Joint optimization component and control parameter method for power split hybrid vehicle

Abstract

The invention discloses a component and control parameter joint optimization method for a power split hybrid electric vehicle, which establishes a component and control parameter joint optimization framework for the power split hybrid electric vehicle, and component parameter optimization and control parameter optimization are respectively carried out on the outer layer and the inner layer of the framework. Component cost and an inner-layer optimization objective function are considered at the same time for the outer-layer optimization objective function; the target function of the inner layer optimization is the fuel cost. A whole vehicle model, a control strategy model and a joint optimization algorithm are integrated in Isight software, joint optimization of component parameters and control parameters is achieved by traversing all combined component parameters, and the optimization period of the power system and the control strategy of the power split type hybrid electric vehicle is shortened. Under the condition that the dynamic property index is met, the economic benefit problem concerned by manufacturers and users together is solved by taking the economical efficiency improvement and the component cost reduction as targets, and the method has important guiding significance on the design of the power split type hybrid power system.

Description

Technical Field

[0001] The invention relates to the technical field of hybrid vehicle power transmission and control, and in particular to a method for jointly optimizing components and control parameters of a power-split hybrid vehicle. Background Art

[0002] The greatest significance of hybrid vehicles is to improve fuel economy. The matching effect of component parameters and control parameters will directly affect the fuel saving effect of hybrid vehicles. However, it is generally found in the matching design process that the two factors of component parameters and control parameters will affect each other. For example, changes in component parameters such as engine power and motor torque will affect the selection and effect of control strategy, and the adjustment of control parameters will have a reverse effect on the working state of the components and the performance of the entire system. This complex coupling relationship increases the difficulty of jointly optimizing component parameters and control parameters. At the same time, the joint optimization problem needs to take into account multiple objectives, such as power, fuel economy, cost, emission performance, etc. There are often contradictions between these objectives. Therefore, for a specific hybrid topology, establishing an optimization process that meets the optimal solution for the comprehensive performance of the system is the development trend of multi-objective optimization problems.

[0003] Existing patents, such as the invention patent with patent number CN 110667566 B, propose a method for coordinated combination optimization of hybrid vehicle matching parameters and control strategies. The invention sets the transmission ratio of the gearbox as the matching parameter to be optimized, sets the upper and lower limits of the engine torque, the battery SOC when the engine is involved, and the engine starting speed as the control strategy parameters to be optimized, integrates the vehicle model, the control strategy model and the combined optimization algorithm, and takes the comprehensive energy consumption per 100 kilometers as the optimization objective function. Although the invention realizes the coordinated automatic optimization of matching parameters and control strategies and shortens the optimization cycle of vehicles and control strategies, the matching parameters only include the transmission ratio of the gearbox, and the optimization objective function only considers the economic indicator of comprehensive energy consumption per 100 kilometers, without considering important realistic factors such as the cost of each component and the cost of fuel. Summary of the invention

[0004] The main technical problem solved by the present invention is to overcome the existing technical difficulties. A method for jointly optimizing components and control parameters of a power-split hybrid vehicle is provided. The method establishes an optimization framework for jointly optimizing component parameters and control parameters for power-split hybrid vehicles. The outer and inner layers of the optimization framework respectively perform component parameter optimization and control parameter optimization. The constraint conditions for the outer layer optimization are set as the vehicle power performance index. The objective function simultaneously considers the component cost and the inner layer optimization objective function. The constraint conditions for the inner layer optimization are set as the SOC balance at the beginning and end of the cycle condition. The objective function is the fuel cost. The vehicle model, control strategy model and joint optimization algorithm are integrated in the Isight software. The joint optimization of component parameters and control parameters is achieved by traversing all combined component parameters, shortening the cycle of power system and control strategy optimization of power-split hybrid vehicles. The goal is to improve economy and reduce component costs while meeting the power performance index. This has important guiding significance for the design of power-split hybrid systems.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] A method for jointly optimizing components and control parameters of a power-split hybrid vehicle comprises the following steps:

[0007] Step 1: Establish a vehicle model of a power-split hybrid vehicle and determine component design variables, including engine model, motor MG1 peak power, motor MG2 peak power, motor MG2 peak torque, planetary gear characteristic parameters, gearbox maximum and minimum speed ratios, gearbox gear number and final reduction ratio. The nine component design variables are discretized within their value ranges to generate a group of component parameters.

[0008] Step 2: Establish a control strategy model for a power-split hybrid vehicle, determine the control strategy design variables, including the SOC threshold SOC_EV of the motor-only working mode and the power threshold Pow_EV of the motor-only working mode, discretize the two control strategy design variables within their value ranges, and generate group b of control parameters;

[0009] Step 3: Establish a joint optimization framework for component parameters and control parameters. The outer and inner layers of the optimization framework are used to optimize component parameters and control parameters respectively. The constraint conditions for the outer layer optimization are set as the vehicle power performance index. The objective function of the outer layer optimization is designed as equation (1), and the cost functions are designed as equations (2) and (3).

[0010] J L =μ1C p +μ2C f (1)

[0011]

[0012] C f =S y α5Q s (3)

[0013] In the formula, μ1 is the component cost weight coefficient, C p is the component cost, μ2 is the fuel cost weight coefficient, C f is the fuel cost, C e , C m , C b and C g are the costs of the engine, motor, battery and gearbox respectively, S y is the vehicle mileage, Q s is the fuel consumption per 100 kilometers, α1~α5 are the price coefficients in each cost function;

[0014] The constraint condition of the inner optimization is set as the SOC balance at the beginning and end of the cycle condition, specifically as equation (4), and the objective function of the inner optimization is designed as the fuel cost C f ;

[0015] |ΔSOC|=|SOC(end)-SOC0|≤ε (4)

[0016] Where ΔSOC is the SOC difference between the beginning and end of the cycle, SOC(end) is the SOC value at the end of the cycle, SOC0 is the SOC value at the beginning of the cycle, and ε is the SOC balance limit.

[0017] In the optimized technical solution, the specific process of the joint optimization in the third step is:

[0018] S1: Start with the optimization of the outer component parameters. First, for the component parameters of group a, calculate the corresponding dynamic performance indicators of each group in turn. If the dynamic constraint conditions are met, keep the current group number m and set the mth group component parameter P m Input into the whole vehicle model of the power split hybrid vehicle, enter step S2, if the dynamic constraint condition is not met, then let the component parameter optimization objective function J L (m) is 0, go to step S4;

[0019] S2: Under the mth group of component parameters, for the bth group of control parameters, combined with the vehicle model and control strategy model of the power split hybrid vehicle, calculate the corresponding fuel cost C of each group in turn f (m,n), n represents the group number of the currently calculated control parameters, traverses all the control parameters of group b, and retains the ΔSOC and fuel cost C calculated for each group f (m,n), and obtain the minimum fuel cost C that satisfies the constraints of formula (4) f_min(m,n_opt) and the corresponding control parameters S n_opt , proceed to step S3;

[0020] S3: Calculate the component cost C under the mth set of component parameters p (m), combined with the minimum fuel cost C obtained in step S2 f_min (m,n_opt), the total cost J corresponding to the outer optimization objective function under the mth group of component parameters is calculated by formula (1) L (m), record the calculation results and go to step S4;

[0021] S4: The component parameter group number m is incremented by 1. If m is less than or equal to the number of component parameter groups a, then return to step S1 to traverse. If m is greater than the number of component parameter groups a, then output the smallest J except 0. L_min and its corresponding component parameter P m_opt and control parameter S n_opt .

[0022] Compared with the prior art, the advantages of the present invention are:

[0023] 1. The method for jointly optimizing components and control parameters of a power-split hybrid vehicle of the present invention establishes an optimization framework for jointly optimizing component parameters and control parameters, realizes the coordinated optimization of component parameters and control parameters, solves the problem that the mutual influence and restriction between the two often lead to suboptimal solutions when they are optimized separately, and gives full play to the energy-saving potential of power-split hybrid vehicles;

[0024] 2. The method for jointly optimizing components and control parameters of a power-split hybrid vehicle of the present invention proposes a cost function that considers power, economy and cost at the same time, aiming at the main contradiction between the increase in power system cost and the reduction in fuel cost of hybrid vehicles, thus solving the economic benefit problem that manufacturers and users are concerned about;

[0025] 3. The method for jointly optimizing components and control parameters of a power-split hybrid vehicle described in the present invention integrates the vehicle model, control strategy model and joint optimization algorithm on the same software platform, realizes the joint optimization of component parameters and control parameters in a relatively short time, and shortens the optimization cycle of the power system and control strategy of the power-split hybrid vehicle while improving fuel economy and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0027] Figure 1 A flowchart of a method for jointly optimizing components and control parameters of a power-split hybrid vehicle according to the present invention;

[0028] Figure 2 The present invention provides a flowchart for determining a control strategy mode in a method for jointly optimizing components and control parameters of a power-split hybrid vehicle. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0030] The present invention will be further described below by taking a power split hybrid system for a light commercial vehicle as an example and combining with the accompanying drawings.

[0031] See also Figure 1 The present invention provides a method for jointly optimizing components and control parameters of a power-split hybrid vehicle, which specifically includes the following steps:

[0032] The first step is to establish a power-split hybrid vehicle model and determine component design variables, including engine model, motor MG1 peak power, motor MG2 peak power, motor MG2 peak torque, planetary gear characteristic parameters, gearbox maximum and minimum speed ratios, gearbox gear number and final reduction ratio. The nine component design variables are discretized within their value ranges. The constraint ranges and discretized values ​​of the design variables of different components of the power-split hybrid system of a light commercial vehicle in this embodiment are shown in Table 1.

[0033] Table 1 Discretization table of components and control design variables for a power-split hybrid light commercial vehicle

[0034]

[0035] Step 2: Establish the control strategy model of the power-split hybrid vehicle, see Figure 2First, the required power Pow_req is calculated according to the working condition data. If the required power is positive, it is judged as a driving mode, otherwise it is judged as a braking mode; in the driving mode, if the required power is greater than the power threshold Pow_EV of the motor working mode alone and the SOC is greater than the SOC threshold SOC_EV of the motor working mode alone, it is judged as a pure electric EV mode; otherwise, if the required power is less than the maximum power of the engine at the current speed P_e_max, it is judged as an engine driving mode alone EO mode, and if the required power is greater than the maximum power of the engine at the current speed P_e_max, it is judged as a motor assist HEV_MA mode; in the braking mode, if the SOC is less than the SOC threshold SOC_RGB of the braking recovery mode, it is judged as a braking recovery RGB mode, otherwise it is judged as a mechanical braking MB mode; the control strategy design variables are determined by the above mode judgment process, including the SOC threshold SOC_EV of the motor working mode alone and the power threshold Pow_EV of the motor working mode alone. The two control strategy design variables are discretized within their value ranges. The constraint range and discretization values ​​are shown in Table 1.

[0036] Step 3: Establish a joint optimization framework for component parameters and control parameters. The outer and inner layers of the optimization framework are used to optimize component parameters and control parameters respectively. The constraint conditions for the outer layer optimization are set as the vehicle power performance index. The objective function of the outer layer optimization is designed as equation (1), and the cost functions are designed as equations (2) and (3).

[0037] J L =μ1C p +μ2C f (1)

[0038]

[0039] C f =S y α5Q s (3)

[0040] Where μ1 is the component cost weight coefficient, C p is the component cost, μ2 is the fuel cost weight coefficient, C f is the fuel cost, C e , C m , C b and C g are the costs of the engine, motor, battery and gearbox respectively, S y is the vehicle mileage, Q s is the fuel consumption per 100 kilometers, α1~α5 are the price coefficients in each cost function;

[0041] The constraint condition of the inner optimization is set as the SOC balance at the beginning and end of the cycle condition, specifically as equation (4), and the objective function of the inner optimization is designed as the fuel cost Cf ;

[0042] |ΔSOC|=|SOC(end)-SOC0|≤ε (4)

[0043] Where ΔSOC is the SOC difference between the beginning and end of the cycle, SOC(end) is the SOC value at the end of the cycle, SOC0 is the SOC value at the beginning of the cycle, and ε is the SOC balance limit.

[0044] In the optimized technical solution, the specific process of the joint optimization in the third step is:

[0045] S1: Start with the optimization of the outer component parameters. First, for the component parameters of group a, calculate the corresponding dynamic performance indicators of each group in turn. If the dynamic constraint conditions are met, keep the current group number m and set the mth group component parameter P m Input into the whole vehicle model of the power split hybrid vehicle, enter step S2, if the dynamic constraint condition is not met, then let the component parameter optimization objective function J L (m) is 0, go to step S4;

[0046] S2: Under the mth group of component parameters, for the bth group of control parameters, combined with the vehicle model and control strategy model of the power-split hybrid vehicle, calculate the corresponding fuel cost C of each group in turn f (m,n), n represents the group number of the currently calculated control parameters, traverses all the control parameters of group b, and retains the ΔSOC and fuel cost C calculated for each group f (m,n), and obtain the minimum fuel cost C that satisfies the constraints of formula (4) f_min (m,n_opt) and the corresponding control parameters S n_opt , proceed to step S3;

[0047] S3: Calculate the component cost C under the mth set of component parameters p (m), combined with the minimum fuel cost C obtained in step S2 f_min (m,n_opt), the total cost J corresponding to the outer optimization objective function under the mth group of component parameters is calculated by formula (1) L (m), record the calculation results and go to step S4;

[0048] S4: The component parameter group number m is incremented by 1. If m is less than or equal to the number of component parameter groups a, then return to step S1 to traverse. If m is greater than the number of component parameter groups a, then output the smallest J except 0. L_min and its corresponding component parameter P m_opt and control parameter S n_opt .

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for jointly optimizing components and control parameters of a power-split hybrid vehicle, characterized in that: The following steps are involved: Step 1: Establish a vehicle model of a power-split hybrid vehicle and determine component design variables, including engine model, motor MG1 peak power, motor MG2 peak power, motor MG2 peak torque, planetary gear characteristic parameters, gearbox maximum and minimum speed ratios, gearbox gear number and final reduction ratio. The nine component design variables are discretized within their value ranges to generate a group of component parameters. Step 2: Establish a control strategy model for a power-split hybrid vehicle, determine the control strategy design variables, including the SOC threshold SOC_EV of the motor-only working mode and the power threshold Pow_EV of the motor-only working mode, discretize the two control strategy design variables within their value ranges, and generate group b of control parameters; Step 3: Establish a joint optimization framework for component parameters and control parameters. The outer and inner layers of the optimization framework are used to optimize component parameters and control parameters respectively. The constraint conditions for the outer layer optimization are set as the vehicle power performance index. The objective function of the outer layer optimization is designed as equation (1), and the cost functions are designed as equations (2) and (3). J L =μ1C p +μ2C f (1) C f =S y α5Q s (3) In the formula, μ1 is the component cost weight coefficient, C p is the component cost, μ2 is the fuel cost weight coefficient, C f is the fuel cost, C e , C m , C b and C g are the costs of the engine, motor, battery and gearbox respectively, S y is the vehicle mileage, Q s is the fuel consumption per 100 kilometers, α1~α5 are the price coefficients in each cost function; The constraint condition of the inner optimization is set as the SOC balance at the beginning and end of the cycle condition, specifically as equation (4), and the objective function of the inner optimization is designed as the fuel cost C f ; |ΔSOC|=|SOC(end)-SOC0|≤ε (4) Where ΔSOC is the SOC difference between the beginning and end of the cycle, SOC(end) is the SOC value at the end of the cycle, SOC0 is the SOC value at the beginning of the cycle, and ε is the SOC balance limit.

2. A method for jointly optimizing components and control parameters of a power-split hybrid vehicle according to claim 1, characterized in that: The joint optimization process of the third step includes: S1: Start with the optimization of the outer component parameters. First, for the component parameters of group a, calculate the corresponding dynamic performance indicators of each group in turn. If the dynamic constraint conditions are met, keep the current group number m and set the mth group component parameter P m Input into the whole vehicle model of the power split hybrid vehicle, enter step S2, if the dynamic constraint condition is not met, then let the component parameter optimization objective function J L (m) is 0, go to step S4; S2: Under the mth group of component parameters, for the bth group of control parameters, combined with the vehicle model and control strategy model of the power-split hybrid vehicle, calculate the corresponding fuel cost C of each group in turn f (m,n), n represents the group number of the currently calculated control parameters, traverses all the control parameters of group b, and retains the ΔSOC and fuel cost C calculated for each group f (m,n), and obtain the minimum fuel cost C that satisfies the constraints of formula (4) f_min (m,n_opt) and the corresponding control parameters S n_opt , proceed to step S3; S3: Calculate the component cost C under the mth set of component parameters p (m), combined with the minimum fuel cost C obtained in step S2 f_min (m,n_opt), the total cost J corresponding to the outer optimization objective function under the mth group of component parameters is calculated by formula (1) L (m), record the calculation results and go to step S4; S4: The component parameter group number m is incremented by 1. If m is less than or equal to the number of component parameter groups a, then return to step S1 to traverse. If m is greater than the number of component parameter groups a, then output the smallest J except 0. L_min and its corresponding component parameter P m_opt and control parameter S n_opt .

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