Optimization Design Method for Low-Conservative Hybrid Electric Vehicles Considering Driveability

Abstract

The present invention discloses an optimized design method for a low-conservatism hybrid vehicle considering drivability, comprising the following steps: establishing a vehicle longitudinal dynamics model according to the target hybrid vehicle configuration and selected design parameters; combining the target hybrid vehicle configuration and constructing a drivability predictor based on maximizing control; solving the drivability of the hybrid vehicle under the design parameters for a given cycle condition; constructing a global optimal energy management control algorithm; solving the global optimal energy management control algorithm under reachable conditions to obtain the best fuel economy; and evaluating the feasible solutions of the design parameters according to the drivability and the best fuel economy. The present invention optimizes the parameter evaluation process of the hybrid vehicle under global optimal energy management, solves the deficiency in design parameter discrimination of the backward simulation method applying global optimal energy management by introducing a drivability predictor, and reduces the conservatism of parameter design of hybrid vehicles, especially heavy-duty hybrid vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle electronic control optimization, and particularly to an optimization design method for a low-conservatism hybrid vehicle considering drivability. Background Art

[0002] By coupling electrified power such as motors and batteries, hybrid vehicles can control the engine to operate in an efficient region, improving the fuel economy of the whole vehicle. Therefore, it is of great significance for carbon emission reduction and energy pressure alleviation. When performing the power parameter matching design of hybrid vehicles, in order to evaluate the parameters by obtaining the maximum potential of the fuel economy of the whole vehicle under given parameters, global optimal energy management is generally used to solve the fuel consumption. However, when using global optimal energy management, it is necessary to assume that the hybrid vehicle can perfectly track the working conditions, which easily leads to a high conservatism of the feasible solutions of the design parameters under emergency acceleration conditions such as urban driving. Especially for heavy-duty hybrid vehicles, it often makes the power level of the motor or battery higher, increasing the production cost of the whole vehicle. Summary of the Invention

[0003] In view of the above problems, the present invention proposes an optimization design method for a low-conservatism hybrid vehicle considering drivability, aiming to solve the problem that the design parameters under the existing global optimal energy management will increase the cost of the whole vehicle.

[0004] To solve the above technical problems, the technical solution of the present invention is as follows:

[0005] An optimization design method for a low-conservatism hybrid vehicle considering drivability, comprising the following steps:

[0006] S1, establishing a longitudinal dynamics model of the whole vehicle according to the configuration of the target hybrid vehicle and the selected design parameters;

[0007] S2, combining the configuration of the target hybrid vehicle, and constructing a drivability predictor based on maximizing control;

[0008] S3, in the drivability predictor, for a given cycle condition, solving the drivability of the hybrid vehicle under the design parameters, and generating a corresponding reachable condition;

[0009] S4, constructing a global optimal energy management control algorithm according to the configuration of the target hybrid vehicle and the longitudinal dynamics model of the whole vehicle;

[0010] S5, solving the global optimal energy management control algorithm under the reachable condition to obtain the best fuel economy;

[0011] S6, evaluating the feasible solutions of the design parameters according to the drivability and the best fuel economy.

[0012] In some embodiments, in S1, the target hybrid vehicle configuration includes series hybrid, parallel hybrid, series-parallel hybrid, power-split hybrid, or composite energy hybrid.

[0013] In some embodiments, in S1, the design parameters include the peak power, peak torque, and peak speed of the engine, motor, and generator respectively, the peak power and peak capacity of the energy buffer unit, and the gear ratio of the transmission gear and the transmission device.

[0014] In some embodiments, in S1, the vehicle longitudinal dynamics model includes the rotational speed relationship and torque balance relationship formed by the engine, the motor, the generator, the transmission gear, and the transmission device under longitudinal dynamics.

[0015] In some embodiments, in S2, the drivability estimator uses the maximum control to solve for the maximum output torque provided by the hybrid vehicle corresponding to the current vehicle speed.

[0016] In some embodiments, in S3, by detecting the required speed and required torque at each moment under the cycle condition, combining with the maximum output torque to solve for the drivability, and generating the corresponding achievable condition.

[0017] In some embodiments, in S3, at time t, when the maximum output torque is greater than or equal to the required torque, it is determined that the vehicle can complete the tracking of the current vehicle speed, and the achievable condition is generated; when the maximum output torque is less than the required torque, the achievable vehicle speed at time t is corrected according to the maximum output torque; when the time t + 1 arrives, based on the current vehicle speed of the cycle condition and the achievable vehicle speed at time t, and according to the vehicle longitudinal dynamics model, the required torque at time t + 1 is calculated.

[0018] In some embodiments, in S4, the global optimal energy management control algorithm includes dynamic programming, the minimum principle, and convex optimization.

[0019] In some embodiments, in S6, the design parameters corresponding to the drivability that satisfy Equation (1) are recognized as the feasible solutions of the hybrid design parameter set:

[0020]

[0021] where t err is the duration during which the achievable condition cannot track the given cycle condition, t tot is the total duration of the cycle condition, and ε is the given index.

[0022] The beneficial effects of the present invention are as follows: The present invention optimizes the parameter evaluation process of a hybrid vehicle under global optimal energy management. By introducing a drivability predictor, it solves the deficiency in design parameter discrimination of the backward simulation method using global optimal energy management, and reduces the conservatism of parameter design for hybrid vehicles, especially heavy-duty hybrid vehicles. Brief Description of the Drawings

[0023] Figure 1 It is a schematic flow chart of an optimized design method for a low-conservatism hybrid vehicle considering drivability disclosed in an embodiment of the present invention;

[0024] Figure 2 It is a schematic diagram of the power transmission path of a CVT parallel hybrid vehicle. Detailed Embodiment

[0025] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the content of the present invention will be further described in detail below with reference to the drawings and specific embodiments. It can be understood that the specific embodiments described herein are only for explaining the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the content.

[0026] This embodiment proposes an optimized design method for a low-conservatism hybrid vehicle considering drivability, which optimizes the parameter evaluation process of a hybrid vehicle under global optimal energy management. By introducing a drivability predictor, it solves the deficiency in design parameter discrimination of the backward simulation method using global optimal energy management, and reduces the conservatism of parameter design for hybrid vehicles, especially heavy-duty hybrid vehicles.

[0027] As Figure 1 shown, this design method includes the following steps:

[0028] S1. Establish a vehicle longitudinal dynamics model according to the target hybrid vehicle configuration and the selected design parameters.

[0029] In this embodiment, in S1, the target hybrid vehicle configuration includes series hybrid, parallel hybrid, series-parallel hybrid, power-split hybrid or composite energy hybrid.

[0030] Specifically, the design parameters include the peak power, peak torque and peak speed of the engine, motor and generator respectively, the peak power and peak capacity of the energy buffer unit, and the gear ratio of the transmission gear and the transmission device. More preferably, the vehicle longitudinal dynamics model includes the speed relationship and torque balance relationship formed by the engine, motor, generator, transmission gear and transmission device under longitudinal dynamics.

[0031] S2. Combine with the target hybrid vehicle configuration and construct a drivability estimator based on maximization control;

[0032] In this embodiment, in S2, the drivability estimator uses maximization control to solve for the maximum output torque provided by the hybrid vehicle corresponding to the current vehicle speed.

[0033] S3. In the drivability estimator, for a given driving cycle, solve for the drivability of the hybrid vehicle under the design parameters and generate the corresponding achievable driving conditions.

[0034] In this embodiment, in S3, by detecting the required speed and required torque at each moment under the driving cycle, combine with the maximum output torque to solve for the drivability and generate the corresponding achievable driving conditions.

[0035] Specifically, in S3, at time t, when the maximum output torque is greater than or equal to the required torque, it is determined that the vehicle can complete the tracking of the current vehicle speed and generate the achievable driving conditions; when the maximum output torque is less than the required torque, correct the achievable vehicle speed at time t according to the maximum output torque; when the time t + 1 arrives, based on the current vehicle speed of the driving cycle and the achievable vehicle speed at time t, and calculate the required torque at time t + 1 according to the vehicle longitudinal dynamics model.

[0036] S4. Construct a global optimal energy management control algorithm according to the target hybrid vehicle configuration and the vehicle longitudinal dynamics model.

[0037] In this embodiment, in S4, the global optimal energy management control algorithm includes dynamic programming, the minimum principle, and convex optimization.

[0038] S5. Under the achievable driving conditions, solve the global optimal energy management control algorithm to obtain the best fuel economy.

[0039] S6. Evaluate the feasible solutions of the design parameters according to the drivability and the best fuel economy.

[0040] In this embodiment, in S6, the design parameters corresponding to the drivability that satisfy Equation (1) are identified as the feasible solutions of the hybrid design parameter set:

[0041]

[0042] where t err is the duration during which the given driving cycle cannot be tracked in the achievable driving conditions, t tot is the total duration of the driving cycle, and ε is the given index.

[0043] Next, take the low-conservatism design process of a CVT parallel hybrid vehicle as a specific example and combine with Figure 2 to further illustrate.

[0044] First, given any design parameters, the longitudinal dynamic model of a CVT parallel hybrid vehicle can be expressed as:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Wherein, T e , T m , and T d are the engine torque, the motor torque, and the required torques equivalent to the input of the CVT and the input of the main reducer, respectively; ω e , ω m , and ω d are the engine speed, the motor speed, and the speeds of the input of the CVT and the input of the main reducer, respectively; i f and i c are the speed ratios of the main reducer and the CVT, respectively, r is the wheel radius; ρ is the air density, C d is the air resistance coefficient, A is the frontal area, v is the vehicle speed, θ is the road gradient, g is the acceleration due to gravity, m is the vehicle mass, η f and η c are the efficiencies of the main reducer and the CVT, respectively.

[0051] Based on the maximum control, the drivability estimator of the CVT parallel hybrid can be expressed as:

[0052]

[0053] In the formula, is the maximum torque that the current vehicle can provide, T emax is the maximum torque that the engine can provide, is the maximum torque that the motor can provide, P bmax is the maximum output power of the battery;

[0054] Then the longitudinal acceleration of the vehicle at the current moment can be corrected as:

[0055]

[0056] From Equation (8), the corrected value of the vehicle speed at time t + 1 can be obtained:

[0057]

[0058] Solve the entire operating condition using Equations (7) to (9), and the reachable operating condition of the CVT parallel hybrid vehicle under the given design parameters can be obtained.

[0059] Under the reachable operating condition Solve the global optimal energy management of the CVT hybrid vehicle, such as solving the fuel economy problem shown in Equations (10-a) - (10-j):

[0060]

[0061] subject to: (2) - (6)

[0062]

[0063]

[0064] x(t + ) = x(t) + u x (t) (10-e)

[0065] x(t) ∈ {0, 1} (10-f)

[0066] u x (t) ∈ {-1, 0, 1} (10-g)

[0067] E bmin ≤ E b (t) ≤ E bmax (10-h)

[0068] E b (t) = E bf (10-j)

[0069] Among them, J is the objective function, i.e., fuel consumption, t0 and t f are the start and end times, P b is the battery power, E b is the remaining energy of the battery, E bf is the final state value that the battery needs to reach, E bmin and E bmax are the upper and lower limits of the state of charge, x is the on / off state of the engine, u x is the on / off control action of the engine, t + represents the next sampling time at time t, B b is the loss of the battery, μ is the penalty coefficient for engine switch switching, is the fuel consumption rate;

[0070] Finally, evaluate the feasibility of the design parameters according to Equation (1) and the value of J.

[0071] In the above solution, by constructing a drivability predictor based on maximizing control, the vehicle drivability corresponding to the design parameters is evaluated in advance, considering the low-conservative hybrid vehicle optimization design method for drivability, and the actual achievable operating conditions corresponding to the design parameters are generated. When the drivability meets the performance requirements, the global optimization energy management is applied to evaluate the vehicle fuel consumption under the actual achievable operating conditions, so as to obtain a low-conservative design solution set.

[0072] The above embodiments are only used to illustrate the technical concept and characteristics of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it should not be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A low-conservatism hybrid vehicle optimization design method considering drivability, characterized in that It includes the following steps: S1. Establish a vehicle longitudinal dynamics model according to the target hybrid vehicle configuration and the selected design parameters; the design parameters include the peak power, peak torque and peak speed of the engine, motor and generator respectively, the peak power and peak capacity of the energy buffer unit, and the gear ratio of the transmission gear and the transmission device; S2. Combine the target hybrid vehicle configuration and construct a drivability predictor based on the maximization control; in S2, the drivability predictor uses the maximization control to solve the maximum output torque provided by the hybrid vehicle corresponding to the current vehicle speed; S3. In the drivability predictor, for a given cycle condition, solve the drivability of the hybrid vehicle under the design parameters and generate the corresponding reachable condition; In S3, by detecting the required speed and required torque at each moment under the cycle condition, combine the maximum output torque to solve the drivability and generate the corresponding reachable condition; S4. Construct a global optimal energy management control algorithm according to the target hybrid vehicle configuration and the vehicle longitudinal dynamics model; S5. Solve the global optimal energy management control algorithm under the reachable condition to obtain the best fuel economy; S6. Evaluate the feasible solutions of the design parameters according to the drivability and the best fuel economy; 2. The low-conservatism hybrid vehicle optimization design method considering drivability according to claim 1, wherein In S1, the target hybrid vehicle configuration includes series hybrid, parallel hybrid, series-parallel hybrid, power-split hybrid or composite energy hybrid; 3. The low-conservatism hybrid vehicle optimization design method considering drivability according to claim 1, characterized in that, In S1, the vehicle longitudinal dynamics model includes the speed relationship and torque balance relationship formed by the engine, the motor, the generator, the transmission gear and the transmission device under longitudinal dynamics; 4. The low-conservative hybrid vehicle optimization design method considering drivability as described in claim 1, characterized in that, In S3, at time t, when the maximum output torque is greater than or equal to the required torque, it is determined that the vehicle can complete the tracking of the current vehicle speed and generate the reachable condition; When the maximum output torque is less than the required torque, correct the reachable vehicle speed at time t according to the maximum output torque; when the time t+1 arrives, calculate the required torque at time t+1 from the current vehicle speed of the cycle condition and the reachable vehicle speed at time t according to the vehicle longitudinal dynamics model; 5. The low-conservatism hybrid vehicle optimization design method considering drivability according to claim 1, characterized in that In S4, the global optimal energy management control algorithm includes dynamic programming, minimum principle and convex optimization; 6. The low-conservatism hybrid vehicle optimization design method considering drivability according to claim 1, characterized in that, In S6, the design parameters corresponding to the drivability satisfying Equation (1) are determined as the feasible solutions of the hybrid design parameter set: (1) Wherein, is the duration that cannot track the given cycle condition in the reachable working condition, is the total duration of the cycle condition, is the given index.

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