Vehicle electromechanical coupling control method considering braking comfort and endurance mileage

Abstract

The invention relates to the field of dynamic coordination control of electric vehicles, in particular to a vehicle electromechanical coupling control method considering braking comfort and endurance mileage, which comprises the following steps: firstly, acquiring vehicle total braking force, active suspension control force of a hub motor-suspension system, a chopping signal of a motor driving system and a switching angle signal; based on the total braking force, the system intelligently distributes it to motor drive and mechanical braking systems. The active suspension control force is utilized to realize coupling control of braking vertical dynamics and endurance mileage. And the braking comfort and the endurance mileage are further coordinated through a chopping signal and a switching angle signal. And finally, the system outputs braking force distribution, the optimized active suspension control force, the optimized motor chopping and the optimized switching angle signal, and the braking performance and the cruising ability of the vehicle are comprehensively improved. According to the method, multi-system data are effectively integrated, a scientific and efficient solution is provided for vehicle braking control, and the braking comfort and the endurance mileage are improved.

Description

Technical Field

[0001] The invention relates to the field of electric vehicle dynamics coordinated control, and in particular to a vehicle electromechanical coupling control method taking braking comfort and cruising range into consideration. Background Art

[0002] With the continuous development of electrification, new energy vehicle technology has attracted much attention. Compared with other drive types, wheel hub drive has the advantages of more flexible control, simpler mechanical transmission structure and higher transmission efficiency, and is considered to have great potential in driving vehicles. Switched reluctance motor has the advantages of high cost-effectiveness, low starting current, high power density, large starting torque and high reliability, and has great application potential in the automotive industry.

[0003] However, unlike the traditional centralized drive, in the wheel hub drive system, the motor, brake and wheel hub are concentrated in one unit, which may increase the unsprung mass of the vehicle, resulting in vertical vibration of the vehicle. In addition, the unbalanced vertical force generated by the eccentricity of the stator and rotor of the wheel hub motor further deteriorates the ride comfort of the vehicle, which is another factor to be considered. The existing technologies mainly suppress the vertical vibration of the vehicle through motor drive system control technology and active suspension control technology respectively, and do not take into account the coupling characteristics between the suspension and the wheel hub motor under the vehicle feedback braking condition. Considering the vehicle braking condition, the switched reluctance wheel hub motor can be used as a generator to recover braking energy, thereby increasing the vehicle's cruising range. In addition, the active suspension control technology based on linear motors can not only improve the vertical comfort of the vehicle, but also recover the suspension vibration energy.

[0004] Therefore, the present invention proposes a vehicle electromechanical coupling control method that takes into account braking comfort and cruising range, and achieves a comprehensive improvement in vehicle braking comfort and cruising range through dual-level coupling control of the motor drive system and the vehicle control system. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle electromechanical coupling control method taking into account braking comfort and cruising range to solve the problems raised in the above background, which has important research significance for enriching the dynamic coupling control technology of wheel hub driven electric vehicles.

[0006] The present invention provides to achieve the above purpose, and the technical solution of the present invention is achieved as follows: According to one aspect of the present invention, a vehicle electromechanical coupling control method considering braking comfort and cruising range is provided, comprising: The acquisition steps include: Obtaining the total braking force of the vehicle; Obtain active suspension control force of the wheel hub motor-suspension system; Obtain the chopping signal of the motor drive system; Obtain the switch angle signal of the motor drive system; Processing steps include: Based on the total vehicle braking force, distribute it to the motor drive system and the mechanical brake system; According to the active suspension control force of the wheel hub motor-suspension system, the vehicle braking vertical dynamics performance and the driving range are coupled to control; Based on the chopping signal of the motor drive system, the vehicle braking comfort and the cruising range are coordinated and controlled; Further coordinate and control the vehicle braking comfort and cruising range according to the switch angle signal of the motor drive system; Output steps include: The output braking force distribution results, optimized active suspension control force, optimized motor chopping signal and optimized motor switch angle signal can achieve a comprehensive improvement in vehicle braking comfort and cruising range.

[0007] According to another aspect of the present invention, the method of obtaining the total braking force of the vehicle specifically includes: obtaining a braking condition input by a driver; and determining the total braking force of the vehicle based on the braking condition.

[0008] According to another aspect of the present invention, the distribution of the total braking force of the vehicle to the motor drive system and the mechanical braking system is provided, which specifically includes: combining the external characteristics of the motor and the ideal braking force distribution curve, establishing a vehicle front and rear axle braking force distribution model based on braking intensity zoning, thereby realizing the distribution of the braking force of the front and rear axles of the vehicle, and considering the coupling characteristics of the real-time driving parameters of the vehicle, formulating a vehicle electromechanical braking force distribution coupling factor, thereby realizing the coupled distribution of the vehicle electromechanical braking force.

[0009] According to another aspect of the present invention, the vehicle front and rear axle braking force distribution model based on braking intensity partitioning is also provided, including: When the vehicle's current braking intensity is between 0 and z 1, the braking force distribution follows the OA line; When the vehicle's current braking strength is z 1 and z 2, the braking force distribution follows the AB line; When the vehicle's current braking strength is z 2 and z 3, the braking force distribution follows the BC line; When the vehicle's current braking strength is z 3 and z When the speed is between 4, the braking force distribution follows the CD line; When the vehicle's current braking strength exceeds z 4, the braking force distribution follows the I curve; when the vehicle's current braking intensity exceedsz When 4, the braking force distribution follows the I curve; the calculation formula is: , in, m is the vehicle mass, g is the acceleration due to gravity, z is the vehicle braking strength, F bf and F br is the braking force on the front and rear axles of the vehicle, z 1. z 2. z 3. And z 4 are the braking strengths corresponding to points A, B, C and D, respectively, β max is the maximum front wheel braking force distribution coefficient, φ is the road adhesion coefficient, L is the vehicle wheelbase, b is the distance from the vehicle center of mass to the rear axle, h g is the height of the vehicle's center of mass, z 1. z 2. z 3 and z 4 is the preset braking intensity threshold, z 1. z 2. z 3 and z The sizes of 4 are: 0.15, 0.1621, 0.4482, 0.6.

[0010] According to another aspect of the present invention, a method for determining the electromechanical braking force distribution coupling factor is also provided, including: obtaining the real-time vehicle speed, battery SOC and vehicle braking strength; normalizing the vehicle speed, battery SOC and braking strength based on preset upper and lower thresholds; and calculating the electromechanical braking force distribution coupling factor based on the normalization result.

[0011] According to another aspect of the present invention, the method for obtaining the active suspension control force of the hub motor-suspension system is also provided, which specifically includes: establishing a linear quadratic regulation (LQR) control framework based on a linear motor, realizing accurate estimation of system state variables based on a Kalman filter estimation algorithm, optimizing the LQR control weight matrix through a genetic algorithm, and obtaining the active suspension control force of the hub motor-suspension system that is adaptive based on the vehicle braking condition.

[0012] According to another aspect of the present invention, the optimization objective function of the LQR control framework is provided, including: the root mean square value of the vehicle vertical acceleration; the root mean square value of the suspension dynamic deflection; the root mean square value of the wheel bounce; and the suspension vibration energy loss.

[0013] According to another aspect of the present invention, there is also provided the acquisition of the motor drive system chopping signal specifically comprising: defining a reference unbalanced vertical force, obtaining the actual unbalanced vertical force; Formulate fuzzy control rules and determine the increment of PI control parameters; According to the PI control parameter increment, the PWM duty cycle signal of the PI controller is adjusted; based on the PWM duty cycle signal, a motor drive system chopping signal is generated.

[0014] According to another aspect of the present invention, it is provided that the input variables of the fuzzy control rule include: the difference between the reference unbalanced vertical force and the actual unbalanced vertical force; and the rate of change of the difference.

[0015] According to another aspect of the present invention, the method of obtaining the switch angle signal of the motor drive system specifically includes: establishing a multi-index optimization function based on vehicle braking recovery energy, vibration energy loss, vertical acceleration and braking deceleration, The calculation formula of the multi-index optimization function is: (1) in, K opt It is the vehicle performance optimization index. θ on and θ off are the motor turn-on and turn-off angles, RMS (( ) Co )and RMS (( a f ) Co ) represent the optimized RMS values ​​of vehicle vertical acceleration and braking deceleration, respectively, ( Q net ) Co and Ereg Co Represent the optimization values ​​of suspension vibration energy loss and vehicle braking recovery energy, RMS (( ) LQR ), RMS (( a f ) LQR )、( Q net ) LQR and Ereg LQR Respectively represent the corresponding index values ​​obtained based on the LQR optimization controller, ω 1 and ω 2 is the weight coefficient of vehicle braking comfort and cruising range, α 1 andα 2 is the weight coefficient of the vehicle's vertical acceleration and braking deceleration, β 1 and β 2 is the weight coefficient of suspension vibration energy loss and vehicle braking recovery energy; The control parameters of the motor drive system including the turn-on angle and the turn-off angle are optimized by genetic algorithm. According to the optimization results, the optimal motor switching angle signal based on the vehicle braking condition is determined.

[0016] The beneficial effects of the present invention are mainly reflected in the following aspects: The vehicle electromechanical coupling control method considering braking comfort and cruising range provided by the present invention has the following beneficial effects:

[0017] 1. System integration and collaborative optimization: The present invention organically combines the wheel hub motor drive, active suspension, braking system and energy management system to form a complete electromechanical coupling system. Through a multi-level control architecture, the system is fully optimized, the potential of each subsystem is fully utilized, and a synergistic effect of 1+1>2 is achieved.

[0018] 2. Refined control of braking force distribution: The front and rear axle braking force distribution method based on braking intensity zoning is adopted, combined with the electromechanical braking force distribution coupling factor, to achieve refined distribution of braking force under different braking conditions. This method can better adapt to different braking conditions, improve braking efficiency and safety, while taking into account comfort.

[0019] 3. Optimization control of active suspension: The optimization control strategy of active suspension based on LQR comprehensively considers vehicle comfort and energy loss. The LQR control weight matrix is ​​optimized by genetic algorithm, and the adaptive adjustment of control parameters is realized, which can better adapt to different road conditions and braking conditions.

[0020] 4. Refined optimization of motor control: The coordinated control strategy of the switched reluctance hub motor and the optimized control of the motor switch angle realize the refined control of the motor operation state. This can not only improve energy utilization efficiency and extend the driving range, but also effectively reduce the vertical vibration of the vehicle and improve braking comfort.

[0021] 5. Adaptive control: The idea of ​​adaptive control is introduced in multiple control modules, so that the system can respond to changes in the external environment and vehicle status in real time, greatly improving the adaptability and robustness of the control system.

[0022] 6. Multidisciplinary cross-integration: The present invention embodies the cross-integration of knowledge from multiple disciplines such as vehicle engineering, control theory, electrical machinery, and mechanical dynamics, which helps to break through the limitations of a single discipline and generate new technological breakthroughs.

[0023] 7. Engineering practicability: The present invention fully considers the feasibility of engineering implementation, such as considering computational efficiency and real-time performance, introducing practical constraints, considering sensor measurement errors and system uncertainties, etc., which greatly improves the practicability and feasibility of the method.

[0024] 8. Comprehensive improvement of braking comfort and cruising range: Through the combined effect of the above-mentioned innovative points, the present invention achieves the simultaneous improvement of vehicle braking comfort and cruising range, solving the problem that traditional methods are difficult to take into account both goals.

[0025] In summary, the method provided by the present invention achieves a comprehensive improvement in the comfort and energy efficiency of hub-driven electric vehicles during braking through innovative means such as system integration, refined control, and adaptive optimization, providing a new technical path for the development of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The structure diagram of a vehicle electromechanical coupling braking system taking braking comfort and cruising range into consideration is provided according to the present invention.

[0027] Figure 2 The figure is a block diagram of front and rear axle braking force distribution based on braking intensity zoning.

[0028] Figure 3 It is the dynamic model of the wheel hub motor-suspension system.

[0029] Figure 4 Flow chart of genetic algorithm optimization process.

[0030] Figure 5 is the fuzzy control rule curve. DETAILED DESCRIPTION

[0031] Please refer to Figure 1-5 It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] The present invention provides a vehicle electromechanical coupling control method considering braking comfort and cruising range, comprising the following steps: The acquisition steps include: Obtaining the total braking force of the vehicle; Obtain active suspension control force of the wheel hub motor-suspension system; Obtain the chopping signal of the motor drive system; Obtain the switch angle signal of the motor drive system; Processing steps include: Based on the total vehicle braking force, distribute it to the motor drive system and the mechanical brake system; According to the active suspension control force of the wheel hub motor-suspension system, the vehicle braking vertical dynamics performance and the driving range are coupled to control; Based on the chopping signal of the motor drive system, the vehicle braking comfort and the cruising range are coordinated and controlled; Further coordinate and control the vehicle braking comfort and cruising range according to the switch angle signal of the motor drive system; Output steps include: Output braking force distribution results, optimized active suspension control force, optimized motor chopping signal and optimized motor switch angle signal.

[0035] Specifically, the obtaining of the total braking force of the vehicle specifically includes: obtaining a braking condition input by a driver; and determining the total braking force of the vehicle based on the braking condition.

[0036] Specifically, the distribution of the total braking force of the vehicle to the motor drive system and the mechanical braking system specifically includes: combining the external characteristics of the motor and the ideal braking force distribution curve to establish a vehicle front and rear axle braking force distribution model based on braking intensity zoning; according to the distribution model, determining the front and rear axle braking force distribution ratio; based on the real-time driving parameters of the vehicle, determining the electromechanical braking force distribution coupling factor; according to the coupling factor, performing coupling distribution of the vehicle electromechanical braking force.

[0037] Specifically, the vehicle front and rear axle braking force distribution model based on braking intensity partitioning includes: When the vehicle's current braking intensity is between 0 and z 1, the braking force distribution follows the OA line; When the vehicle's current braking strength is z 1 and z 2, the braking force distribution follows the AB line; When the vehicle's current braking strength is z 2 and z 3, the braking force distribution follows the BC line; When the vehicle's current braking strength is z 3 and z When the speed is between 4, the braking force distribution follows the CD line; When the vehicle's current braking strength exceeds z At 4, the braking force distribution follows the I curve; in, z 1. z 2. z 3 and z 4 is the preset braking intensity threshold.

[0038] Specifically, the method for determining the electromechanical braking force distribution coupling factor includes: Obtain the vehicle's real-time speed, battery SOC and vehicle braking strength; Based on preset upper and lower thresholds, the vehicle speed, battery SOC and braking intensity are normalized; Based on the normalized results, the computer distributes the coupling factor of the electric braking force.

[0039] Specifically, the obtaining of the active suspension control force of the wheel hub motor-suspension system specifically includes: Establish a linear quadratic regulation (LQR) control framework based on linear motors; Based on the Kalman filter estimation algorithm, the system state variables are obtained; Optimize the LQR control weight matrix through genetic algorithm; According to the optimized control weight matrix, the active suspension control force of the hub motor-suspension system adaptively based on the vehicle braking condition is determined.

[0040] Specifically, the optimization objective function of the LQR control framework includes: The root mean square value of the vehicle's vertical acceleration; RMS value of suspension dynamic deflection; RMS value of wheel runout; Suspension vibration energy loss.

[0041] Specifically, the step of obtaining the chopping signal of the motor drive system includes: Define the reference unbalanced vertical force and obtain the actual unbalanced vertical force; Formulate fuzzy control rules and determine the increment of PI control parameters; According to the PI control parameter increment, adjusting the PWM duty cycle signal of the PI controller; Based on the PWM duty cycle signal, a motor drive system chopping signal is generated.

[0042] Specifically, the input variables of the fuzzy control rules include: The difference between the reference unbalanced vertical force and the actual unbalanced vertical force; The rate of change of the difference.

[0043] Specifically, the step of obtaining the switch angle signal of the motor drive system includes: Establish a multi-index optimization function based on vehicle braking energy recovery, vibration energy loss, vertical acceleration and braking deceleration; Optimize the motor drive system control parameters including the turn-on angle and turn-off angle through genetic algorithm; According to the optimization results, the optimal switching angle signal of the motor based on the vehicle braking condition adaptation is determined.

[0044] Through the above steps, according to a vehicle electromechanical coupling control method that takes into account braking comfort and cruising range, the purpose of using the controller in this embodiment to improve the braking comfort and cruising range of a vehicle driven by a hub motor under braking conditions can be achieved.

[0045] Specifically, the present invention provides a vehicle electromechanical coupling control method that takes into account braking comfort and cruising range, including: establishing a braking force distribution controller for obtaining the total braking force of the vehicle and distributing it to the motor drive system and the mechanical braking system; establishing an active suspension optimization controller for obtaining the active suspension control force of the hub motor-suspension system, thereby achieving coupled control of the vehicle's braking vertical dynamics performance and cruising range; establishing a switched reluctance hub motor coordination controller for obtaining the motor drive system chopping signal, thereby achieving coordinated control of the vehicle's braking comfort and cruising range; establishing a motor angle optimization controller for obtaining the motor drive system switch angle signal, thereby achieving a comprehensive improvement in the vehicle's braking comfort and cruising range.

[0046] Specifically, Figure 1 The braking force distribution controller shown in the figure combines the external characteristics of the motor and the ideal braking force distribution curve to establish a vehicle front and rear axle braking force distribution model based on braking intensity partitioning, realizes the distribution of the vehicle's front and rear axle braking force, considers the coupling characteristics of the vehicle's real-time driving parameters, formulates the vehicle's electromechanical braking force distribution coupling factor, realizes the vehicle's electromechanical braking force coupling distribution, and Figure 1 In the figure, GT refers to the control signal from the brake torque controller, which is used to follow the reference electric brake force distributed to the motor drive system by the brake force distribution controller; Gk refers to the control signal from the PWM generator, which is used to coordinate the vehicle's vertical comfort and cruising range; GA refers to the signal from the switch signal controller, which is used to further coordinate the vehicle's braking comfort and cruising range; G is a fusion signal, which is obtained by logical operation of GT, GA, and Gk, and is used to improve the vehicle's braking performance and cruising range.

[0047] The vehicle front and rear axle braking force distribution method based on braking intensity partitioning is defined as Figure 2 And formula (2), that is: when the vehicle's current braking intensity is between 0 and z 1, the vehicle braking force distribution follows the OA line; when the vehicle's current braking intensity is between z 1 and z 2, the vehicle braking force distribution follows the AB line; when the vehicle's current braking intensity is between z 2 and z 3, the vehicle braking force distribution follows the BC line; when the vehicle's current braking intensity is between z 3 and z 4, the vehicle braking force distribution follows the CD line; when the vehicle's current braking strength exceeds z At 4, the vehicle braking force distribution follows the I curve.

[0048] (2) in, m is the vehicle mass, g is the acceleration due to gravity, z is the vehicle braking strength, F bf and F br is the braking force on the front and rear axles of the vehicle, z 1. z 2. z 3. And z 4 are the braking intensities corresponding to points A, B, C and D, βmax is the maximum front wheel braking force distribution coefficient, φ is the road adhesion coefficient, L is the vehicle wheelbase, b is the distance from the vehicle center of mass to the rear axle, and hg is the height of the vehicle center of mass. In fact, according to the proposed braking intensity partitioning vehicle front and rear axle braking force distribution model, as shown in Figure 2 And as shown in formula (2). According to formula (2), the sizes of z1, z2, z3 and z4 can be calculated as 0.15, 0.1621, 0.4482 and 0.6 respectively.

[0049] OA line: Point A is the intersection of OA line and AB line, and the corresponding braking intensity is 0.15. The vehicle braking intensity is relatively small, which can effectively ensure the braking safety performance. At this time, in order to maximize the vehicle energy recovery, the total braking force required by the vehicle is preferentially provided by the motor regenerative braking force.

[0050] AB line: As the braking intensity gradually increases, in order to ensure braking safety performance and meet ECE braking regulations, the rear wheels partially participate in the braking process, but the front wheel motor still bears most of the vehicle's braking force.

[0051] BC line: Point B is the intersection of AB line and BC line, and the corresponding braking intensity is 0.1621. As the braking intensity exceeds z2, the braking force distribution curve should meet the BC line in order to obtain more feedback braking energy with braking safety as the premise.

[0052] CD line: Point C is the intersection of the f line and the BC line when the road adhesion coefficient is 0.6, and the corresponding braking intensity is 0.4482. As the braking intensity exceeds z3, the vehicle's braking force can easily exceed the ground limit, and the front wheels may lock and slip. Therefore, in order to ensure braking safety, the vehicle's braking force is distributed according to the CD line at this time, allowing the rear wheels to bear more of the vehicle's braking force.

[0053] I curve: Point D is the intersection of line f and the ideal braking force curve when the road adhesion coefficient is 0.6. When the braking intensity is greater than 0.6, in order to ensure the braking performance of the vehicle, the front and rear wheel braking forces should be distributed along the ideal braking force distribution curve.

[0054] Specifically, the vehicle electromechanical braking force distribution coupling factor is defined as formula (3), that is: determine whether each coupling factor meets the restriction conditions: if at least one meets the restriction, the vehicle braking force is only provided by the mechanical braking system; otherwise, it will be provided by the mechanical braking system and the motor drive system. Further determine whether the braking force distributed to the drive wheel exceeds the maximum electric braking force it can generate. If not, the wheel braking force is only provided by the motor drive braking system; otherwise, the excess is provided by the mechanical braking system. Therefore, the regenerative braking torque actually generated by the motor T act It can be calculated as formula (4).

[0055] (3) Among them, u refers to the real-time speed of the vehicle. k 1. k 2 and k 3 are the vehicle real-time speed, battery SOC, and vehicle braking intensity influencing factors, u l , SOC l andz l are the vehicle real-time speed, battery SOC and the lower threshold of vehicle braking strength, u u , SOC u and z u They are respectively the upper thresholds of the vehicle's real-time speed, battery SOC and vehicle braking intensity.

[0056] (4) in, T reg_max is the maximum regenerative braking torque that the motor can produce.

[0057] Specifically, Figure 1 The active suspension optimization controller shown includes establishing an LQR control framework based on a linear motor, realizing accurate estimation of system transition variables based on a Kalman filter estimation algorithm, optimizing the LQR control weight matrix through a genetic algorithm, and obtaining an active suspension control force of a hub motor-suspension system that is adaptive to vehicle braking conditions.

[0058] Specifically, according to Figure 3 The wheel hub motor-suspension system dynamics model is established, its mathematical model is established, and its spatial state equation is further established, as shown in Equations (5) and (6), respectively. Then, considering the balance between the vehicle vertical comfort and the suspension vibration energy loss, the cost function of the system is defined, as shown in Equation (7).

[0059] (5) in, m b , m sb and m rw They are 1 / 4 of the vehicle's sprung mass, stator-bearing mass, and rotor-tire mass. x z , x rw , x b and x sb They are the vertical displacement of the road surface, the vertical displacement of the rotor-tire, the vertical displacement of the stator-bearing and the vertical displacement of the vehicle body. k t , k sb , k s They are tire stiffness, axle stiffness and suspension stiffness. c t andc s They are tire damping and suspension damping. F r_z is the unbalanced vertical force of the motor, F c It is the main driving force of the suspension.

[0060] (6) in, X is the system state variable, A c and C c is the system state matrix, B u and D u is the system control matrix, U is the system control variable, B ω and D ω is the system interference matrix, ω is the external disturbance variable, Y is the system output variable.

[0061] (7) in, Q and R is the weight matrix of system output variables and control variables, as shown in formula (8), Q d , N d and R d are the system equivalent weight matrices respectively.

[0062] (8) in, q 1 、q 2 、q 3 、q 4 and q 5 represents the weight corresponding to the system output variable, r Represents the weight corresponding to the system control variable.

[0063] Specifically, considering the high nonlinear characteristics of the wheel hub motor-suspension system and the high cost of measuring the system state variables, the present invention uses a Kalman filter estimation algorithm to estimate the system state variables, discretizes equation (6), and obtains equation (9): (9) in, x k is the system state quantity, A cd is the discretized state matrix, B ud is the discretized control matrix, u k is the system control quantity, B ωd is the discretized disturbance matrix (process noise), ω k is the system disturbance, y k is the system measurement, v k is the system measurement noise.

[0064] Specifically, the Kalman filter estimation algorithm mainly includes three parts: prediction, Kalman gain and correction, among which the prediction equation includes two parts: prior estimation and the prior error covariance , the correction equation also consists of two parts: the posterior estimate and the state error covariance P k The prediction equation, Kalman gain and correction equation are shown in equations (10), (11) and (12) respectively: Prediction equation: (10) in, Q d is the process noise covariance.

[0065] Kalman Gain K k : (11) in, R d is the measurement noise covariance.

[0066] Correction equation: (12) in, I is the identity matrix.

[0067] Specifically, the system active suspension force F c It can be expressed as: (13) in, K is the optimal controller gain matrix, which can be expressed as formula (14): (14) Among them, the matrix PIt can be obtained through the Riccati equation: (15) Specifically, considering the high nonlinear characteristics of the system and the complexity of the vehicle operating conditions, the present invention uses a genetic algorithm to optimize the LQR weight matrix under different braking conditions, establishes a multi-index optimization function as shown in formula (16), and realizes adaptive LQR optimization control based on vehicle braking conditions. The genetic algorithm optimization process is as follows: Figure 4 As shown: First, randomly create an initial individual population that satisfies the constraints, that is, q 1 、q 2 、q 3 、q 4 、q 5 and r ; Then, the LQR controller calculates and generates the active suspension control force to the wheel hub motor-suspension system model and outputs the system optimization index; then, the system comprehensive index is calculated through the adaptability function (multi-index optimization function) established by formula (16); then, it is determined whether the termination condition is met: if yes, it ends; if not, it continues to perform genetic operations and regenerates a new individual population. This cycle continues until the individual adaptability function reaches the target value or the algorithm reaches the number of iterations.

[0068] (16) in, RMS ( ), RMS ( x b -x sb )and RMS ( x rw -x z ) represent the optimized RMS values ​​of vehicle vertical acceleration, suspension dynamic deflection and wheel bounce, respectively. Q net is the energy loss of the suspension vibration, RMS (( ) ref ), RMS ( x b -x sb ) ref , RMS ( x rw -x z ) ref andQ net_ref Represent the initial values ​​of the corresponding indicators, ω 1. ω 2. ω 3 and ω 4 represent the weight coefficient of each indicator.

[0069] Specifically, Figure 1 The switch reluctance hub motor coordinated controller shown includes: fuzzy control, PI control and PWM generator, wherein the fuzzy control input is the difference between the reference unbalanced vertical force and the actual unbalanced vertical force E and its rate of change EC , the output is the PI control parameter increment ∆ K p and ∆ K i , realizing adaptive adjustment of PI control parameters; the PI control input is the difference between the reference unbalanced vertical force and the actual unbalanced vertical force and the parameter increment from the fuzzy control, which can adjust the PWM duty cycle signal of the PI control; the PWM generator input signal is the PWM duty cycle signal from the PI control, and the output signal is the chopping signal acting on the motor drive system, which is used to control the motor operation state.

[0070] Specifically, the fuzzy control in the switched reluctance hub motor coordinated controller mainly includes three parts: fuzzy processing, fuzzy reasoning and defuzzification. The fuzzy control process is as follows: First, considering the coupling relationship between vehicle comfort and braking energy, the following is formulated: Figure 5 The fuzzy rules shown in the figure are used; then, the input variables are fuzzified; next, fuzzy reasoning is performed; and finally, the fuzzy control output is obtained by defuzzification.

[0071] Specifically, Figure 1 The motor switch angle optimization controller shown in the figure establishes a multi-index optimization function based on vehicle braking recovery energy, vibration energy loss, vertical acceleration and braking deceleration. The motor drive system control parameters including the opening angle and the closing angle are optimized by genetic algorithm to obtain the motor optimal switch angle signal based on vehicle braking condition adaptation. The calculation formula of the multi-index optimization function is shown in formula (1). The genetic algorithm optimization process can be described as follows: First, randomly create an initial individual population that meets the constraints, that is, θ on and θ off, acts on the motor drive system and outputs the optimization index; then, the system comprehensive index is calculated through the adaptive function (multi-index optimization function) established by formula (1); then, it is determined whether the termination condition is met: if yes, it ends; if not, it continues to perform genetic operations and regenerates a new individual population. This cycle is repeated until the individual adaptive function reaches the target value or the algorithm reaches the number of iterations.

[0072] Through the above steps, according to a vehicle electromechanical coupling control method that takes into account braking comfort and cruising range, the purpose of using the controller in this embodiment to improve the braking comfort and cruising range of a vehicle driven by a hub motor under braking conditions can be achieved.

[0073] The above description is only a preferred specific implementation of the present invention and is not intended to limit the present invention. Any modification, replacement, etc. made to the scheme of the present invention should be within the protection scope of the present invention.

Claims

1. A vehicle electromechanical coupling control method considering braking comfort and cruising range, characterized in that ,include: The acquisition steps include: Get the total braking force of the vehicle; Obtain active suspension control force of the wheel hub motor-suspension system; Get the chopping signal of the motor drive system; Obtain the switch angle signal of the motor drive system; The processing steps include: Based on the total braking force of the vehicle, distributing it to the motor drive system and the mechanical brake system; According to the active suspension control force of the wheel hub motor-suspension system, the vehicle braking vertical dynamics performance and the driving range are coupled to control; Based on the chopping signal of the motor drive system, the vehicle braking comfort and cruising range are coordinated and controlled; According to the switch angle signal of the motor drive system, the braking comfort and cruising range of the vehicle are further coordinated and controlled; The output steps include: The output braking force distribution results, optimized active suspension control force, optimized motor chopping signal and optimized motor switch angle signal can achieve a comprehensive improvement in vehicle braking comfort and cruising range.

2. The method according to claim 1, characterized in that , the obtaining of the total braking force of the vehicle specifically includes: Obtaining the braking condition input by the driver; Based on the braking condition, a total vehicle braking force is determined.

3. The method according to claim 1, characterized in that , the distribution of the total braking force of the vehicle to the motor drive system and the mechanical brake system specifically includes: Combining the motor external characteristics and the ideal braking force distribution curve, a vehicle front and rear axle braking force distribution model based on braking intensity partitioning is established; According to the distribution model, determining the front and rear axle braking force distribution ratio; Based on the real-time driving parameters of the vehicle, the coupling factor of the electromechanical braking force distribution is determined; According to the coupling factor, the coupling distribution of the vehicle electromechanical braking force is performed.

4. The method according to claim 3, characterized in that The vehicle front and rear axle braking force distribution model based on braking intensity partitioning includes: When the vehicle's current braking intensity is between 0 and z 1, the braking force distribution follows the OA line; When the vehicle's current braking strength is z 1 and z 2, the braking force distribution follows the AB line; When the vehicle's current braking strength is z 2 and z 3, the braking force distribution follows the BC line; When the vehicle's current braking strength is z 3 and z When the speed is between 4, the braking force distribution follows the CD line; When the vehicle's current braking strength exceeds z When 4, the braking force distribution follows the I curve; the calculation formula is: ， in, m is the vehicle mass, g is the acceleration due to gravity, z is the vehicle braking strength, F bf and F br is the braking force on the front and rear axles of the vehicle, z 1. z 2. z 3. And z 4 are the braking strengths corresponding to points A, B, C and D, respectively, β max is the maximum front wheel braking force distribution coefficient, φ is the road adhesion coefficient, L is the vehicle wheelbase, b is the distance from the vehicle center of mass to the rear axle, h g is the height of the vehicle's center of mass, z 1. z 2. z 3 and z 4 is the preset braking intensity threshold, z 1. z 2. z 3 and z The sizes of 4 are: 0.15, 0.1621, 0.4482, 0.

6.

5. The method according to claim 3, characterized in that , the method for determining the electromechanical braking force distribution coupling factor includes: Get the vehicle's real-time speed, battery SOC and vehicle braking strength; Based on preset upper and lower thresholds, the vehicle speed, battery SOC and braking intensity are normalized; Based on the normalized results, the computer electric brake force is distributed coupling factor.

6. The method according to claim 1, characterized in that The step of obtaining the active suspension control force of the wheel hub motor-suspension system specifically includes: Establish a linear quadratic regulation LQR control framework based on linear motor; Based on the Kalman filter estimation algorithm, the system state variables are obtained; Optimize the LQR control weight matrix by genetic algorithm; According to the optimized control weight matrix, the active suspension control force of the hub motor-suspension system based on vehicle braking condition adaptation is determined.

7. The method according to claim 6, characterized in that ,The optimization objective function of the LQR control framework includes: The RMS value of the vehicle's vertical acceleration; RMS value of suspension dynamic deflection; RMS value of wheel runout; Suspension vibration energy loss.

8. The method according to claim 1, characterized in that , the obtaining of the motor drive system chopping signal specifically includes: Define the reference unbalanced vertical force and obtain the actual unbalanced vertical force; Formulate fuzzy control rules and determine the increment of PI control parameters; According to the PI control parameter increment, adjusting the PWM duty cycle signal of the PI controller; Based on the PWM duty cycle signal, a motor drive system chopping signal is generated.

9. The method according to claim 8, characterized in that ,The input variables of the fuzzy control rules include: The difference between the reference unbalanced vertical force and the actual unbalanced vertical force; The rate of change of the difference.

10. The method according to claim 1, characterized in that , the step of obtaining the switch angle signal of the motor drive system specifically includes: Establish a multi-index optimization function based on vehicle braking energy recovery, vibration energy loss, vertical acceleration and braking deceleration; The calculation formula of the multi-index optimization function is: (1) in, K opt It is the vehicle performance optimization index. θ on and θ off are the motor turn-on and turn-off angles, RMS (( ) Co )and RMS (( a f ) Co ) represent the optimized RMS values ​​of the vehicle vertical acceleration and braking deceleration, respectively, ( Q net ) Co and Ereg Co Represent the optimization values ​​of suspension vibration energy loss and vehicle braking recovery energy respectively. RMS (( ) LQR ), RMS (( a f ) LQR )、( Q net ) LQR and Ereg LQR Respectively represent the corresponding index values ​​obtained based on the LQR optimization controller, ω 1 and ω 2 is the weight coefficient of vehicle braking comfort and cruising range, α 1 and α 2 is the weight coefficient of the vehicle's vertical acceleration and braking deceleration, β 1 and β 2 is the weight coefficient of suspension vibration energy loss and vehicle braking recovery energy; Optimize the motor drive system control parameters including the turn-on angle and turn-off angle through genetic algorithm; According to the optimization results, the optimal switching angle signal of the motor based on the vehicle braking condition adaptation is determined.

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