitute a multi-cost energy management strategy for a hybrid vehicle, and implement a multi-cost energy management strategy control for a hybrid power source module 4 of the hybrid vehicle. The output end of the hybrid power source module 4 is respectively connected to the driver module 1, the hierarchical cost function module 2, the ECMS adjustment module 3, and the SOC prediction module 5, the driver module 1, the hierarchical cost function module 2, and the ECMS adjustment module 3 are connected in sequence, and the output end of the SOC prediction module 5 is respectively connected to the hierarchical cost function module 2 and the ECMS adjustment module 3.

[0027] Combination 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 allocated 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 , from 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 based on 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 The actual torque T of the motor 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] In the formula, 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 capacitor.

[0038] The hybrid power source module 4 converts the actual torque T actual and actual state of charge SOC actual The SOC prediction module 5 is inputted, and the SOC prediction module 5 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] In the formula, 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 The 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 They are input into the hierarchical cost function module 2 and the ECMS adjustment module 3 respectively. The SOC prediction module 5 performs comprehensive prediction planning of the future SOC based on information such as road conditions, not only considering the consumption of fuel and battery, but also combining the speed change frequency on the road section, mode switching, etc., and finally selects a comprehensive SOC prediction 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 command module 13. The output of the vehicle speed controller 11 is connected to the driving command 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 the 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 * A PID controller is provided in the vehicle speed controller 11. Based on the general PID control principle, the vehicle speed error V * Approaching 0, the corresponding output of the regulation process is the PID signal value μ containing acceleration or braking information, and 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 of 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 the motor at 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 actualThe driving instruction module 13 has a built-in engine efficiency map. According to the efficiency characteristics under different speeds and loads in the engine efficiency map, a series of optimal working area tables of throttle opening and vehicle speed combinations are designed in advance to ensure that the engine can operate in a higher efficiency area, thereby improving fuel economy and reducing emissions. Based on the mapping relationship of the optimal working area table, the driving instruction module 13 calculates the PID signal value μ signal and the actual vehicle speed V actual As input, the throttle opening that matches the current demand is found, which is the hybrid vehicle driving signal sign.

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

[0051] The inputs of the driving comfort cost module 21 are the driving instruction sign of the vehicle, the current actual vehicle speed V actual And predict the state of charge SOC fore The output is the driving comfort cost m1. The driving comfort cost module 21 determines the acceleration and deceleration of the hybrid vehicle after the current vehicle speed according to the input, generates a speed sequence within the prediction horizon k=1, 2, ..., n, where n is the number of sequences, and determines the vehicle comfort cost m1 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] Where m1 is the comfort cost output by the driving comfort cost module 21, k is the simulation step length within the cost prediction horizon, and V p (k) represents the actual vehicle speed V actual, driving command sign, predicted state of charge SOC fore The prediction speed within the prediction range is determined jointly, if and only if k = 1 V p (k) = V actual , a(k) is the driving instruction sign and the predicted state of charge SOC fore The acceleration determined by the two systems is a1, a2, respectively, the driving command sign and the predicted state of charge SOC. fore The weight factors of the deviations 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 of time is obtained by the integral 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 model of mode switching cost. Its purpose is to add the previously ignored mode switching process into the cost function, which is more consistent with the actual situation and 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] 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] In the formula, 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 0.6 to 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, and 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 converts the input 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:

[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 is the SOC reference value, which can be solved offline by the DP algorithm (Dynamic Programming algorithm). ΔSOC is the predicted state of charge SOC fore 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. If it does not exceed the constraint, output 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 difference is the value.

[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 driving cost function of the hybrid vehicle according to 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 by 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] In the formula, 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 PMP 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] Combination Figure 1 , the engine torque distribution value T e And the motor torque distribution value T m The input is input into the hybrid power source module 4, thereby forming a multi-cost energy management strategy for hybrid vehicles, which effectively improves the poor driving comfort of the hybrid power source of the hybrid vehicle and the problems of mode switching frustration. 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 the required vehicle speed V req After being adjusted by the vehicle speed controller 11, the power controller module 12 and the driving command module 13, the vehicle driving command sign, the power P corresponding to the engine and the motor are output. e , P m When the signal enters the hierarchical cost function module 2, combined with the actual vehicle speed V actual and predict the state of charge SOC fore , construct the total hierarchical cost including three sub-cost functions. In ECMS adjustment module 3, according to the principle of minimum equivalent fuel consumption, combined with the input total cost m total , actual vehicle speed V actual And predict the state of charge SOC fore The torque distribution at the next moment is carried out, and the cost function is iteratively solved. Under the premise of ensuring basic driving performance and fuel economy, driving comfort is improved, and the mode switching and shifting frequency are reduced. Therefore, efficient control of the energy management system of hybrid and electric vehicle systems can be achieved.

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 allocated 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 ; Step 2): The SOC prediction module (5) calculates the actual motor torque T according to the actual motor torque T. m1 The SOC decline 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 command 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) form a hierarchical cost function module (2); the input of the driving comfort cost module (21) is the driving instruction sign of the car, 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) 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. 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 allocation module (32) form an 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 a judgment signal X: total The judgment signal X is input into the ECMS torque allocation module (32), and the ECMS torque allocation 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) determines the acceleration and deceleration of the hybrid vehicle after the current vehicle speed according to the input, and generates a speed sequence within the prediction horizon k=1, 2, ..., n, where n is the number of sequences, which 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 prediction range is determined jointly, if and only if k = 1 V 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 command sign and the predicted state of charge SOC respectively fore The weighting factors for the deviations are a1=0.9 and a2=0.1; The fuel economy cost module (22) 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; 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 =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, which 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 motor torque 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 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 battery internal resistance value, 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 capacitor.

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, i.e., 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 controller 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 SOC reference value SOC ref The difference between the actual torque T and the actual torque T actual , determine whether the 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 PMP allocation is composed of x, which is the state function in the PMP process, which is the SOC value, u is the control function in the PMP process, which is the engine torque allocation value T at the corresponding moment. e And the motor torque distribution value T m .

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