A regenerative braking control method considering dynamic vehicle-road conditions and braking intention

Abstract

This invention discloses a regenerative braking control method considering dynamic vehicle-road conditions and braking intent, belonging to the field of electric vehicle braking control. The method first establishes dynamic constraint boundaries for front and rear axle braking force distribution and motor power based on road adhesion coefficient, gradient, and vehicle load. Second, it constructs a multi-objective optimization model focusing on energy recovery efficiency, braking stability, and safety, and uses the NSGA-III algorithm to obtain the Pareto optimal solution set. Then, it employs the analytic hierarchy process (AHP) to determine the weights of each optimization objective under different braking intents, and combines this with a method for ranking near-ideal solutions to select the optimal braking force distribution coefficient from the solution set. Finally, it outputs control commands to achieve coordinated braking force distribution. This invention can adaptively adjust the control strategy according to dynamic vehicle-road conditions and braking intent, maximizing energy recovery efficiency while ensuring braking safety and stability, thus solving the problem of insufficient adaptability of existing technologies under complex operating conditions.

Description

Technical Field

[0001] This invention relates to the technical fields of electric vehicle braking control and braking energy recovery technology, specifically a regenerative braking control method that takes into account dynamic vehicle-road conditions and braking intentions. Background Technology

[0002] With the rapid development of electric vehicles, regenerative braking technology has become an important technical approach to improve vehicle range and reduce overall vehicle energy consumption. Regenerative braking converts the vehicle's kinetic energy into electrical energy and feeds it back to the battery by having the drive motor operate in a generator state during deceleration or braking. This reduces mechanical braking energy loss and improves overall vehicle energy utilization efficiency. As the core of the regenerative braking system, regenerative braking control needs to rationally distribute braking force among the front and rear axles, the hydraulic braking system, and the electric motor braking system, while simultaneously meeting requirements for braking safety, braking stability, and energy recovery efficiency (energy regeneration efficiency).

[0003] Most existing regenerative braking control methods rely on empirical rules or fixed distribution curves. Their control parameters are typically designed around a single typical operating condition, making it difficult to adequately adapt to the constantly changing vehicle-road conditions during actual driving. In reality, vehicles are affected by factors such as road adhesion coefficient, road gradient, vehicle load, and changes in the center of gravity during braking. These factors alter the front-to-rear axle normal load distribution, the boundary of the regenerative braking force that the motor can provide, and the overall braking demand of the vehicle, thus affecting braking distance, wheel adhesion utilization, and the effectiveness of brake energy recovery. When a vehicle is on a low-adhesion road surface, downhill, or under different loading conditions, using a uniform braking force distribution strategy can easily lead to an unreasonable front-to-rear axle braking force distribution, resulting in increased wheel slip ratio, decreased braking stability, and even compromised vehicle braking safety.

[0004] Furthermore, drivers' braking intentions vary significantly across different driving scenarios. During light braking, the vehicle's braking demand is low, making it more suitable to prioritize electric motor braking for higher energy recovery. During normal braking, a balance between energy recovery efficiency and braking smoothness is necessary. During emergency braking, the control objective should prioritize braking safety and stability, requiring faster and more complete intervention of hydraulic braking. While many existing regenerative braking control methods consider braking intensity or pedal input, they often fail to systematically integrate the changing priorities of control objectives corresponding to different braking intentions into the regenerative braking control process. This results in insufficient adaptability of the control strategy across different braking scenarios, making it difficult to achieve a harmonious balance between energy recovery and braking performance.

[0005] Therefore, there is an urgent need to propose a regenerative braking control method that can comprehensively consider dynamic vehicle-road conditions and differences in braking intent, so that regenerative braking force and hydraulic braking force can be adaptively distributed according to real-time operating conditions, thereby achieving a comprehensive improvement in braking safety, braking stability and energy recovery efficiency while meeting braking and system constraints. Summary of the Invention

[0006] To overcome the shortcomings of existing regenerative braking control methods, such as insufficient adaptability under dynamic vehicle-road conditions, inflexible target switching under different braking intentions, and difficulty in effectively selecting the best result from multi-objective optimization, this invention proposes a regenerative braking control method that considers both dynamic vehicle-road conditions and braking intentions. This method constructs a control framework oriented towards coordinated optimization of braking safety, braking stability, and energy recovery efficiency, achieving adaptive allocation of regenerative braking force and hydraulic braking force under complex operating conditions. Through systematic design of regenerative braking constraint boundaries, multi-objective optimization solutions, and optimal solution selection strategies, the method improves the matching capability of regenerative braking control to varying operating conditions and different braking demands, ensuring the rationality and effectiveness of braking force allocation results, thereby enhancing the safety, stability, and energy recovery performance of the vehicle during braking.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a regenerative braking control method that considers dynamic vehicle-road conditions and braking intentions, the method comprising the following steps: Obtain vehicle status information and road environment information, and establish dynamic constraint boundaries that include front and rear axle braking force distribution constraints and motor regenerative braking force constraints; Based on the driver's braking intention, a multi-objective optimization model is constructed with braking safety, braking stability and energy regeneration efficiency as optimization objectives; The NSGA-Ⅲ algorithm is used to solve the multi-objective optimization model to generate a Pareto optimal solution set for braking force allocation that satisfies the dynamic constraint boundary. The weights of each optimization objective under different braking intentions are determined based on the analytic hierarchy process (AHP), and the Pareto optimal solution set is evaluated and ranked using the approximation ideal solution ranking method to select the optimal braking force distribution coefficient. Based on the optimal braking force distribution coefficient, a regenerative braking control command is output to achieve coordinated distribution of regenerative braking force and hydraulic braking force.

[0008] Furthermore, establishing dynamic constraint boundaries specifically includes: Based on dynamic vehicle-road conditions, ECE curve constraints, I curve constraints, and f-line group constraints are established to determine the range of front and rear axle braking force distribution coefficients. Determine the motor regenerative braking torque limit based on the constraints of the motor external characteristics, the battery charging power constraint, and the coupled effects of vehicle speed, braking intensity, and the state of charge of the battery pack.

[0009] Further, the braking intention includes the first-intensity braking (light braking), the second-intensity braking (conventional braking), and the third-intensity braking (emergency braking); taking the braking intensity z as the quantization value, when 0.1g < z ≤ 0.3g it is light braking, when 0.3g < z ≤ 0.8g it is conventional braking, and when z > 0.8g it is emergency braking, where g is the gravitational acceleration; among them, when performing light braking, the energy regeneration efficiency is taken as the optimization target first, when performing conventional braking, multiple optimization targets are comprehensively balanced, and when performing emergency braking, the braking safety is taken as the optimization target first.

[0010] Further, the constructed multi-objective optimization model includes: Energy regeneration efficiency objective function:

[0011] In the formula, is the energy regeneration efficiency objective function; is the braking energy; is the motor regenerative braking energy; is the motor regenerative braking torque; is the motor speed, is the motor output working efficiency; is the mass of the vehicle, is the vehicle speed, is the vehicle speed after braking; is the vehicle gravity; is the rolling resistance coefficient; is the air resistance coefficient; is the downhill road slope; is the frontal area; is the air density; is the driving time; Braking stability objective function:

[0012] In the formula, is the braking stability objective function; are the utilization adhesion coefficients of the front axle and the rear axle respectively; is the braking intensity of the vehicle driving on a downhill road; Braking safety objective function:

[0013] In the formula, is the braking safety objective function; is the front axle braking force.

[0014] Furthermore, the specific steps for solving the problem using the NSGA-Ⅲ algorithm include: Based on the constraints of the braking force distribution coefficient variable, an initial braking force distribution coefficient sequence set is generated, and iterative processing is performed by simulating binary crossover and point mutation operations. The sequence set after iteration is sorted non-dominated, and the braking force allocation coefficient sequence with the shortest distance is selected using the reference point mechanism for the next iteration, until the maximum number of iterations is reached and the Pareto optimal solution set is output.

[0015] Furthermore, the evaluation ranking using the approximation-to-ideal-solution ranking method specifically includes: calculating the normalized distance from each solution in the Pareto optimal solution set to the ideal solution and the normalized distance to the worst solution; establishing an evaluation standard index based on weighted distance, and selecting the solution with the optimal evaluation standard index as the final braking force allocation coefficient.

[0016] Furthermore, the calculation of weighting factors using the analytic hierarchy process includes: ① When setting up light braking, energy regeneration efficiency is given priority; when setting up regular braking, three objective functions are considered comprehensively; when setting up emergency braking, braking safety is given priority. ②Establish a multi-level structure, including the target layer, the criteria layer, and the basic indicator layer; ③ Compare the priorities of the objective functions and design the corresponding judgment matrix; ④ Calculate the consistency index and consistency ratio to determine whether the judgment matrix is ​​reasonable and obtain the weight factors for the adaptive objective function.

[0017] Secondly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the above-described method when executing the program.

[0018] As can be seen from the above technical solution, the present invention discloses a regenerative braking control method that considers dynamic vehicle-road conditions and braking intentions, which has the following advantages compared with the prior art: This invention can adaptively adjust the priority of control targets according to dynamic vehicle-road conditions and different braking intentions, thereby improving the safety, stability and energy recovery efficiency of regenerative braking control.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0023] Figure 1 This is a schematic flowchart of a regenerative braking control method that takes into account dynamic vehicle-road conditions and braking intentions, provided by the present invention.

[0024] Figure 2 This is a schematic diagram of the multi-objective space of the braking force distribution coefficient provided by the present invention, wherein (a) is the Pareto front and (b) is the optimal Pareto solution based on the distance evaluation criterion.

[0025] Figure 3 The diagram shows the optimal front and rear axle braking force distribution coefficients under different working conditions, considering dynamic road conditions and braking intentions, provided by the present invention. Part (a) represents the high-adhesion road surface, part (b) represents the high-adhesion road surface, and part (c) represents the low-adhesion road surface.

[0026] Figure 4 The diagram shows the optimal electro-hydraulic braking force distribution coefficient under different working conditions, considering dynamic vehicle-road conditions and braking intentions, provided by the present invention. Part (a) represents a high-adhesion road surface, part (b) represents a high-adhesion road surface, and part (c) represents a low-adhesion road surface.

[0027] Figure 5 This is a schematic diagram of the electronic device structure provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0029] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0030] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0031] See Figure 1 As shown in the figure, this invention discloses a regenerative braking control method that considers dynamic vehicle-road conditions and braking intentions. The specific implementation method and working principle are as follows: I. Establishing Multiple Constraints and Limitations: The safety and stability of braking are crucial to the design of regenerative braking control, which manifests in the need for the distribution of braking force between the front and rear axles to conform to the constraints of the braking control line group. The braking control line group consists of constraints such as the ECE curve, the I curve, and the f-line group. This invention considers the influence of dynamic vehicle-road conditions on the constraints of the braking control line group and establishes a front-to-rear axle braking force distribution coefficient. Scope limitations: (1) The ECE curve constraint proposes a front-to-rear axle braking force distribution principle based on maintaining braking stability and improving braking efficiency. Under the ECE curve constraint... The scope is established as follows:

[0032] In the formula, The front and rear axle braking force distribution coefficient under ECE curve constraints; This is the distance between the front and rear axles; This is the horizontal distance from the rear axle to the vehicle's center of gravity. The slope of the downhill road; The distance from the vehicle's center of gravity to the ground; The braking intensity of an electric vehicle traveling downhill; The braking intensity of an electric vehicle traveling on a flat road.

[0033] (2) Electric vehicles following ideal braking force distribution can maintain high braking stability even when both front and rear wheels lock up simultaneously under arbitrary road surface adhesion coefficients. (I-curve constraint) The scope is established as follows:

[0034] In the formula, The front and rear axle braking force distribution coefficient under I-curve constraints.

[0035] (3) Front axle anti-lock braking system (ABS) cable group constraints can prevent premature locking of the front wheels of electric vehicles. Under f-cup constraint... The scope is established as follows:

[0036] In the formula, The front and rear axle braking force distribution coefficient under the constraint of line group f; This is the road surface adhesion coefficient.

[0037] In addition to the constraints of the braking control line group, regenerative braking control also needs to consider the external characteristic constraints of the drive motor. Based on the limitations of the drive motor's external characteristic of constant torque at low speeds and constant power at high speeds, the regenerative braking torque under the motor's external characteristic constraints is established as follows:

[0038] In the formula, The regenerative braking torque is given by the external characteristics of the motor. This represents the maximum motor torque. This represents the maximum power of the motor. This refers to the motor speed; This is the rated speed.

[0039] The drive motor is also affected by the battery charging power. Therefore, the regenerative braking torque under the constraint of the battery's maximum charging power is:

[0040] In the formula, Regenerative braking torque under the constraint of maximum battery charging power; This represents the battery's maximum charging power. The motor speed at the battery's maximum charging power; For battery operating efficiency.

[0041] Furthermore, this invention also takes into account the braking intensity and vehicle speed of electric vehicles. The coupling effect of the battery pack's state of charge (SOC) on the regenerative braking force of the motor is as follows: At higher braking intensities, the electro-hydraulic braking system is more stable than the drive motor; when the vehicle operates at low speeds, the back electromotive force generated by the drive motor is smaller, reducing the energy recovery from the battery pack; when the battery pack's SOC is sufficiently high, overcharging will shorten the battery pack's lifespan. The limit for the motor's regenerative braking torque is established as follows:

[0042]

[0043]

[0044]

[0045] In the formula, Limiting the regenerative braking torque of the motor; The factor affecting vehicle speed; This is a factor affecting braking intensity; The battery SOC impact factor. , These are the upper and lower limits of vehicle speed, respectively. , These are the upper and lower limits of braking intensity, respectively. , These represent the upper and lower limits of battery SOC, respectively.

[0046] II. Establishment of Multi-Objective Functions: Before solving a multi-objective optimization problem, the optimization objectives must first be clearly defined, and then a corresponding objective function should be established to characterize the key performance indicators in regenerative braking control. This invention, in establishing the multi-objective function, not only considers the energy regeneration efficiency of electric vehicles, but also quantifies the safety and stability indicators during regenerative braking of electric vehicles. (1) During the process of braking energy traveling from the wheel to the input battery, it undergoes mechanical energy consumption in the transmission mechanism, energy consumption during motor power generation, and energy consumption during battery charging. Ultimately, only a portion of the initial braking energy can be recovered. To better evaluate the quality of regenerative braking control energy conversion, energy regeneration efficiency is used as the objective function, defined as the maximum value of the ratio of motor regenerative braking energy to the total braking energy.

[0047] In the formula, The objective function is the energy regeneration efficiency. As braking energy; Regenerate braking energy for the motor; To provide regenerative braking torque for the motor; To improve the motor's output efficiency; This refers to the vehicle speed after braking; For the gravity of electric vehicles; This is the rolling resistance coefficient; This refers to the air drag coefficient; For windward area; air density; This refers to the travel time.

[0048] (2) Braking stability determines the rationality of the front and rear axle braking force distribution. This invention introduces the use of the adhesion coefficient as an indicator for evaluating braking stability. The adhesion coefficient is defined as the ratio of wheel braking force to wheel normal load when an electric vehicle brakes at a certain deceleration. The total vehicle braking force is the resultant force of the front and rear axle braking forces. When there is a large difference between the adhesion coefficient and braking intensity of the front and rear axles, it indicates that one of the front and rear axle braking forces must be larger. Under conditions where the braking force demand is high, it is easy to cause the braked wheels to lock up, affecting braking stability. Braking stability is taken as the objective function and defined as the minimum value of the difference between the adhesion coefficient and braking intensity of the front and rear axles:

[0049] In the formula, Let the braking stability objective function be... The coefficient of adhesion is used for the front axle and the rear axle, respectively.

[0050] (3) The braking safety of front-wheel-drive electric vehicles also affects the braking force distribution strategy. During braking, a larger proportion of the braking force should be distributed to the front axle. This ensures vehicle safety while also maximizing energy recovery by the front axle drive motor. Braking safety is defined as the maximum braking force on the front axle as the objective function:

[0051] In the formula, The objective function for braking safety; This is for front axle braking force.

[0052] III. Design of Braking Force Distribution Method Based on NSGA-III: Based on the established multi-constraints and multi-objective functions, this invention applies the NSGA-III-based braking force allocation method to generate the Pareto solution set of the braking force allocation coefficients. The specific process for generating the Pareto solution set of the braking force allocation coefficients includes the following four steps.

[0053] Steps 1 and 2: Generation and iteration of the braking force distribution coefficient sequence.

[0054] First, based on the constraints of the braking force distribution coefficient variable, a random generator is generated containing... The initial braking force allocation coefficient sequence set of each sequence Then, by performing crossover and mutation iterations on the initial sequence set, an iterative braking force allocation coefficient sequence set is generated. Among them, the simulated binary crossover method, as the crossover operation in the genetic algorithm, can improve search efficiency and maintain the diversity of solutions. Assume the selected sequence is... and The corresponding crossover sequence generated after the simulated binary crossover method is: and :

[0055]

[0056] In the formula, and To select sequences; and The modulus is the crossover sequence generated by the quasi-binary crossover method; It is the distribution factor; This represents the crossover rate.

[0057] Furthermore, point mutation, as a mutation operation in genetic algorithms, has advantages such as good search performance and suitability for multi-objective optimization problems. When the sequence... A mutation occurs, and the corresponding mutated sequence is generated. for:

[0058] In the formula, The generated mutant sequence; and Mutant sequences The upper and lower bounds; The mutation rate.

[0059] Step 3: Non-dominated sorting of the braking force distribution coefficient sequence.

[0060] Set the braking force distribution coefficient sequence before and after iteration and merged into ,Will Divided into common There are several non-dominated layers. To ensure that the number of sequences participating in each iteration remains constant, it is necessary to select the better sequences from all sequences before and after the iteration. The sequence is used for the next iteration.

[0061] First, the energy regeneration efficiency, braking stability, and safety of all braking force distribution coefficient sequences are calculated using an established multi-objective function. Then, all sequences not dominated by other braking force distribution coefficient sequences are selected and stored in the non-dominated layer. In the middle. Among them, if a certain sequence Its performance in any objective function defined in this invention is no worse than that of another sequence. Poor, or It outperforms in at least one objective function Then it is called quilt Domination. Next, in the sequence set. Remove from From the sequences included, sequences that are not dominated by other sequences are selected from the remaining sequences and stored in the non-dominated layer. In the middle. And so on, until all braking force distribution coefficient sequences are sorted and stored in the non-dominated set. In the middle. Based on the definition and properties of domination, it can be seen that non-dominated sets... The optimal braking force distribution coefficient sequence is found in [the dataset]. The second sequence is the braking force distribution coefficient. The braking force distribution coefficient sequence is the worst.

[0062] Step 4: Selection of Pareto solution set for braking force distribution coefficient.

[0063] First, the better sequences are stored in the initial braking force allocation coefficient sequence for the next iteration, arranged in ascending order of non-dominated sets. In. Until added. Time (the first) (one non-dominated layer) The number of sequences exceeds the number of sequences with the initial braking force allocation coefficient. Then, the non-dominated set Eliminate and adopt a reference point mechanism for Sort the sequences in the dataset. Calculate the vertical distance of each individual sequence to the reference line, and select the braking force allocation coefficient sequence with the shortest distance for the next iteration. If the non-dominated set... It includes Given a velocity sequence, then select... The front with a smaller vertical distance to the reference line A sequence, namely Deposit Make the number of sequences contained therein reach the number of sequences of the initial braking force allocation coefficient. and the remaining braking force distribution coefficient sequence Eliminate. This will allow for... The sequence in the sequence is used for the next iteration.

[0064] This completes one iteration of the braking force distribution coefficient sequence. (Maximum number of iterations) The Pareto solution set for the braking force distribution coefficients can then be obtained.

[0065] IV. Extraction of the Pareto solution for the optimal braking force distribution coefficient: The Pareto solution set of the braking force distribution coefficients is distributed in a multi-objective space, forming a Pareto front, where a large number of candidate Pareto solutions typically exist, such as... Figure 2As shown in section (a). Therefore, this invention studies the extraction of the optimal Pareto solution from the following two aspects: (1) Traditional global optimal solution selection methods rely on prior knowledge such as expert experience, which increases the difficulty of selecting the Pareto solution for the optimal braking force allocation coefficient. Therefore, this invention proposes a Pareto solution extraction method for the optimal braking force allocation coefficient based on the distance evaluation criterion, according to the approximation ideal solution ranking method.

[0066] 2) Most methods for selecting the global optimal solution for braking force distribution coefficients in regenerative braking control mainly consider the constant objective function optimization requirements, while ignoring the priority changes among multiple objective functions caused by differences in braking levels. To address this varying multi-objective optimization requirement, this invention utilizes the analytic hierarchy process (AHP) to generate suitable objective function weighting factors and extracts the Pareto solution for the optimal braking force distribution coefficients, taking into account the influence of different braking intentions.

[0067] First, the approximation-to-ideal-solution sorting method considers the distance from the optimal Pareto solution to the ideal solution as the shortest, and the distance to the worst solution as the longest, such as... Figure 2 Part (b) shows the positions of the optimal Pareto solution, the ideal solution, and the worst solution in the multi-objective space. The normalized distances from the selected optimal Pareto solution to the ideal and worst solutions are also shown. , They are respectively:

[0068] In the formula, The normalized distance from the optimal Pareto solution to the ideal solution; The normalized distance between the selected optimal Pareto solution and the worst solution; , , The distance between the ideal solution and the optimal Pareto solution in the coordinate axes of the three objective functions: energy regeneration efficiency, braking stability, and braking safety, respectively. , , The distances between the worst and best Pareto solutions are respectively located along the three objective function coordinate axes. As a weighting factor for energy regeneration efficiency; This is a braking stability weighting factor; This is a weighting factor for braking safety.

[0069] Then, the distance evaluation criterion for selecting the optimal Pareto solution is established as follows:

[0070] In the formula, This is a distance evaluation standard index.

[0071] Secondly, this invention utilizes the analytic hierarchy process (AHP) to calculate weighting factors under different multi-objective optimization requirements: ① For minor braking, energy regeneration efficiency is prioritized; for regular braking, three objective functions are considered comprehensively; and for emergency braking, braking safety is prioritized. ② A multi-step hierarchical structure is established, including an objective layer, a criterion layer, and a basic indicator layer. This multi-step hierarchical structure can generate corresponding judgment matrices based on the number of objective, criterion, and basic indicator layers. ③ The priorities of the objective functions are compared, and corresponding judgment matrices are designed. ④ The consistency index is calculated. CI With consistency ratio CR for:

[0072] In the formula, CI It is a consistency index; CR The consistency ratio; To determine the largest eigenvalue of a matrix; To determine the order of a matrix; RI It is the average random consistency index, and is related to Positive correlation.

[0073] Calculate different braking intentions based on the consistency index and the average random consistency index. CR Value, when CR When the value is less than 0.1, the judgment matrix is ​​considered reasonable. The weight factors corresponding to the three objective functions are calculated using the analytic hierarchy process (AHP), and the results are calculated under different operating conditions. , and Based on the normalized distances from the selected optimal Pareto solution to the ideal and worst solutions, we obtain the following: Figure 3 and Figure 4 The Pareto solution for the optimal braking force distribution coefficients shown is used as the optimal front and rear axle braking force distribution coefficients. and optimal electro-hydraulic braking force distribution coefficient .

[0074] V. Coordination and Allocation: Based on the obtained optimal front and rear axle braking force distribution coefficients and electro-hydraulic composite braking force distribution coefficients, the desired regenerative braking torque and desired braking pressure are obtained, and regenerative braking control commands are output and sent to the wheel hub drive motor and hydraulic braking system to achieve coordinated distribution of regenerative braking and hydraulic braking under complex working conditions.

[0075] As can be seen from the description of the above embodiments, the present invention provides a regenerative braking control method that considers dynamic vehicle-road conditions and braking intentions. This method addresses the problems of insufficient adaptability of existing regenerative braking control strategies under complex vehicle-road conditions, unclear target switching under different braking intentions, and difficulty in effectively selecting the best result from multi-objective optimization. The present invention constructs a regenerative braking control method that integrates dynamic constraint modeling, multi-objective optimization, and optimal decision-making. First, based on dynamic vehicle-road conditions such as road adhesion coefficient, road gradient, and vehicle load, constraint boundaries for front and rear axle braking force distribution and electro-hydraulic braking force distribution are established. Second, three braking intentions—light braking, conventional braking, and emergency braking—are introduced into the regenerative braking control process. A multi-objective optimization model focusing on braking safety, braking stability, and energy recovery efficiency is constructed and solved using the NSGA-III algorithm to obtain the Pareto optimal solution set for braking force distribution that satisfies the constraints. Then, the weights of each optimization objective under different braking intentions are determined using the analytic hierarchy process (AHP), and the Pareto solution set is comprehensively evaluated and ranked using a method for ranking approximate ideal solutions to select the optimal front and rear axle braking force distribution ratio and the electro-hydraulic composite braking force distribution ratio. Finally, regenerative braking control commands are output based on the selected optimal solution to achieve coordinated distribution of regenerative braking and hydraulic braking under complex operating conditions. This invention can adaptively adjust the priority of control objectives according to dynamic vehicle-road conditions and different braking intentions, improving the safety, stability, and energy recovery efficiency of regenerative braking control.

[0076] Additionally, refer to Figure 5 As shown, this embodiment of the invention also provides an electronic device that can perform the above-described method. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10.

[0077] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units, microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 and calls data stored in the memory 11 to perform various functions and process data within the electronic device.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, software systems, electronic devices, or computer program products, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0080] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0081] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A regenerative braking control method considering dynamic vehicle-road conditions and braking intention, characterized in that, The method includes the following steps: Obtain vehicle status information and road environment information, and establish dynamic constraint boundaries that include front and rear axle braking force distribution constraints and motor regenerative braking force constraints; Based on the driver's braking intention, a multi-objective optimization model is constructed with braking safety, braking stability and energy regeneration efficiency as optimization objectives; The NSGA-Ⅲ algorithm is used to solve the multi-objective optimization model to generate a Pareto optimal solution set for braking force allocation that satisfies the dynamic constraint boundary. The weights of each optimization objective under different braking intentions are determined based on the analytic hierarchy process (AHP), and the Pareto optimal solution set is evaluated and ranked using the approximation ideal solution ranking method to select the optimal braking force distribution coefficient. Based on the optimal braking force distribution coefficient, a regenerative braking control command is output to achieve coordinated distribution of motor regenerative braking force and hydraulic braking force.

2. The method according to claim 1, characterized in that, Establishing dynamic constraint boundaries specifically includes: Based on dynamic vehicle-road conditions, ECE curve constraints, I curve constraints, and f-line group constraints are established to determine the range of front and rear axle braking force distribution coefficients; wherein, the dynamic vehicle-road conditions include road adhesion coefficient, road gradient, and vehicle load; Based on the constraints of motor external characteristics, battery charging power, and the coupled effects of vehicle speed, braking intensity, and battery pack state of charge, the limit of motor regenerative braking torque is determined.

3. The method according to claim 1, characterized in that, The braking intent includes first-intensity braking, second-intensity braking, and third-intensity braking; wherein, the first-intensity braking prioritizes energy regeneration efficiency as the optimization objective, the second-intensity braking comprehensively balances multiple optimization objectives, and the third-intensity braking prioritizes braking safety as the optimization objective.

4. The method according to claim 3, characterized in that, The constructed multi-objective optimization model includes: Energy regeneration efficiency objective function: In the formula, The objective function is the energy regeneration efficiency. As braking energy; Regenerate braking energy for the motor; To provide regenerative braking torque for the motor; This refers to the motor speed. To improve the motor's output efficiency; For the quality of the car, For vehicle speed, This refers to the vehicle speed after braking; For the car's gravity; This is the rolling resistance coefficient; This refers to the air drag coefficient; The slope of the downhill road; For windward area; air density; Travel time; Braking stability objective function: In the formula, Let the braking stability objective function be... The coefficient of adhesion is used for the front and rear axles, respectively. The braking intensity of a car driving downhill; Braking safety objective function: In the formula, The objective function for braking safety; For front axle braking force.

5. The method according to claim 2, characterized in that, Solving using the NSGA-III algorithm specifically includes: Based on the constraints of the braking force distribution coefficient variable, an initial braking force distribution coefficient sequence set is generated, and iterative processing is performed by simulating binary crossover and point mutation operations. The sequence set after iteration is sorted non-dominated, and the braking force allocation coefficient sequence with the shortest distance is selected using the reference point mechanism for the next iteration, until the maximum number of iterations is reached and the Pareto optimal solution set is output.

6. The method according to claim 5, characterized in that, The evaluation and ranking method using the approximation of ideal solutions specifically includes: calculating the normalized distance from each solution in the Pareto optimal solution set to the ideal solution and the normalized distance to the worst solution; establishing an evaluation standard index based on weighted distance; and selecting the solution with the optimal evaluation standard index as the final braking force allocation coefficient.

7. The method according to claim 4, characterized in that, The calculation of weighting factors using the analytic hierarchy process includes: The first level of braking prioritizes energy regeneration efficiency; the second level of braking comprehensively considers three objective functions; and the third level of braking prioritizes braking safety. Establish a multi-level structure, including a target layer, a criterion layer, and a basic indicator layer; Compare the priorities of the objective functions and design the corresponding judgment matrix; Calculate the consistency index and consistency ratio to determine whether the judgment matrix is ​​reasonable and obtain the weight factors for the adaptive objective function.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that can be executed by the processor, the processor executing the program to implement the method as described in any one of claims 1 to 7.

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