PHEV personalized energy management and gear decision joint optimization method

Abstract

The invention relates to the technical field of intelligent management of plug-in hybrid electric vehicles, and discloses a PHEV personalized energy management and gear decision joint optimization method. Firstly, according to power system configuration, working modes are divided, and working mode judgment logic is determined; then establishing an engine model, a driving motor and generator model and a power battery model; and establishing a personalized comprehensive performance optimal problem mathematical model in each working mode needing energy management and gear decision joint optimization, solving a personalized comprehensive performance optimal problem, and obtaining a personalized energy management and gear decision optimal solution. According to the invention, through joint optimization of energy management and gear decision, the purpose of comprehensive optimization of power performance and economic performance is achieved while individual demands of a driver are fully embodied.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of plug-in hybrid electric vehicles, and in particular to a joint optimization method of personalized energy management and gear decision of PHEV. Background Art

[0002] Plug-in hybrid electric vehicles have more mature technology than fuel cell vehicles, longer driving range than pure electric vehicles, and lower reliance on charging facilities. They are an effective way to further release the potential for energy conservation and emission reduction. Energy management and gear decisions have an important impact on vehicle performance. In addition, reflecting the driver's performance requirements in real time and accurately in energy management and gear decisions is of great significance to achieving personalized driving of PHEV (Plug-in Hybrid Electric Vehicle) while improving vehicle performance.

[0003] In "A real-time optimization energy management method for plug-in hybrid electric vehicles" (CN109760669A), the invention discloses a real-time optimization energy management method for plug-in hybrid electric vehicles. This method introduces hybrid system theory into the PHEV system, applies the optimization algorithm of control theory to the energy management method, and distributes the power output of the engine and motor in real time, which not only meets the driver's demand for the driving force of the whole vehicle, but also optimizes the efficiency of the engine, motor, battery and the whole vehicle. This method formulates the total fuel consumption at each moment as the optimization target to solve the control variable, that is, the output torque of the engine and motor, to achieve the minimum fuel consumption at each moment when the driving conditions are unknown. Its fuel-saving effect is better than the rule-based energy management method, and there is no constraint condition that the driving conditions are known that is required by the global optimization energy management method.

[0004] In "A method for optimizing shift points of a multi-speed electromechanical composite transmission system" (CN116588114A), the invention discloses a method for optimizing shift points of a multi-speed electromechanical composite transmission system. The method selects a parallel plug-in hybrid vehicle with a multi-speed electromechanical composite transmission system as the research object, takes vehicle speed, pedal opening, and operating mode as control parameters, considers the influence of the engine and motor dual power sources and the battery charge state, and based on the initial shifting law, takes the system working efficiency as the goal, and uses the global optimization method of DIRECT (DIviding RECTangles) for offline optimization of the shifting vehicle speed under different pedal openings in different modes such as pure electric mode, hybrid mode, engine single drive mode, and driving charging mode.

[0005] In the paper "A two-term energy management strategy of hybrid electric vehiclesfor power distribution and gear selection with intelligent state-of-charge reference" (Zhou, Q; Du, CQ. Journal of Energy Storage. 2021.10, Vol. 42), in order to simultaneously improve the optimization performance and real-time performance of the vehicle control strategy, a long-term and short-term energy management and gear decision joint optimization control strategy based on model predictive control is proposed. The control strategy can be divided into two parts: using a large amount of actual vehicle operating condition data, the dynamic programming method is used to calculate the optimal SOC (State of Charge) change trajectory, and the neural network training is used to obtain a neural network model with operating condition information as input and predicted SOC change trajectory as output; the predicted SOC trajectory is used as the reference trajectory in the model predictive control algorithm, and the current energy distribution results and gear decision results can be obtained in real time, which effectively overcomes the shortcomings of the dynamic programming optimization method alone that relies on the operating conditions.

[0006] In summary, most of the existing technical solutions study energy management and gear decision separately, and fail to fully consider the coupling characteristics between the two. A small number of technical solutions consider energy management and gear decision at the same time, but such technical solutions fail to reasonably reflect the driver's performance requirements, and rarely consider the optimal solution for comprehensive power and economy. Summary of the invention

[0007] In view of the above problems, the purpose of the present invention is to provide a PHEV personalized energy management and gear decision joint optimization method, through the joint optimization of energy management and gear decision, to achieve the goal of fully reflecting the personalized needs of drivers while achieving the optimal power performance and economic performance. The technical solution is as follows:

[0008] A PHEV personalized energy management and gear decision joint optimization method comprises the following steps:

[0009] Step 1: Divide the working mode according to the power system configuration; and determine the working mode judgment logic based on the power consumption-power maintenance strategy according to the power system configuration;

[0010] Step 2: Establish a power system model, including:

[0011] Establishing engine model: Based on the universal characteristic test data obtained from the engine performance test, establish the fuel consumption model and engine torque model respectively;

[0012] Establish drive motor and generator models: Based on the drive motor and generator bench test data, establish the drive motor and generator torque and efficiency models respectively;

[0013] Establish a power battery model: establish a power battery pack SOC estimation model, a power battery pack charging efficiency model, and a power battery pack discharging efficiency model;

[0014] Step 3: According to the power system model, a mathematical model for the optimal problem of personalized comprehensive performance is established in each working mode that requires joint optimization of energy management and gear decision;

[0015] Step 4: According to the mathematical model of the personalized comprehensive performance optimization problem, solve the personalized comprehensive performance optimization problem in each working mode that requires joint optimization of energy management and gear decision-making, and obtain the personalized energy management and gear decision-making optimal solution.

[0016] The beneficial effects of the present invention are:

[0017] The present invention incorporates the driver's expectations of power and economy into the personalized comprehensive performance function of plug-in hybrid vehicles in a linear weighted manner, and uses the distributed torque of the power system and the target gear of the transmission as decision variables to establish a mathematical model for the personalized comprehensive performance optimization problem; through the joint optimization of energy management and gear decision, the goal of achieving the comprehensive optimization of power performance and economic performance while fully reflecting the driver's personalized needs is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a basic flow chart of the plug-in hybrid electric vehicle personalized energy management and gear decision joint optimization method of the present invention.

[0019] Figure 2 Schematic diagram of the PHEV power system configuration.

[0020] Figure 3 This is the logic flow chart for determining the working mode.

[0021] Figure 4 It is the 3D surface diagram of the engine torque model.

[0022] Figure 5 It is the 3D surface diagram of the fuel consumption model.

[0023] Figure 6 It is the 3D surface diagram of the driving motor torque.

[0024] Figure 7 This is the efficiency curve of the drive motor.

[0025] Figure 8 This is the generator efficiency curve.

[0026] Figure 9(a) is a 3D surface diagram of the charging efficiency of the power battery pack.

[0027] Figure 9(b) is a 3D surface diagram of the power battery pack discharge efficiency.

[0028] Fig.10 Jointly optimize the main process for personalized energy management and gear decision-making in each working mode.

[0029] Fig.11 The main process is jointly optimized for personalized energy management and gear decision-making based on SA in each working mode.

[0030] Figure 12(a) shows the engine output torque when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 0.

[0031] Figure 12(b) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 0.

[0032] Figure 12(c) shows the engine output torque when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 0.5.

[0033] Figure 12(d) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 0.5.

[0034] Figure 12(e) shows the engine output torque when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 1.

[0035] Figure 12(f) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the engine direct drive mode is 1.

[0036] Figure 13(a) shows the engine output torque when the dynamics weight of the SA-PJO-EMGD control model in the hybrid drive mode is 0. Figure 13(b) shows the drive motor output torque when the dynamics weight of the SA-PJO-EMGD control model in the hybrid drive mode is 0.

[0037] Figure 13(c) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the hybrid driving mode is 0.

[0038] Figure 13(d) shows the engine output torque when the dynamic weight of the SA-PJO-EMGD control model in the hybrid driving mode is 0.5.

[0039] Figure 13(e) shows the output torque of the drive motor when the dynamic weight of the SA-PJO-EMGD control model in the hybrid drive mode is 0.5.

[0040] Figure 13(f) shows the gear position when the dynamics weight of the SA-PJO-EMGD control model in the hybrid driving mode is 0.5.

[0041] Figure 13(g) shows the engine output torque when the dynamics weight of the SA-PJO-EMGD control model in the hybrid drive mode is 1. Figure 13(h) shows the drive motor output torque when the dynamics weight of the SA-PJO-EMGD control model in the hybrid drive mode is 1.

[0042] FIG13( i ) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the hybrid driving mode is 1. ...

[0043] Figure 14(a) shows the engine output torque when the dynamics weight of the SA-PJO-EMGD control model in the driving charging mode is 0. Figure 14(b) shows the drive motor output torque when the dynamics weight of the SA-PJO-EMGD control model in the driving charging mode is 0.

[0044] Figure 14(c) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the driving charging mode is 0.

[0045] Figure 14(d) shows the engine output torque when the dynamic weight of the SA-PJO-EMGD control model in the driving charging mode is 0.5.

[0046] Figure 14(e) shows the output torque of the drive motor when the dynamic weight of the SA-PJO-EMGD control model in the driving charging mode is 0.5.

[0047] Figure 14(f) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the driving charging mode is 0.5.

[0048] Figure 14(g) shows the engine output torque when the dynamics weight of the SA-PJO-EMGD control model in the driving charging mode is 1. Figure 14(h) shows the drive motor output torque when the dynamics weight of the SA-PJO-EMGD control model in the driving charging mode is 1.

[0049] Figure 14(i) shows the gear position when the dynamic weight of the SA-PJO-EMGD control model in the driving charging mode is 1.

[0050] Fig.15 A schematic diagram of the combined optimization method for realizing personalized energy management and gear decision-making according to the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] The invention proposes a plug-in hybrid vehicle personalized energy management and gear decision joint optimization method, the basic process is as follows Figure 1 The optimization method includes four steps: dividing the working mode and determining the mode judgment logic, establishing the power system model, establishing the mathematical model of the personalized comprehensive performance optimization problem, and jointly optimizing the personalized energy management and gear decision.

[0053] This embodiment takes a series-parallel PHEV personalized energy management and gear decision joint optimization as an example to illustrate the implementation scheme of the technology of the present invention. Figure 2 As shown, the main components of the power system include the engine, 3-speed gearbox, generator M1, drive motor M2, clutch C0, clutch C1, power battery pack, gear mechanism G1, gear mechanism G2, gear mechanism G3, gear mechanism G4, gear mechanism G5, and final reducer G0. G1, G4 and G5 together form a coupling device for the output power of the gearbox and the drive motor.

[0054] The connection relationship of the main components of the power system is as follows: the output shaft of the engine is connected to the input shaft of the clutch C1, the output shaft of the clutch C1 is connected to the input shaft of the 3-speed gearbox, the output shaft of the 3-speed gearbox is connected to the input shaft of the gear mechanism G1 of the coupling device, and the output shaft of the gear mechanism G1 of the coupling device is connected to the input shaft of the main reducer G0; the output shaft of the engine is also connected to the input shaft of the gear mechanism G2, the output shaft of the gear mechanism G2 is connected to the input shaft of the gear mechanism G3, the output shaft of the gear mechanism G3 is connected to the input shaft of the clutch C0, the output shaft of the clutch C0 is connected to the input shaft of the generator M1, the generator M1 is electrically connected to the power battery pack, the power battery pack is electrically connected to the drive motor M2, the generator outputs electrical energy to charge the power battery pack, the power battery pack provides electrical energy to the drive motor M2, the output shaft of the drive motor M2 is connected to the input shaft of the gear mechanism G4 of the coupling device, the output shaft of the gear mechanism G4 of the coupling device is connected to the input shaft of the gear mechanism G5 of the coupling device, and the output shaft of the gear mechanism G5 of the coupling device is also connected to the input shaft of the main reducer G0.

[0055] The specific steps of the optimization method of the present invention are as follows:

[0056] S1 divides the working mode and determines the mode judgment logic. The specific steps are as follows:

[0057] S11 divides the working mode.

[0058] The working modes are divided according to the power system configuration. For series PHEV, the working modes are divided into pure electric mode, extended range mode, and regenerative braking mode. For parallel PHEV, the modes are divided into pure electric mode, engine direct drive mode, hybrid drive mode, driving charging mode, and regenerative braking mode. For series-parallel PHEV, the working modes are divided into pure electric mode, extended range mode, engine direct drive mode, hybrid drive mode, driving charging mode, and regenerative braking mode; for power split PHEV, the working modes are divided into pure electric mode, engine direct drive mode, power split hybrid drive mode, driving charging mode, and regenerative braking mode.

[0059] The PHEV of this embodiment adopts a series-parallel structure, and the working modes can be divided into a pure electric mode, an extended-range mode, an engine direct drive mode, a hybrid drive mode, a driving charging mode and a regenerative braking mode.

[0060] S12 determines the working mode judgment logic.

[0061] According to the power system configuration, the working mode judgment logic is determined based on the CD-CS (Charge Depletion-Charge Sustaining) strategy.

[0062] The working mode judgment logic flow chart is as follows: Figure 3 As shown, take SOC min =0.45,SOC min2 =0.46; u m2max is the vehicle speed corresponding to the maximum speed of the drive motor; u eco T is the speed threshold for switching between high and low speed conditions; ecoff The switching threshold torque is expressed as the engine shut-off curve. The main steps are described as follows:

[0063] a. Calculate the required torque T req .

[0064] When clutch C1 is disengaged:

[0065] T req =αT m2cmax (u a ) (1)

[0066] When clutch C1 is engaged:

[0067] T req =α (T ecmax (u a )+ T m2cmax (u a )) (2)

[0068] Where, T reqrepresents the required torque (N·m), α represents the accelerator pedal opening, T m2cmax Indicates the maximum torque output by the drive motor to the output shaft of the power coupling device, T ecmax Indicates the maximum torque output by the engine to the output shaft of the power coupling device. a For vehicle speed.

[0069] b. Driving / braking mode determination. When T req <0, enter regenerative braking mode, when T req ≥0, it enters the drive mode.

[0070] c. Power consumption stage mode judgment. When the battery state of charge SOC>SOC min2 When u a >u m2max When T req >T m2cmax When the battery is charged, the hybrid drive mode is entered, otherwise the pure electric mode is entered and Chr = off. Chr represents the instruction to determine whether to charge, off means not to charge, and on means to charge.

[0071] d. Power maintenance stage mode judgment. When the battery state of charge SOC ≤ SOC min2 , it enters the CS (Charge Sustaining) stage.

[0072] d1. Low speed working mode judgment. a <u eco When T req >T m2cmax When SOC>SOC min Or when Chr=off, enter the pure electric mode and set Chr=off, otherwise enter the extended range mode and set Chr=on.

[0073] d2. High-speed working mode judgment. a ≥u eco When T req >T ecmax When SOC <SOC min Or Chr=on, enter the driving charging mode and set Chr=on, otherwise when T req >T ecoff When the engine is in direct drive mode, it enters the pure electric mode.

[0074] S2 builds the power system model. The engine model, drive motor and generator model, and power battery model are built separately. The specific steps are as follows:

[0075] S21 establishes an engine model. Based on the universal characteristic test data obtained from the engine performance test, a fuel consumption model and an engine torque model are established respectively, which can be expressed as formula (1). The fuel consumption model inputs are speed and torque, and the output is the engine fuel consumption rate. The torque model inputs are speed and accelerator pedal opening, and the output is the engine torque.

[0076]

[0077] Where b e Indicates the engine fuel consumption rate (g / kW·h); f be represents the fuel consumption rate mapping function; n e Indicates engine speed (r / min); T e Indicates engine output torque (N·m); f te represents the engine torque mapping function; α represents the accelerator pedal opening.

[0078] The three-dimensional surfaces of the engine torque model and fuel consumption model used in the PHEV are as follows: Figure 4 and Figure 5 shown.

[0079] S22 establishes the drive motor and generator models. Based on the motor bench test data, the drive motor and generator torque and efficiency models are established respectively. The input of the drive motor torque model is the drive motor speed and the accelerator pedal opening, and the output is the drive motor torque; the torque characteristics of the generator are determined by the specific control strategy. The efficiency models of the drive motor and generator take their respective torque and speed as input, and the output of the two is their respective efficiencies.

[0080] The output torque of the driving motor can be obtained by equation (4) from the external characteristic data of the motor torque obtained from the bench test. Its 3D surface is as follows: Figure 6 shown.

[0081] T m2 =α·T m2max (n m2 ) (4)

[0082] Where, T m2 Indicates the output torque of the drive motor (N·m); n m2 Indicates the speed of the driving motor (r / min); T m2max Indicates the maximum output torque (N·m) of the drive motor at the current speed.

[0083] The torque characteristics of the generator are closely related to the engine output torque and the state of the clutch C0. Only when the clutch C0 is closed and the engine outputs torque, the generator outputs torque to supply power to the power battery pack. The efficiency models of the drive motor and generator take their respective torque and speed as input, and the outputs of the two are their respective efficiencies. Based on the bench test data of the drive motor and generator, the efficiency models of the drive motor and generator can be established. The curves of the two are as follows: Figure 7 and Figure 8 shown.

[0084] S23 establishes a power battery model, including establishing a power battery pack SOC estimation model, establishing a power battery pack charging efficiency model, and establishing a power battery pack discharge efficiency model. The power battery pack SOC is estimated using the ampere-hour integration method.

[0085]

[0086] In the formula, I b Represents the output current of the power battery pack (A); R represents the internal resistance of the battery; P b Indicates the output power of the power battery pack (kW); U OCV represents the open circuit voltage of the power battery pack (V); t represents the charge and discharge time (s); SOC(t) represents the SOC at time t; SOC init Indicates the initial value of SOC; C b Indicates the total battery capacity (Ah).

[0087] Power battery pack charging efficiency:

[0088]

[0089] Power battery pack discharge efficiency:

[0090]

[0091] Where η chr Indicates the charging efficiency of the power battery pack; η dis Indicates the discharge efficiency of the power battery pack.

[0092] The charge and discharge efficiency curves of the power battery pack used in the PHEV of this embodiment are shown in FIG. 9( a ) and FIG. 9( b ).

[0093] S3 establishes a mathematical model for the optimal problem of personalized comprehensive performance. In each working mode that requires joint optimization of energy management and gear decision, establish a mathematical model for the optimal problem of personalized comprehensive performance. The specific steps are as follows:

[0094] S31 constructs a personalized comprehensive performance function. The quantized value of driving intention is used as the weight coefficient, and the personalized comprehensive performance function J is constructed using the linear weighting method:

[0095]

[0096] In the formula, ω d ,ω e are the power weight and economic weight, respectively, to reflect the driver's performance expectations; T e , G are engine torque and gear position respectively; L′ d , L′ e They are respectively the normalized dynamic sub-objective function and economic sub-objective function.

[0097] The normalization method uses the range method, and the calculation formula is as follows:

[0098]

[0099] In the formula, x' represents the normalized data; x represents the original data; x min 、x max Represent the minimum and maximum values ​​of the sample data respectively.

[0100] The power sub-objective function is the inverse of the vehicle backup power. Since the gearbox is in neutral in the PHEV pure electric mode and extended range mode in this embodiment, only the power sub-objective function in the three modes of engine direct drive mode, hybrid mode, and driving charging mode is considered.

[0101] Engine direct drive mode or driving charging mode:

[0102]

[0103] Where D a Indicates the vehicle's backup power (kW); D e Indicates engine reserve power (kW); P emax Indicates the maximum output power (kW) of the engine at the current speed; P e Indicates engine output power (kW); L d represents the dynamic objective function.

[0104] Hybrid drive mode:

[0105]

[0106] Where D m2 Indicates the backup power of the drive motor (kW); P bmax Indicates the maximum output power of the power battery pack (kW); P m2max Indicates the maximum output power (kW) of the drive motor at the current speed; P m2 Indicates the output power of the drive motor (kW).

[0107] The economic objective function is taken as the comprehensive equivalent hourly fuel consumption of the whole vehicle. The calculation formula is as follows:

[0108] L e =Q e +Q b (13)

[0109] Where, L e Indicates the comprehensive equivalent hourly fuel consumption of the vehicle (L / h); Q e Indicates the hourly fuel consumption of the engine (L / h); Q b Indicates the equivalent hourly fuel consumption of the power battery pack (L / h).

[0110] Q e The calculation formula is as follows:

[0111]

[0112] Where P e represents the engine output power; ρ represents the fuel density; g represents the acceleration due to gravity.

[0113] Q b The calculation formula is as follows:

[0114]

[0115] Where c represents the oil-to-electricity equivalent conversion factor.

[0116] S32 establishes constraint conditions. According to the corresponding working mode, the constraint conditions of the optimization problem are established, including the maximum and minimum range constraints of the torque of the engine, the generator, and the drive motor, and the gear range constraints of the transmission.

[0117] The constraints that may be involved in each PHEV mode of this embodiment are as follows:

[0118]

[0119] Where, T emin and T emax Indicates the minimum and maximum output torque of the engine at the current speed; T m2min and T m2max They represent the minimum and maximum output torque of the drive motor at the current speed; T m1min and T m1max Respectively represent the minimum and maximum output torque of the generator at the current speed; G min and G maxRespectively represent the minimum and maximum values ​​of the gear. g1 and g2 respectively represent that the engine output torque is not less than the minimum output torque of the engine at the current speed and not greater than the maximum output torque of the engine at the current speed; g3 and g4 respectively represent that the drive motor output torque is not less than the minimum output torque of the drive motor at the current speed and not greater than the maximum output torque of the drive motor at the current speed; g5 and g6 respectively represent that the generator output torque is not less than the minimum output torque of the generator at the current speed and not greater than the maximum output torque of the generator at the current speed; g7 and g8 respectively represent that the gear is not less than the minimum gear value and not greater than the maximum gear value.

[0120] Specifically, the constraints in each PHEV mode of this embodiment are:

[0121] Engine direct drive mode:

[0122]

[0123] Hybrid drive mode:

[0124]

[0125] Driving charging mode:

[0126]

[0127] S33 establishes a mathematical model for the optimal personalized comprehensive performance problem with the goal of minimizing the personalized comprehensive performance function under the established constraints.

[0128] The mathematical models for the optimal personalized comprehensive performance of the PHEV in each mode of this embodiment are:

[0129] Engine direct drive mode:

[0130]

[0131] Hybrid drive mode:

[0132]

[0133] Driving charging mode:

[0134]

[0135] S4 personalized energy management and gear decision joint optimization. In each working mode that requires energy management and gear decision joint optimization, solve the personalized comprehensive performance optimization problem and obtain the personalized energy management and gear decision optimal solution. The main optimization process in each working mode is as follows: Fig.10 shown.

[0136] For the PHEV of this embodiment, there are three modes: engine direct drive, hybrid drive and driving charging. The main optimization process of each working mode is as follows: Fig.11 shown.

[0137] The specific steps are as follows:

[0138] S41 calculates the maximum and minimum values ​​of the sub-objective functions. The maximum and minimum values ​​of the dynamic sub-objective function and the maximum and minimum values ​​of the economic sub-objective function that meet the constraints of the personalized comprehensive performance optimization problem are calculated respectively.

[0139] S42 Initialize the parameters. Divide the dynamic weight into N w The engine output torque is divided into N t The accelerator pedal opening is divided into N a The vehicle speed is divided into N v Assume the number of gearbox gears is N g .

[0140] Let N w =11,N t =108,N a =20, N v =36, N g =3.

[0141] S43 solves the optimization problem. For different power and economy weight combinations, at different accelerator pedal openings and vehicle speeds, the required torque T is calculated according to the required torque calculation formula under the corresponding working mode. req , call the optimization algorithm to solve the optimal problem of its personalized comprehensive performance, and obtain the optimal solution for personalized energy management and gear decision-making.

[0142] Given the working mode, power and economy weight combination, accelerator pedal opening and vehicle speed, the specific optimization steps are as follows:

[0143] S431 Calculate the required torque T req When clutch C1 is disengaged, the required torque is calculated according to formula (1); when clutch C1 is engaged, the required torque is calculated according to formula (2). The required torque is resolved into the torque at the output end of the power coupling device, and a relationship model between the output torque of each power device and the required torque is established.

[0144] Engine direct drive mode:

[0145]

[0146] Hybrid drive mode:

[0147]

[0148] Driving charging mode:

[0149]

[0150] In the formula, i g (n) represents the transmission ratio of gearbox n; i G1 Indicates the speed ratio of the gear mechanism G1 at the output shaft of the gearbox; η T Indicates the transmission efficiency of the gearbox; η G1 Indicates the transmission efficiency of the gear mechanism G1; T m1 Represents the generator output torque; i m2 represents the total speed ratio of the gear mechanisms G4 and G5 at the output shaft of the drive motor; η G4 , η G5 Respectively represent the transmission efficiency of gear mechanisms G4 and G5; i m1 represents the total speed ratio of the gear mechanisms G2 and G3; η G2 , η G3 Respectively represent the transmission efficiency of gear mechanisms G2 and G3.

[0151] S432 calls the SA algorithm to solve the problem of optimal personalized comprehensive performance. The steps are as follows:

[0152] S4321 sets the initial temperature to T0 = 1000°C and randomly generates the engine output torque T according to formula (26). e and the solution S(T e ,G) initial value. Calculate the objective function J(T e ,G) as the starting point of iteration.

[0153]

[0154] Where randi is a function that generates uniformly distributed pseudo-random integers; T e The value of is an even number between 0 and 216; the value of G is 1, 2 or 3.

[0155] S4322 achieves the purpose of cooling by executing the temperature decay function. The specific cooling steps are as follows:

[0156] S1) Set the temperature iteration number to k and the temperature iteration value to T tem,k , then the initial temperature iteration value is T tem,1 =T0, the lowest temperature setting is T min = 0.001℃, set the iterative perturbation times to t, and the maximum iterative perturbation times to N iter =100, and the number of iterative perturbations starts from t=1.

[0157] S2) At each temperature, the engine output torque T is randomly generated according to formula (26) e The new solution S formed by the gear position G new (T e , G), if the absolute value of the difference between the gear position of the new solution and the gear position of the current solution exceeds 1, then the engine output torque T is randomly generated again according to formula (26) e The new solution S formed by the gear position G new (T e ,G), until the absolute value of the difference between the gear position of the new solution and the gear position of the current solution does not exceed 1, and the objective function value increment ΔJ of the new solution is calculated according to formula (27).

[0158] ΔJ=J new (T e ,G)-J(T e ,G)(27)

[0159] In the formula, J new (T e ,G) is the new solution S new (T e ,G)’s objective function value.

[0160] S3) If ΔJ<0, or ΔJ≥0 and exp(-ΔJ / T tem,k )>rand[0,1], update the new solution as the current solution, that is, S(T e ,G)=S new (T e ,G); otherwise, proceed to the next step.

[0161] S4) If t<N iter , then let t=t+1, return to S2), continue iterating to generate a new solution; otherwise, execute the next step.

[0162] S5) If T tem,k >T min , then update the temperature according to the cooling attenuation function shown in formula (28), then set k=k+1 and return to S2); otherwise, end S432.

[0163] T tem,k+1 =σT tem,k (28)

[0164] Where, σ represents the temperature attenuation coefficient, which is taken as 0.98.

[0165] By repeating this cycle, the engine output torque and gear position corresponding to different working modes, dynamic weight combinations, accelerator pedal openings and vehicle speeds can be obtained, and the corresponding generator output torque and drive motor output torque can be obtained through equations (24) and (25).

[0166] The SA-PJO-EMGD control model of the PHEV in different working modes and different combinations of driver performance expectation weights was established based on the SA-PJO-EMGD method. The control model surfaces with power weights of 0, 0.5 and 1 in engine direct drive mode, hybrid power mode and driving charging mode were plotted respectively.

[0167] The SA-PJO-EMGD control model with dynamic weights of 0, 0.5, and 1 in the engine direct drive mode is as follows: Figure 12(a)-Figure 12(f) As shown in the figure. In the engine mode, the engine provides power and the power is output only through the gearbox. Due to space limitations, only three representative driver performance expectations with power and economy weight combinations of [0,1], [0.5,0.5], and [1,0] are given. The surface model is obtained by using the SA-PJO-EMGD method with engine output torque and gear position as output, taking different accelerator pedal openings and vehicle speeds as inputs.

[0168] The SA-PJO-EMGD control model with dynamic weights of 0, 0.5, and 1 in the hybrid drive mode is as follows: Figure 13(a)-Figure 13(i) As shown. In the hybrid drive mode, the engine and the drive motor provide power, and the power is output through the gearbox and the drive motor respectively. Due to space limitations, only three representative driver performance expectations with power weights and economic weights of [0,1], [0.5,0.5], and [1,0] are given. The surface model is obtained by using the SA-PJO-EMGD method with different accelerator pedal openings and vehicle speeds as inputs and engine output torque, drive motor output torque, and gear position as outputs.

[0169] The SA-PJO-EMGD control model with dynamic weights of 0, 0.5, and 1 in driving charging mode is as follows: Figure 14(a)-Figure 14(i) As shown. In the driving charging mode, the engine provides power, and the power output power is sent through the gearbox and the generator to charge the power battery pack. Due to space limitations, only three representative driver performance expectations with power weights and economic weights of [0,1], [0.5,0.5], and [1,0] are given. The surface model is obtained by using the SA-PJO-EMGD method with engine output torque, generator output torque, and gear position as outputs, taking different accelerator pedal openings and vehicle speeds as inputs.

[0170] Fig.15 The invention illustrates how to realize the functions of the invention in the actual driving process of plug-in hybrid electric vehicles. The personalized energy management and gear decision corresponding to different weight combinations in different working modes are obtained through offline optimization calculation, and stored in the personalized energy management and gear decision database in the form of a data table. The hybrid control unit (HCU) collects vehicle speed, accelerator pedal opening, brake pedal opening, and power battery pack SOC signals in real time; the driver's power expectation quantification model is used to calculate the driver's power expectation value, and the economic expectation value is calculated according to the principle that the sum of the power expectation value and the economic expectation value is 1; the working mode is determined according to the mode judgment logic; then, according to the working mode, power expectation value and economic expectation value, the personalized energy management and gear decision matching the current working mode, power expectation and economic expectation is selected from the personalized energy management and gear decision database; and based on the selected personalized energy management and gear decision, the distribution torque of the power system and the target gear of the transmission are studied according to the vehicle speed and accelerator pedal opening. Then, the existing technology is used to achieve the optimal torque distribution and gear shifting operation of the comprehensive performance that meets the driver's performance expectations through energy management and gear shifting control system.

Claims

1. A PHEV personalized energy management and gear decision joint optimization method, characterized in that: The following steps are involved: Step 1: Divide the working mode according to the power system configuration; and determine the working mode judgment logic based on the power consumption-power maintenance strategy according to the power system configuration; Step 2: Establish a power system model, including: Establishing engine model: Based on the universal characteristic test data obtained from the engine performance test, establish the fuel consumption model and engine torque model respectively; Establish drive motor and generator models: Based on the drive motor and generator bench test data, establish the drive motor and generator torque and efficiency models respectively; Establish a power battery model: establish a power battery pack SOC estimation model, a power battery pack charging efficiency model, and a power battery pack discharging efficiency model; Step 3: According to the power system model, a mathematical model for the optimal problem of personalized comprehensive performance is established in each working mode that requires joint optimization of energy management and gear decision; Step 4: According to the mathematical model of the personalized comprehensive performance optimization problem, solve the personalized comprehensive performance optimization problem in each working mode that requires joint optimization of energy management and gear decision-making, and obtain the personalized energy management and gear decision-making optimal solution.

2. The PHEV personalized energy management and gear decision joint optimization method according to claim 1 is characterized in that: The working mode division is specifically as follows: for series PHEV, the working mode is divided into pure electric mode, extended range mode, and regenerative braking mode; for parallel PHEV, the mode is divided into pure electric mode, engine direct drive mode, hybrid drive mode, driving charging mode and regenerative braking mode; for series-parallel PHEV, the working mode is divided into pure electric mode, extended range mode, engine direct drive mode, hybrid drive mode, driving charging mode and regenerative braking mode; for power-split PHEV, the working mode is divided into pure electric mode, engine direct drive mode, power-split hybrid drive mode, driving charging mode and regenerative braking mode.

3. The PHEV personalized energy management and gear decision joint optimization method according to claim 1 is characterized in that: The PHEV is a series-parallel PHEV, and its power system includes an engine, a 3-speed gearbox, a generator M1, a drive motor M2, a clutch C0, a clutch C1, a power battery pack, a gear mechanism G1, a gear mechanism G2, a gear mechanism G3, a gear mechanism G4, a gear mechanism G5 and a final reducer G0; the gear mechanisms G1, G4 and G5 together constitute a coupling device for the 3-speed gearbox and the drive motor M2 to output power; The engine output shaft is connected to the clutch C1 input shaft, the clutch C1 output shaft is connected to the 3-speed gearbox input shaft, the 3-speed gearbox output shaft is connected to the gear mechanism G1 input shaft, the gear mechanism G1 output shaft is connected to the final reducer G0 input shaft; The output shaft of the engine is simultaneously connected to the input shaft of the gear mechanism G2, the output shaft of the gear mechanism G2 is connected to the input shaft of the gear mechanism G3, the output shaft of the gear mechanism G3 is connected to the input shaft of the clutch C0, and the output shaft of the clutch C0 is connected to the input shaft of the generator M1; the generator M1 is electrically connected to the power battery pack, and the power battery pack is electrically connected to the drive motor M2, the generator outputs electrical energy to charge the power battery pack, and the power battery pack provides electrical energy to the drive motor M2; the output shaft of the drive motor M2 is connected to the input shaft of the gear mechanism G4 of the coupling device, the output shaft of the gear mechanism G4 is connected to the input shaft of the gear mechanism G5, and the output shaft of the gear mechanism G5 is also connected to the input shaft of the main reducer G0; For the series-parallel PHEV, the working mode determination logic is specifically as follows: Step a: Calculate the required torque T req : When clutch C1 is disengaged: T req =αT m2cmax (u a ) (1) When clutch C1 is engaged: T req =α (T ecmax (u a )+ T m2cmax (u a )) (2) Where, T req represents the required torque, α represents the accelerator pedal opening, T m2cmax Indicates the maximum torque output by the drive motor to the output shaft of the power coupling device, T ecmax Indicates the maximum torque output by the engine to the output shaft of the power coupling device; u a is the vehicle speed; Step b: Driving / braking mode determination: When the required torque T req <0, enter regenerative braking mode, when the required torque T req ≥0, then enter the driving mode; Step c: Power consumption stage mode determination: When the battery state of charge SOC>SOC min2 When the vehicle speed u a >u m2max When the engine enters the direct drive mode, SOC min2 The minimum value of the battery state of charge allowed during the power consumption stage; u m2max The vehicle speed corresponding to the maximum speed of the drive motor; Otherwise: When T req >T m2cmax When the battery is turned on, the hybrid drive mode is entered; otherwise, the pure electric mode is entered and Chr=off; Chr indicates the instruction to determine whether to charge, off indicates not to charge, and on indicates to charge; Step d: Power maintenance stage mode determination: When the battery state of charge SOC ≤ SOC min2 When , it enters the power maintenance stage; Step d1: Low speed operating mode determination: When u a <u eco When u eco Switching speed thresholds for high and low speed conditions; When T req >T m2cmax When the ignition is on, it enters the hybrid driving mode; Otherwise: When SOC>SOC min Or when Chr=off, enter the pure electric mode and set Chr=off, otherwise enter the extended range mode and set Chr=on; Step d2: High-speed working mode determination: When u a ≥u eco When the speed reaches 0, it enters high-speed working condition; When the required torque T req >T ecmax When the ignition is on, it enters the hybrid driving mode; Otherwise: When SOC <SOC min Or Chr=on, enter the driving charging mode and set Chr=on; otherwise when the required torque T req >T ecoff When the engine is in direct drive mode, it enters pure electric mode; otherwise, it enters pure electric mode; ecoff The switching threshold torque expressed as the engine shut-off curve; SOC min It is the minimum value of battery charge state allowed in the power maintenance stage.

4. The PHEV personalized energy management and gear decision joint optimization method according to claim 1 is characterized in that: Step 2 is as follows: Step 21: Establish a fuel consumption model and an engine torque model; The fuel consumption model inputs the speed and torque, and outputs the engine fuel consumption rate; the engine torque model inputs the speed and accelerator pedal opening, and outputs the engine torque; it is expressed as: Where b e Indicates the engine fuel consumption rate; f be represents the fuel consumption rate mapping function; n e Indicates engine speed; T e Indicates the engine output torque; f te represents the engine torque mapping function; α represents the accelerator pedal opening; Step 22: Establish the torque model and efficiency model of the driving motor and generator; The input of the drive motor torque model is the drive motor speed and the accelerator pedal opening, and the output is the drive motor torque; the torque characteristics of the generator are determined by the control strategy; the efficiency models of the drive motor and generator take their respective torque and speed as input, and the output of the two is their respective efficiencies; The output torque of the driving motor is obtained from the motor torque external characteristic data obtained from the bench test by the following formula: T m2 =α·T m2max (n m2 ) (4) Where, T m2 Indicates the output torque of the driving motor; n m2 Indicates the speed of the driving motor; T m2max Indicates the maximum output torque of the drive motor at the current speed; Step 23: Establish a power battery pack SOC estimation model, a power battery pack charging efficiency model, and a power battery pack discharging efficiency model; The ampere-hour integration method is used to estimate the SOC of the power battery pack: In the formula, I b Represents the output current of the power battery pack; R represents the internal resistance of the battery; P b Indicates the output power of the power battery pack; U OCV represents the open circuit voltage of the power battery pack; t represents the charge and discharge time; SOC(t) represents the SOC at time t; SOC init Indicates the initial value of SOC; C b Indicates the total battery capacity; Calculate the charging efficiency of the power battery pack: Calculate the power battery pack discharge efficiency: Where η chr Indicates the charging efficiency of the power battery pack; η dis Indicates the discharge efficiency of the power battery pack.

5. The PHEV personalized energy management and gear decision joint optimization method according to claim 1 is characterized in that: Step 3 is as follows: Step 31: construct a personalized comprehensive performance function; The quantified value of driving intention is used as the weight coefficient, and the linear weighted method is used to construct the personalized comprehensive performance function J: In the formula, ω d and ω e They are the power weight and economy weight, respectively, to reflect the driver's performance expectations; T e and G are engine output torque and gear position respectively; L′ d and L′ e They are respectively the normalized dynamic sub-objective function and economic sub-objective function; The dynamic performance objective function is the inverse of the vehicle's reserve power; For the engine direct drive mode or driving charging mode, the dynamic sub-objective function is: Where D a Indicates the vehicle's reserve power; D e Indicates engine reserve power; P emax Indicates the maximum output power of the engine at the current speed; P e Indicates engine output power; L d represents the dynamic sub-objective function; For the hybrid driving mode, the dynamic sub-objective function is: Where D m2 Indicates the backup power of the drive motor; P bmax Indicates the maximum output power of the power battery pack; P m2max Indicates the maximum output power of the drive motor at the current speed; P m2 Indicates the output power of the drive motor; The economic sub-objective function is taken as the comprehensive equivalent hourly fuel consumption of the whole vehicle, and the calculation formula is as follows: L e =Q e +Q b (12) Where, L e Indicates the comprehensive equivalent hourly fuel consumption of the vehicle; Q e Indicates the hourly fuel consumption of the engine; Q b Indicates the equivalent hourly fuel consumption of the power battery pack; Engine hourly fuel consumption Q e The calculation formula is as follows: In the formula, ρ represents the fuel density; g represents the acceleration due to gravity; Power battery pack equivalent hourly fuel consumption Q b The calculation formula is as follows: In the formula, c represents the oil-to-electricity equivalent conversion factor; Step 32: Establish constraints; Establishing the constraints of the optimization problem according to the corresponding working mode, including the maximum and minimum torque range constraints of the engine, generator and drive motor, and the gear range constraints of the transmission; Step 33: Establish a mathematical model for the optimal personalized comprehensive performance problem with the goal of minimizing the personalized comprehensive performance function under the established constraints.

6. The PHEV personalized energy management and gear decision joint optimization method according to claim 1 is characterized in that: Step 4 is as follows: Step 41: Calculate the maximum and minimum values ​​of the sub-objective function: Calculate the maximum and minimum values ​​of the dynamic sub-objective function and the maximum and minimum values ​​of the economic sub-objective function that meet the constraints of the personalized comprehensive performance optimization problem respectively; Step 42: Initialize parameters: Divide the dynamic weight into N w The engine output torque is divided into N t The accelerator pedal opening is divided into N a share, Divide the vehicle speed into N v Assume the number of gearbox gears is N g ; Step 43: Solve the optimization problem: For different power and economy weight combinations, at different accelerator pedal openings and vehicle speeds, the required torque T is calculated according to the required torque calculation formula under the corresponding working mode. req , call the optimization algorithm to solve the optimal problem of its personalized comprehensive performance, and obtain the optimal solution for personalized energy management and gear decision-making.

7. The PHEV personalized energy management and gear decision joint optimization method according to claim 6 is characterized in that: The PHEV is a series-parallel PHEV, and its power system includes an engine, a 3-speed gearbox, a generator M1, a drive motor M2, a clutch C0, a clutch C1, a power battery pack, a gear mechanism G1, a gear mechanism G2, a gear mechanism G3, a gear mechanism G4, a gear mechanism G5 and a final reducer G0; the gear mechanisms G1, G4 and G5 together constitute a coupling device for the 3-speed gearbox and the drive motor M2 to output power; The engine output shaft is connected to the clutch C1 input shaft, the clutch C1 output shaft is connected to the 3-speed gearbox input shaft, the 3-speed gearbox output shaft is connected to the gear mechanism G1 input shaft, the gear mechanism G1 output shaft is connected to the final reducer G0 input shaft; The output shaft of the engine is simultaneously connected to the input shaft of the gear mechanism G2, the output shaft of the gear mechanism G2 is connected to the input shaft of the gear mechanism G3, the output shaft of the gear mechanism G3 is connected to the input shaft of the clutch C0, and the output shaft of the clutch C0 is connected to the input shaft of the generator M1; the generator M1 is electrically connected to the power battery pack, and the power battery pack is electrically connected to the drive motor M2, the generator outputs electrical energy to charge the power battery pack, and the power battery pack provides electrical energy to the drive motor M2; the output shaft of the drive motor M2 is connected to the input shaft of the gear mechanism G4 of the coupling device, the output shaft of the gear mechanism G4 is connected to the input shaft of the gear mechanism G5, and the output shaft of the gear mechanism G5 is also connected to the input shaft of the main reducer G0; For the series-parallel PHEV, step 43 is specifically as follows: Step 431: Calculate the required torque T req : When the clutch C1 is disengaged, the required torque is calculated according to formula (1); when the clutch C1 is engaged, the required torque is calculated according to formula (2); the required torque is resolved into the torque at the output end of the power coupling device, and a relationship model between the output torque of each power device and the required torque is established; Engine direct drive mode: Hybrid drive mode: Driving charging mode: In the formula, i g (n) represents the transmission ratio of gearbox n; i G1 Indicates the speed ratio of the gear mechanism G1 at the output shaft of the gearbox; η T Indicates the transmission efficiency of the gearbox; η G1 Indicates the transmission efficiency of the gear mechanism G1; T m1 Represents the generator output torque; i m2 Indicates the total speed ratio of the gear mechanisms G4 and G5 at the output shaft of the drive motor; η G4 and η G5 Respectively represent the transmission efficiency of gear mechanisms G4 and G5; i m1 represents the total speed ratio of the gear mechanisms G2 and G3; η G2 and η G3 Respectively represent the transmission efficiency of gear mechanisms G2 and G3; wherein, gear mechanisms G1, G4 and G5 together constitute a coupling device for outputting power of a gearbox and a drive motor, and gear mechanisms G2 and G3 represent gear mechanisms between the output shaft of the engine and the generator; Step 432: calling the annealing algorithm to solve the personalized comprehensive performance optimization problem; Set the initial temperature to T0 and randomly generate the engine output torque T as follows: e and the solution S(T e , G) initial value: Where randi is a function that generates uniformly distributed pseudo-random integers; According to formula (9), the objective function J(T e , G) as the starting point of iteration; The purpose of cooling is achieved by executing the temperature attenuation function in a loop. Finally, the engine output torque and gear position corresponding to different working modes, dynamic weight combinations, accelerator pedal openings and vehicle speeds are obtained, and the corresponding generator output torque and drive motor output torque are obtained through equations (16) and (17).

8. The PHEV personalized energy management and gear decision joint optimization method according to claim 7 is characterized in that: The temperature decay function executed in step 432 is specifically: Step 1): Set the temperature iteration number to k and the temperature iteration value to T tem,k , then the initial temperature iteration value is T tem,1 =T0, the lowest temperature setting is T min , set the number of iterative perturbations to t and the maximum number of iterative perturbations to N iter , the number of iterative perturbations starts from t=1; Step 2): At each temperature, randomly generate the engine output torque T according to formula (18) e The new solution S formed by the gear position G new (T e , G), if the absolute value of the difference between the gear position of the new solution and the gear position of the current solution exceeds 1, then the engine output torque T is randomly generated again according to formula (18) e The new solution S formed by the gear position G new (T e , G), until the absolute value of the difference between the gear position of the new solution and the gear position of the current solution does not exceed 1, then the increment ΔJ of the objective function value of the new solution is calculated as follows: ΔJ=J new (T e ,G)-J(T e ,G) (19) In the formula, J new (T e ,G) is the new solution S new (T e , the objective function value of G); Step 3): If the increment of the objective function value ΔJ < 0, or ΔJ ≥ 0 and exp(-ΔJ / T tem,k )>rand[0,1], then update the new solution as the current solution, that is, S(T e , G)=S new (T e , G); otherwise, proceed to the next step; Step 4): If the number of iterative perturbations t<N iter , then set t = t + 1, return to step 2), continue iterating to generate a new solution; otherwise, proceed to the next step; Step 5): If the temperature iteration value T tem,k >T min , then update the temperature according to the cooling attenuation function shown in the following formula, then set k = k + 1, and return to step 2); otherwise, end the loop; T tem,k+1 =σT tem,k (20) Where σ represents the temperature attenuation coefficient.

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