Regenerative braking control method and system for pure electric vehicle

Abstract

The invention relates to the technical field of new energy vehicles, in particular to a regenerative braking control method and system for a pure electric vehicle, and the method comprises the following steps: in a safe braking area determined by an I curve, an ECE regulation curve and a preset ground adhesion coefficient, distributing braking force of a front axle and a rear axle in stages according to braking strength; establishing a fuzzy controller which takes the braking strength, the vehicle speed and the state of charge of a battery as input variables and takes the proportion coefficient of the regenerative braking force of the front axle in the total braking force of the front axle as an output variable; and optimizing the membership function of the fuzzy controller by adopting a sparrow search algorithm. According to the method, sectional type braking force distribution and fuzzy control optimized through the sparrow algorithm are combined, and the braking energy recovery efficiency is remarkably improved while the braking safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a regenerative braking control method and system for a pure electric vehicle. Background Art

[0002] Regenerative braking technology in pure electric vehicles aims to recover kinetic energy generated during braking and convert it into stored electrical energy, making it key to improving vehicle range. However, existing technologies face two major challenges in regenerative braking control strategies. Traditional braking force distribution methods, such as using a fixed ratio between the front and rear axles, struggle to maximize energy recovery efficiency while meeting the braking safety zone requirements stipulated by national ECE and other regulations. To maximize energy recovery while ensuring safety, more refined braking force distribution strategies are required. Fuzzy controllers are often used to determine the ratio of regenerative braking force to mechanical braking force within the front axle. However, the membership functions of traditional fuzzy controllers are often set based on engineers' experience, which is highly subjective and difficult to adapt to complex driving conditions (such as varying vehicle speeds, braking intensity, and battery state of charge). This results in an inability to output the optimal regenerative braking ratio under all conditions, limiting the potential for energy recovery. Therefore, designing a control strategy that ensures braking safety while maximizing energy recovery efficiency through adaptive optimization remains an urgent technical challenge in this field. Summary of the Invention

[0003] To overcome the existing problems and defects, the present invention proposes a regenerative braking control method for a pure electric vehicle, which is characterized by comprising the following steps:

[0004] Within the safe braking area determined by the I curve, the ECE regulation curve and the preset road adhesion coefficient, the braking force of the front and rear axles is distributed in stages according to the braking intensity;

[0005] A fuzzy controller is established with braking intensity, vehicle speed, and battery state of charge as input variables and the ratio of the front axle regenerative braking force to the total front axle braking force as the output variable.

[0006] The membership function of the fuzzy controller is optimized using a sparrow search algorithm.

[0007] Furthermore, the step of distributing the braking force of the front and rear axles in stages includes:

[0008] Below the first preset braking intensity threshold, the braking force is entirely borne by the front axle;

[0009] Between the first preset braking intensity threshold and the second preset braking intensity threshold, the front and rear axle braking forces are distributed along a preset tangent of the ECE regulation curve;

[0010] Between the second preset braking intensity threshold and the third preset braking intensity threshold, the rear axle braking force increases along a preset road adhesion coefficient line or a line replacing it;

[0011] Above the third preset braking intensity threshold, the regenerative braking system stops working and the braking force is completely borne by the hydraulic braking system.

[0012] Furthermore, the first preset braking intensity threshold is 0.087; the second preset braking intensity threshold is 0.52; and the third preset braking intensity threshold is 0.71.

[0013] Furthermore, the preset tangent line of the ECE regulation curve is determined as a straight line, which satisfies the condition that the area of ​​the polygon enclosed by the straight line, the ECE curve, the I curve and the ground adhesion coefficient line is the smallest.

[0014] Furthermore, the tangent point of the preset tangent line corresponds to a horizontal coordinate value of 4879.76 on the ECE regulation curve.

[0015] Furthermore, the step of optimizing the membership function by using the sparrow search algorithm includes:

[0016] Define a fitness function with the optimization goal of minimizing the change in battery state of charge;

[0017] Setting constraints, including a maximum generating torque limit of the motor, an allowable energy recovery speed range, and an upper threshold of the battery state of charge;

[0018] Execute the optimization algorithm to iteratively update the characteristic parameters of the membership function through the discoverer, follower and vigilant strategies.

[0019] Furthermore, the upper threshold of the battery state of charge is set to 0.91.

[0020] Furthermore, among the input variables of the membership function, the low state and the high state adopt the zmf function and the smf function, and the medium state adopts the gaussmf function; the output variable adopts the triangle membership function.

[0021] A regenerative braking control system for a pure electric vehicle, configured to execute the above-mentioned regenerative braking control method for a pure electric vehicle, comprising:

[0022] a braking force distribution module configured to, within a safety zone defined by the I-curve, the ECE regulation curve, and a preset road adhesion coefficient, distribute all braking force to the front axle below a first preset braking intensity threshold, distribute braking force along a preset tangent line of the ECE regulation curve between the first and second preset braking intensity thresholds, and increase rear axle braking force along a preset road adhesion coefficient line or a line alternative thereto between the second and third preset braking intensity thresholds;

[0023] A fuzzy control module, which takes braking intensity, vehicle speed, and battery state of charge as inputs and outputs the proportion of regenerative braking force on the front axle. Its internal membership function is optimized using a sparrow search algorithm to minimize the change in battery state of charge while satisfying motor and battery constraints.

[0024] The brake execution module is used to receive and execute instructions from the brake force distribution module and the fuzzy control module, and coordinate the control of the regenerative braking system and the hydraulic braking system.

[0025] Beneficial effects of the present invention:

[0026] This invention optimizes the distribution of braking force between the front and rear axles in stages, based on braking intensity, within the safety zone defined by the I-curve and ECE regulations. Specifically, it distributes braking force along an optimal tangent line under moderate braking intensity. This design not only strictly complies with braking safety regulations, but also, through optimized distribution, allows the front axle to bear more braking force, creating the prerequisites for maximizing energy recovery.

[0027] This invention uses a sparrow search algorithm to optimize the fuzzy controller's membership function offline, replacing traditional manual experience-based settings. It automatically finds and determines the optimal regenerative braking force contribution coefficient for different combinations of braking intensity, vehicle speed, and battery SOC. This adaptive optimization allows the control system to more closely approach the theoretical maximum energy recovery under various operating conditions. Simulation results show that under WLTC conditions, this invention significantly improves energy recovery efficiency compared to traditional strategies.

[0028] This invention combines a segmented braking force distribution strategy with fuzzy control optimized by intelligent algorithms to form a complete and adaptable control scheme. This scheme not only provides specific braking force distribution rules and parameters but also provides a systematic optimization method for obtaining these rules and parameters, making the entire control strategy more robust and practically applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 It is a schematic diagram of the regenerative braking control strategy structure;

[0031] Figure 2 It is a schematic diagram of the front and rear axle braking force distribution curve and safety area;

[0032] Figure 3 Optimize the membership function flow chart for the sparrow search algorithm;

[0033] Figure 4 is the optimized input-output membership function curve;

[0034] Figure 5 The comparison curve of battery SOC change under WLTC condition;

[0035] [ Figure 6 This is the comparison curve of braking energy recovery under WLTC working conditions. DETAILED DESCRIPTION

[0036] The design of brake energy recovery on front-wheel drive electric vehicles brings up two basic problems: one is how to distribute the braking force to the front wheels as much as possible to increase the recoverable energy while ensuring safety; the other is how to optimize the proportion of electric motor braking force in the distributed front wheel braking force to allow the motor to participate more in braking.

[0037] The regenerative braking of electric vehicles is affected by many factors such as motor power, energy storage device state of charge (SOC), bus maximum current, and braking intensity.

[0038] Example 1:

[0039] like Figure 1 As shown, this embodiment provides a regenerative braking control method for a pure electric vehicle, which uses a fuzzy controller to take the braking intensity, battery pack state of charge SOC and vehicle speed as inputs, and the proportion of regenerative braking force to total braking force as output. The allocation strategy is as follows: Figure 1 shown.

[0040] First, within the safety zone, the front and rear axle braking forces are distributed in stages according to the braking intensity. All braking force distribution must be carried out within the safety zone determined by the I curve, the ECE regulation curve, and the preset ground adhesion coefficient (0.7 in this embodiment).

[0041] Specifically, the I curve and the ECE regulation curve are as follows (1) and (2):

[0042] (1)

[0043] (2)

[0044] Where: Fxb1 represents the front wheel braking force (N), Fxb2 represents the rear wheel braking force (N), G represents the vehicle weight (N), hg represents the height of the vehicle's center of mass from the horizontal ground (m), L represents the wheelbase (m), and b represents the distance from the center of mass to the centerline of the rear axle (m).

[0045] During the braking process, electric vehicles must not only ensure vehicle stability but also maximize energy recovery efficiency. Figure 2 The diagram shows the improved front and rear axle braking force distribution strategy. A segmented strategy is first used to distribute front and rear braking force based on braking intensity. During low braking intensity, the front and rear braking force distribution follows the OA segment, meaning the front wheel provides all braking force, while the rear wheel braking force is zero. During medium braking intensity, to improve brake energy recovery and increase front wheel braking participation, the distribution curve follows the AF segment, a tangent to the ECE curve, with F being the tangent point. As braking intensity increases, the rear wheel braking force is appropriately increased to account for braking effectiveness, and the distribution curve follows line f instead of line FB, with B being the intersection of curves I and f. During emergency braking, the electric motor disengages the braking process, and the required braking force is fully covered by the hydraulic brake force, resulting in a close adherence to the I curve.

[0046] The AF line needs to be as close to the ECE curve as possible. At the same time, the distance between the f line and the alternative line FB should not be too large, which will reduce the distribution ratio of the front axle braking force. Therefore, the problem of finding the position of point F is transformed into finding the minimum area of ​​the polygon AFBC.

[0047] According to the division of the dotted lines drawn along the way, the area of ​​polygon AFBC can be transformed into formula (3);

[0048] (3)

[0049] Substituting equations (1) and (2) into the minimum value of the polyhedral area can be transformed into the objective function equation (4);

[0050] (4)

[0051] Where: XC and XF are the horizontal coordinates of points C and F respectively; YD is the vertical coordinate of point D, and EF is perpendicular to BD;

[0052] By using the intelligent algorithm to solve Equation (4), the optimal value of the horizontal coordinate of point F can be obtained as 4879.76, and then the optimal tangent line and FB line can be determined as shown in Equations (5) and (6).

[0053] (5)

[0054] (6)

[0055] After determining the appropriate tangent line to replace the ECE curve, the braking intensity at each segment's dividing point can be calculated, given the known AB segment, f line (adhesion coefficient 0.7), and I curve equation. The distribution curve is then divided into multiple segments based on the braking intensity z. The front and rear wheel braking force distribution is as follows:

[0056] In the OA section, when the braking intensity z is 0~0.087, it is a low-intensity braking. The required braking force is completely borne by the front wheels, and the rear wheels do not provide braking force. The front and rear wheel braking force distribution is as shown in formula (7);

[0057] (7)

[0058] In the AF segment, when the braking intensity z is 0.087 to 0.52, it is a medium-intensity braking. At this time, the front and rear wheel braking force distribution is based on the tangent AB determined in this paper. The front and rear wheel braking force distribution is as shown in formula (8);

[0059] (8)

[0060] In the FB section, when the braking intensity z is 0.52 to 0.7, it is medium-high intensity braking. At this time, the rear wheel braking force increases significantly, and the front and rear wheel braking force distribution is as shown in formula (9);

[0061] (9)

[0062] In the emergency braking stage, when the braking intensity z is greater than 0.7, it is considered high-intensity braking. At this time, the motor exits the braking process, and the required braking force is completely borne by the hydraulic braking force. The front and rear wheel braking forces are strictly distributed according to formula (10);

[0063] (10)

[0064] The above-mentioned front and rear wheel braking force distribution strategy aims to distribute as much required braking force as possible to the front wheels while taking into account braking safety and energy recovery performance.

[0065] After determining the total braking force allocated to the front axle through the above steps, the next step is to determine how much of this force can be provided by regenerative braking (the motor). To this end, this method establishes a three-input and one-output fuzzy controller. The input and output of the controller are as follows:

[0066] Input variables: The three input variables of the controller are key factors affecting the efficiency and feasibility of regenerative braking: braking intensity z, current vehicle speed v, and battery state of charge SOC.

[0067] Output variable: The controller's only output variable is the ratio of the front axle regenerative braking force to the total front axle braking force, K. This coefficient, K, has a value range of [0, 1] and directly determines the extent to which the motor participates in braking.

[0068] First, the optimization variables are designed: 24 membership function characteristic parameters for the inputs (brake intensity z, battery SOC, vehicle speed v) and outputs (front wheel regenerative braking force ratio K) (a three-input, one-output fuzzy controller has 12 membership functions, and each membership function has two characteristic parameters to adjust its shape, resulting in a total of 24 optimization variables).

[0069] With the goal of maximizing energy recovery, the fitness function can be designed around the battery SOC. Specifically, the fitness function is as shown in Equation (11):

[0070] (11)

[0071] Considering the constraints of motor torque, speed and battery SOC, the fitness function is restricted accordingly as shown in formula (12);

[0072] (12)

[0073] Among them: SOC end is the battery SOC value at the end of the driving condition; SOC0 is the initial value of the battery SOC; λ is the learning factor; T m_max The generating torque allowed by the motor is a negative value; n m_min The minimum speed at which the motor allows energy recovery; n m_max is the maximum speed of the motor; SOC max The maximum battery SOC value that allows braking energy recovery is 0.9.

[0074] In order to solve the problem that the membership function of the traditional fuzzy controller relies on manual experience, is highly subjective, and cannot achieve the optimal effect, the present invention uses the sparrow search algorithm (SSA) to perform offline optimization. Figure 3 The optimization process of this step is specifically implemented as follows:

[0075] Optimization Objectives and Constraints: The optimization objective is to maximize energy recovery, quantified by a fitness function (Equation 11) designed to minimize the change in battery SOC under specific operating conditions. Furthermore, the optimization process strictly adheres to various physical constraints (Equation 12), including the maximum torque limit for the motor, the speed range within which energy recovery is permitted, and the upper threshold for the battery state of charge (set to 0.9 in this example).

[0076] Optimization process and strategy: The algorithm uses the 24 characteristic parameters of the membership function as optimization variables. It simulates the collaborative optimization strategy of the three roles of the sparrow population, namely, the discoverer, follower, and sentinel, and performs iterative calculations in a virtual vehicle simulation environment. The specific calculation method is as follows:

[0077] The energy recovery rate output by the vehicle model is used as the optimization target. The changes in energy recovery rate under the characteristic parameters after the current iteration are compared with those after the next iteration. If the effect after the iteration is better, the population is updated to the newly discovered position, otherwise it is maintained;

[0078] (13)

[0079] Where: t is the current iteration number; Q is a random number that obeys the normal distribution; L is a 1×d matrix; iter max is the maximum number of iterations; ST is the warning value; R2 is the safety value;

[0080] (14)

[0081] Where: X P is the current optimal position of the discoverer; X worst is the global worst position; A is a 1×d matrix, each element of which is randomly assigned 1 or -1.

[0082] To prevent the algorithm from falling into a local optimum and ensure diversity in the search space, 20% of the population acts as vigilantes, randomly spawning nearby locations and comparing them with the current optimal position of the discoverer. If their energy recovery is better, they will flee to a location near their own; otherwise, they will go to the optimal location.

[0083] (15)

[0084] Where: X best is the current best position, δ is a very small number to prevent the denominator from being 0;

[0085] The population continuously updates its position according to the value of the fitness function until it finds the optimal parameter combination that maximizes energy recovery.

[0086] After the optimization is completed, a set of Figure 4The optimal input and output membership function curves are shown. For example, the low (L) and high (H) states of the input variable use the zmf and smf functions, while the medium (M) state uses the gaussmf function. The output variable K uses the triangular membership function. This set of precise, non-subjective functions ensures that the fuzzy controller can calculate the most appropriate regenerative braking force contribution coefficient for any operating input.

[0087] To verify the beneficial effects of the method described in the present invention, the complete strategy of the present invention was compared with two other different braking strategies under WLTC conditions close to real driving conditions in a Simulink and Cruise co-simulation platform. The comparison strategies included:

[0088] Ideal braking force distribution strategy, a theoretically optimal benchmark strategy, in which the front and rear axle braking forces are distributed completely along the I curve.

[0089] The improved braking force distribution strategy only adopts the first step of the present invention, that is, the optimized staged front and rear axle braking force distribution method, but does not optimize the proportion of regenerative braking force.

[0090] The improved fuzzy control allocation strategy and the strategy of the present invention, based on the "improved braking force allocation strategy", further adopt a fuzzy controller optimized by the sparrow algorithm to determine the optimal regenerative braking force ratio.

[0091] Reference Figure 5 Battery SOC change curve and Figure 6 The simulation results clearly show that after a complete WLTC test cycle, the vehicle using the "inventive strategy" achieved the highest final battery SOC value, and its cumulative braking energy recovery significantly outperformed both the "ideal braking force distribution strategy" and the "improved braking force distribution strategy." This fully demonstrates that the inventive method can effectively improve the energy recovery efficiency of pure electric vehicles while ensuring safety.

[0092] Example 2:

[0093] This embodiment provides a regenerative braking control system for a pure electric vehicle, which is designed to implement the regenerative braking control method described in the above embodiment 1. The system mainly includes a braking force distribution module, a fuzzy control module, and a braking execution module.

[0094] Braking force distribution module:

[0095] This module is the core of achieving phased, optimized braking force distribution. It is configured to precisely distribute braking force between the front and rear axles based on the real-time braking intensity z within the safety zone defined by the I curve, the ECE regulatory curve, and the preset road adhesion coefficient. Its internal logic strictly adheres to the four-stage strategy described in Example 1:

[0096] When z is below 0.087, all braking force is distributed to the front axle.

[0097] When z is between 0.087 and 0.52, distribution is performed along the preset optimal tangent line AF.

[0098] When z is between 0.52 and 0.7, the rear axle braking force is increased along the line FB, which is an alternative to the f-line.

[0099] When z is higher than 0.7, the hydraulic braking force is distributed completely according to the I curve.

[0100] Fuzzy control module:

[0101] The function of this module is to intelligently decide the proportion of regenerative braking force after the total braking force of the front axle is determined.

[0102] Input: This module takes the braking intensity z, current vehicle speed v and battery state of charge SOC provided by the vehicle controller unit (VCU) as input variables.

[0103] Core Logic: It integrates a fuzzy controller whose membership function has been optimized offline using the Sparrow Search Algorithm (SSA). The optimization goal is to maximize energy recovery efficiency while meeting constraints such as the motor's maximum generating torque, speed range, and the battery's SOC upper limit (≤0.9).

[0104] Output: The module ultimately outputs an optimal front axle regenerative braking force coefficient, K. The optimized membership function ensures that the output K value is optimal under all operating conditions.

[0105] Brake execution module:

[0106] This module is the final execution end of the system, responsible for converting instructions from the above two modules into physical actions.

[0107] It receives the total braking force instructions for the front and rear axles from the "braking force distribution module" and the regenerative braking force ratio coefficient K from the "fuzzy control module".

[0108] Based on these commands, the module coordinates the vehicle's regenerative braking system (drive motor) and hydraulic braking system. It precisely controls the motor's torque to deliver the calculated regenerative braking force, while simultaneously controlling the hydraulic brake unit to supplement the remaining required braking force. This completes the braking process, achieving safe and efficient braking energy recovery.

[0109] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that ordinary relevant personnel in this technical field can make several improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered as the scope of protection of the present invention.

[0110] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

Claims

1. A regenerative braking control method for a pure electric vehicle, characterized in that: The following steps are involved: Within the safe braking area determined by the I curve, the ECE regulation curve and the preset road adhesion coefficient, the braking force of the front and rear axles is distributed in stages according to the braking intensity; A fuzzy controller is established with braking intensity, vehicle speed, and battery state of charge as input variables and the ratio of the front axle regenerative braking force to the total front axle braking force as the output variable. The membership function of the fuzzy controller is optimized using a sparrow search algorithm.

2. The method according to claim 1, characterized in that The step of distributing the braking force of the front and rear axles in stages comprises: Below the first preset braking intensity threshold, the braking force is entirely borne by the front axle; Between the first preset braking intensity threshold and the second preset braking intensity threshold, the front and rear axle braking forces are distributed along a preset tangent of the ECE regulation curve; Between the second preset braking intensity threshold and the third preset braking intensity threshold, the rear axle braking force increases along a preset road adhesion coefficient line or a line replacing it; Above the third preset braking intensity threshold, the regenerative braking system stops working and the braking force is completely borne by the hydraulic braking system.

3. The method according to claim 2, wherein: The first preset braking intensity threshold is 0.087; the second preset braking intensity threshold is 0.52; and the third preset braking intensity threshold is 0.

71.

4. The method according to claim 2, wherein: The preset tangent line of the ECE regulation curve is determined as a straight line, which satisfies the condition that the area of ​​the polygon enclosed by the straight line, the ECE curve, the I curve and the ground adhesion coefficient line is the smallest.

5. The method according to claim 4, characterized in that: The tangent point of the preset tangent line corresponds to a horizontal coordinate value of 4879.76 on the ECE regulation curve.

6. The method according to claim 1, wherein: The step of optimizing the membership function by using the sparrow search algorithm comprises: Define a fitness function with the optimization goal of minimizing the change in battery state of charge; Setting constraints, including a maximum generating torque limit of the motor, an allowable energy recovery speed range, and an upper threshold of the battery state of charge; Execute the optimization algorithm to iteratively update the characteristic parameters of the membership function through the discoverer, follower and vigilant strategies.

7. The method according to claim 6, characterized in that: The upper threshold of the battery state of charge is set to 0.

91.

8. The method according to claim 6, wherein: The design of the membership function is that the low state and the high state adopt the zmf function and the smf function, and the medium state adopts the gaussmf function; The output variable uses a triangular membership function.

9. A regenerative braking control system for a pure electric vehicle, characterized in that: The system is configured to perform the method according to any one of claims 1 to 8, comprising: a braking force distribution module configured to, within a safety zone defined by the I-curve, the ECE regulation curve, and a preset road adhesion coefficient, distribute all braking force to the front axle below a first preset braking intensity threshold, distribute braking force along a preset tangent line of the ECE regulation curve between the first and second preset braking intensity thresholds, and increase rear axle braking force along a preset road adhesion coefficient line or a line alternative thereto between the second and third preset braking intensity thresholds; A fuzzy control module, which takes braking intensity, vehicle speed, and battery state of charge as inputs and outputs the proportion of regenerative braking force on the front axle. Its internal membership function is optimized using a sparrow search algorithm to minimize the change in battery state of charge while satisfying motor and battery constraints. The brake execution module is used to receive and execute instructions from the brake force distribution module and the fuzzy control module, and coordinate the control of the regenerative braking system and the hydraulic braking system.

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