A method and system for cooperative braking control of a pure electric commercial vehicle

Abstract

The application provides a kind of pure electric commercial vehicle cooperative braking control method and system, belong to vehicle braking control technical field.The method comprises: real-time estimation road slope based on adaptive extended Kalman filter algorithm;According to brake pedal opening, vehicle mass and road slope, calculate total demand braking torque;With ECE regulation curve as boundary constraint, with maximum rear axle braking torque as optimization target, determine front and rear axle braking force distribution scheme;Adopt model predictive control method, distribute rear axle braking force to motor regenerative braking, hydraulic retarder braking and friction braking;Introduce motor braking dynamic compensation mechanism of feedforward prediction and feedback closed loop compound, dynamically calculate motor compensation torque.The application optimizes braking torque distribution when downhill braking, improves vehicle range while reducing brake drum temperature rise, battery temperature rise and hydraulic retarder shift frequency, reduces torque fault in brake demand mutation process, improves vehicle energy saving, safety and comfort.

Description

Technical Field

[0001] This invention relates to the field of vehicle braking control technology, and more particularly to a cooperative braking control method and system for pure electric commercial vehicles. Background Technology

[0002] Existing pure electric commercial vehicles typically recover some braking energy through regenerative braking of the electric motor, and switch to friction braking when the braking intensity is high, the battery charging capacity is limited, or the vehicle speed is low. For long downhill slopes, heavy loads, or frequent braking conditions, some vehicles are also equipped with hydraulic retarders to provide continuous auxiliary braking torque and reduce the thermal load of friction braking.

[0003] Existing solutions often use fixed thresholds, empirical calibration tables, or simple proportional allocation methods to determine the ratio of regenerative braking, hydraulic retarder braking, and friction braking. While these solutions are simple to implement, they typically rely solely on current vehicle speed, brake pedal opening, and state of charge (SOC), making it difficult to simultaneously consider factors such as battery temperature rise, brake drum temperature rise, road gradient, and vehicle load. Furthermore, existing solutions are mostly designed based on steady-state torque distribution, neglecting the differences in physical response times among the various braking systems. Friction braking suffers from pressure build-up delays, hydraulic retarder charging and discharging processes exhibit significant nonlinear hydraulic inertia, and regenerative braking has an extremely fast torque response.

[0004] In practical use, the above solution is prone to the following shortcomings: First, there is a lack of coordinated braking logic between the hydraulic retarder, brake drum and motor, which can easily lead to insufficient energy recovery, excessive shifting of the hydraulic retarder, and excessively high temperatures of the battery and brake drum. Second, the difference in weight between unloaded and fully loaded commercial vehicles is huge and the road gradient changes in real time. If the front and rear axle braking force distribution is not appropriate, it can easily lead to fishtailing when unloaded or an extended braking distance when fully loaded. Third, there is a lack of dynamic compensation mechanism. In the transient process of sudden changes in braking demand, the total braking torque is prone to break, causing sudden changes in the vehicle's braking deceleration and reducing the vehicle's driving comfort. Summary of the Invention

[0005] The existing technology lacks coordinated braking logic between the hydraulic retarder, brake drum, and motor, which can easily lead to insufficient energy recovery, excessive shifting of the hydraulic retarder, and excessively high temperatures of the battery and brake drum. Furthermore, the significant difference in weight between unloaded and fully loaded commercial vehicles, coupled with real-time changes in road gradient, can result in improper front-to-rear axle braking force distribution, potentially causing unloaded fishtailing or prolonged braking distance under full load. Additionally, the lack of a dynamic compensation mechanism makes it prone to discontinuities in total braking torque during transient processes with sudden changes in braking demand, leading to abrupt changes in vehicle braking deceleration and reduced driving comfort. Therefore, this paper proposes a coordinated braking control method and system for pure electric commercial vehicles. This invention optimizes the braking torque distribution during downhill braking by formulating a front-to-rear axle braking force distribution strategy that considers load and gradient, and a rear axle cooperative braking control strategy based on model predictive control. This improves the overall vehicle range while reducing the temperature rise of the brake drum and battery, as well as the number of gear shifts in the hydraulic retarder. At the same time, in response to the hysteresis characteristics of each braking system, a dynamic compensation mechanism for motor braking is introduced to reduce torque discontinuity during sudden changes in braking demand, increase torque tracking accuracy during braking, and improve the vehicle's energy efficiency, safety, and comfort.

[0006] The technical means employed in this invention are as follows: A cooperative braking control method for pure electric commercial vehicles includes: S1. Collect wheel-end braking force signals, vehicle mass signals, and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, use vehicle longitudinal speed and road slope as system state variables, and combine wheel-end braking force signals, vehicle mass signals, and vehicle speed signals to estimate road slope in real time. S2. Calculate the total required braking torque of the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. S3. Based on the real-time estimated road gradient and deceleration, calculate the braking intensity. Using the front and rear axle braking force distribution curves specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, obtain the front and rear axle braking force distribution curves under different braking intensities, vehicle weights, and road gradients, and determine the front and rear axle braking force distribution scheme. S4. Using model predictive control, the rear axle braking torque is distributed to motor regenerative braking, hydraulic retarder braking and friction braking. S5. Real-time monitoring of the response status of each braking system and the deceleration deviation of the whole vehicle. Dynamically calculate the motor compensation torque to compensate for the torque lag caused by sudden changes in braking demand and dynamically compensate the braking force of the rear axle.

[0007] Further, step S1 includes: S11. Establish the system state-space equations, selecting vehicle longitudinal velocity and road gradient as system state variables, and establish the vehicle longitudinal dynamics equations. Discretize these equations using the forward Euler method to obtain the state transition equations, as follows:

[0008] in, express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; Indicates the sampling time interval; express The longitudinal acceleration of the vehicle at any given moment. ,in express Braking force at the wheel end at all times Indicates the overall vehicle weight. This represents the conversion factor for the rotational mass of a vehicle. Represents gravitational acceleration. express Rolling resistance coefficient at time t, Indicates the air drag coefficient. Indicates the windward area. Indicates air density, express Road gradient at any given time; express Time-based process noise; S12. Selecting vehicle speed and gradient as observation values, the measurement equation is obtained as follows:

[0009] in, express Measurement noise at specific times; S13. Calculate the current prior state using the optimal estimate from the previous time step, and linearize the state transition equation to obtain the Jacobian matrix. Then, the covariance of the prior error is obtained as follows:

[0010] in, It represents the prior error covariance, which is used to measure the uncertainty in the predicted state. It is the process error and the error of the estimated value. This represents the posterior error covariance; Represents the Jacobian matrix; The covariance matrix represents the prediction noise; S14. Perform posterior state update, calculate Kalman gain and posterior error covariance, and obtain the optimal estimate of the slope:

[0011]

[0012]

[0013] in, Kalman gain represents the difference between the predicted and actual values. Represents the measurement matrix. express The covariance of the observed values ​​at time t represents the uncertainty of the state; express The posterior error covariance at time 1. Represents a second-order identity matrix; express The posterior state estimate at time t; express The prior state estimate at time t.

[0014] Furthermore, step S1 also includes: S15. Adaptively update the measurement noise covariance. The noise estimator is as follows:

[0015] in, express The adaptive weighting coefficients at each time step; Indicates the forgetting factor; express Estimation of the mean residual at time step; express The observation vector at time; express The covariance of the observations at time t; express The new information of the moment .

[0016] Further, step S2 includes: S21. Based on the deviation between the target vehicle speed and the actual vehicle speed The brake pedal opening is generated using a PI controller, as follows:

[0017] in, Indicates the brake pedal opening; This indicates the deviation between the target vehicle speed and the actual vehicle speed. Indicates the proportionality coefficient; Indicates the integral coefficient; S22. Based on the brake pedal opening and the maximum ground adhesion, calculate the total braking force requirement using the following formula:

[0018] in, Indicates total braking force demand; Indicates the road surface adhesion coefficient; Indicates the road slope angle; This indicates the rolling radius of the wheel.

[0019] Further, step S3 includes: S31. Calculate the braking intensity under downhill conditions based on the vehicle deceleration and road slope angle, using the following formula:

[0020] in, Indicates braking intensity; Indicates the vehicle's deceleration; S32. Develop curves that meet ECE regulations under both unloaded and fully loaded vehicle operating conditions. The front and rear axle braking force distribution curve constrained by the line group and the ideal front and rear axle braking force distribution curve, wherein: The ECE regulatory curve is as follows:

[0021] The cable set is as follows:

[0022] The ideal front and rear axle braking force distribution curves are as follows:

[0023] In the above formula, Indicates the braking force on the front axle of the vehicle; Indicates the braking force of the vehicle's rear axle; This indicates the distance from the car's center of gravity to the rear axle; Indicates the height of the car's center of gravity; Indicates the weight of the car; Indicates the wheelbase of the car; Indicates the road surface adhesion coefficient; S33. Determine the front and rear axle braking force distribution scheme based on the relationship between braking intensity and the set threshold.

[0024] Further, step S33 includes: When the braking intensity is greater than 0 and less than or equal to 0.1, the ECE regulations do not restrict the distribution of braking force between the front and rear axles. All braking force is provided by the rear axle to improve the energy recovery rate of the motor, and the front axle does not provide braking force. When the braking intensity is greater than 0.1 and less than or equal to 0.25, the ECE regulations begin to impose restrictions on the front and rear axle braking force distribution, with the rear axle braking force remaining unchanged and the front axle braking force gradually increasing; When the braking intensity is greater than 0.25 and less than or equal to 0.7, the braking force of the front and rear axles increases with a line segment with a fixed slope, and the endpoint is the intersection of the equal intensity braking force curve when the braking intensity is equal to 0.7 and the ideal front and rear axle braking force distribution curve. When the braking intensity is greater than 0.7 and less than or equal to 1, the braking force of the front and rear axles increases along a line segment with a fixed slope, ending at the intersection of the equal-intensity braking force curve when the braking intensity is equal to 1 and the ideal front and rear axle braking force distribution curve.

[0025] Further, step S4 includes: S41. Establish a prediction model to predict future vehicle states, determine the prediction time domain and control time domain, and perform rolling derivation to obtain the state prediction sequence within the prediction time domain, specifically including: The prediction model is established as follows;

[0026] Wherein, vehicle speed is the state vector. The regenerative braking torque distribution coefficient and the hydraulic retarder gear position are the control inputs. Battery SOC, temperature, and brake drum temperature are the output variables. Disturbance variables .

[0027] The state equation can be written as:

[0028] in, This indicates the regenerative braking torque of the motor; Indicates the torque of the hydraulic retarder; Indicates the braking torque of the brake drum; The dynamic characteristics of each braking system are represented using a first-order inertial model, as follows:

[0029]

[0030]

[0031] in, , , These represent the response time constants of each braking component; This indicates the braking torque required for the rear axle; set up To control the time domain, For the prediction of the time domain, the state prediction sequence within the future prediction time domain is as follows:

[0032] In the above formula, Represents the state transition function; S42. Construct a cost function that includes multiple control objectives such as maximum regenerative braking, minimum battery and brake drum temperature rise, and minimum hydraulic retarder shift counts. Apply physical characteristic constraints to each braking system to ensure the normal operation of each braking component. Specifically, this includes: Cost function of maximum braking energy recovery Cost function expressed in terms of maximum braking power and minimum battery temperature rise The cost function of minimum brake drum temperature rise, expressed in terms of battery temperature rise rate. The cost function of minimizing the number of gear shifts in a hydraulic retarder, expressed in terms of friction braking power. Expressed as the difference between the constant 1 and the Kronecker function, with the two independent variables being the current gear and the previous gear of the hydraulic retarder, the cost function for each term is:

[0033]

[0034]

[0035]

[0036] in, This indicates the motor's output torque; Indicates the motor speed; Indicates motor efficiency; Indicates wheel speed; Represent the Kronecker function, when and When they are equal, the value is 1; when they are equal, the value is 1. and When they are not equal, the value is 0; Cost function based on maximum braking energy recovery Cost function for minimum battery temperature rise Cost function of minimum brake drum temperature rise Cost function of minimum hydraulic retarder shift times The overall cost function is designed as follows:

[0037] in, , , and These represent the weighting coefficients of each cost function; Considering the limitations imposed by the physical characteristics of the braking system, the constraint functions are as follows:

[0038] in, This indicates the maximum torque of the motor; Indicates the battery's maximum charging power; Indicates the gear ratio of the transmission; Indicates the gear ratio of the main reducer; This indicates the maximum friction braking torque; S43. The optimization problem is solved using dynamic programming in a rolling manner. The first step of the optimal control sequence is sent to the braking system. The state variables are battery SOC and vehicle speed. The optimization objectives are to maximize braking energy recovery, minimize battery temperature rise, minimize brake drum temperature rise, and limit hydraulic retarder shifting. Specifically, this includes: The objective function is described as follows:

[0039] To solve the optimization problem, we first discretize the state and control variables at each stage in the prediction time domain. Then, we work backward from each stage in the prediction time domain to the final stage to solve for the optimal cost function and optimal control value, as follows:

[0040] in, Indicates from the current stage To the final stage The optimal cost function; This represents the optimal control value.

[0041] Furthermore, in step S5, a combined feedforward prediction and feedback closed-loop control is adopted to achieve dynamic compensation of the motor's torque when braking demand changes abruptly. The motor's speed is used to compensate for the slowness of the hydraulic retarder and friction braking. After the braking demand stabilizes, the motor smoothly exits to its original optimized allocation point, including: S51. Feedforward control calculates the transient torque deviation between the desired braking torque and the actual output torque, and adds it to the motor control command, ensuring the system's rapid response. The formula for feedforward control compensation torque is as follows:

[0042] in, Indicates the feedforward compensation torque; This indicates the required braking torque for the rear axle; This indicates the actual output torque of the motor; This indicates the actual output torque of the hydraulic retarder; This indicates the actual output torque of the brake drum; S52. The feedback control adopts gain-adaptive inverse PI control. The feedback correction torque is calculated by the deviation between the desired deceleration and the actual deceleration of the vehicle, ensuring the accuracy and robustness of the system. The formula for the feedback control compensation torque is as follows:

[0043] in, Indicates feedback compensation torque; Indicates the adaptive scaling factor; Indicates the adaptive integral coefficient; This indicates the deviation between the vehicle's expected deceleration and its actual deceleration. S53. An adaptive mechanism is introduced to adjust the PI coefficient value in real time. When the deviation is large, the value is increased. To track quickly and limit Prevent overshoot; when the deviation is small, reduce... To avoid high-frequency oscillations, increase Eliminate steady-state error, and The adaptive value selection formula is as follows:

[0044]

[0045] in, express The minimum value; express The maximum value; and Both represent shape factors; express The maximum value; S54. The feedforward compensation torque and feedback correction torque are superimposed on the steady-state reference target torque of the motor to obtain the final control command of the motor, as follows:

[0046] in, This indicates the final control command for the motor; This represents the steady-state reference target torque of the motor.

[0047] This invention also provides a cooperative braking control system for pure electric commercial vehicles based on the above-mentioned cooperative braking control method for pure electric commercial vehicles, comprising: a slope estimation module, a braking demand calculation module, a front and rear axle braking force distribution module, a rear axle braking force distribution module, and a motor braking dynamic compensation module, wherein: The slope estimation module is used to collect wheel-end braking force signals, vehicle mass signals and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, it uses the vehicle longitudinal speed and road slope as system state variables, and combines the wheel-end braking force signals, vehicle mass signals and vehicle speed signals to estimate the road slope in real time. The braking demand calculation module is used to calculate the total braking torque required by the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. The front and rear axle braking force distribution module is used to calculate the braking intensity based on the real-time estimated road gradient and deceleration. Using the front and rear axle braking force distribution curve specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, it obtains the front and rear axle braking force distribution curves under different braking intensities, vehicle weights and road gradients, and determines the front and rear axle braking force distribution scheme. The rear axle braking force distribution module is used to distribute the rear axle braking torque to the motor regenerative braking, hydraulic retarder braking and friction braking using a model predictive control method. The motor braking dynamic compensation module is used to monitor the response status of each braking system and the deceleration deviation of the whole vehicle in real time. It dynamically calculates the motor compensation torque to compensate the rear axle braking force in response to torque lag caused by sudden changes in braking demand.

[0048] Compared with the prior art, the present invention has the following advantages: 1. The pure electric commercial vehicle cooperative braking control method provided by the present invention uses the vehicle longitudinal speed and road slope as system state variables, and combines the wheel-end braking force signal, vehicle mass signal and vehicle speed signal for real-time estimation, and adaptively updates the measurement noise covariance, thereby achieving accurate and robust estimation of road slope, and providing an accurate data basis for subsequent braking intensity calculation and braking force distribution.

[0049] 2. The cooperative braking control method for pure electric commercial vehicles provided by this invention uses the front and rear axle braking force distribution curves specified in ECE regulations as boundary constraints and maximizes the rear axle braking torque as the optimization objective. It formulates the front and rear axle braking force distribution curves under both unloaded and fully loaded vehicle conditions, and performs segmented braking force distribution based on the relationship between braking intensity and a set threshold. This achieves the maximization of rear axle braking torque while taking into account braking safety regulations, effectively solving the problem of improper front and rear axle braking force distribution caused by the huge difference in the weight of commercial vehicles under unloaded and fully loaded conditions and the real-time changes in road slope. It avoids the safety hazards of tail-wagging under unloaded conditions or extended braking distance under full load conditions.

[0050] 3. The pure electric commercial vehicle cooperative braking control method provided by this invention establishes a predictive model that includes the dynamic characteristics of motor regenerative braking, hydraulic retarder braking, and friction braking. It constructs a multi-objective cost function that includes maximum braking energy recovery, minimum battery temperature rise, minimum brake drum temperature rise, and minimum hydraulic retarder shifting times. It also applies physical characteristic constraints to each braking system and uses dynamic programming to solve the optimization problem in a rolling manner. This achieves the cooperative optimization allocation of motor regenerative braking, hydraulic retarder braking, and friction braking, effectively solving the problem of the lack of cooperative braking logic between the hydraulic retarder, brake drum, and motor. While improving the overall vehicle range, it reduces brake drum temperature rise, battery temperature rise, and hydraulic retarder shifting times.

[0051] 4. The pure electric commercial vehicle cooperative braking control method provided by this invention adopts a composite control strategy of feedforward prediction and feedback closed loop. When braking demand changes abruptly, the rapid response characteristics of the motor are used to compensate for the response lag of the hydraulic retarder and friction brake. The feedforward control achieves rapid response by calculating the transient torque deviation between the expected braking torque and the actual output torque. The feedback control adopts gain-adaptive inverse PI control to calculate and correct the torque through deceleration deviation and ensure robustness. After the braking demand stabilizes, the motor smoothly exits to the original optimized allocation point, realizing dynamic torque compensation during the process of sudden braking demand changes. This effectively solves the problem of total braking torque discontinuity caused by the lack of a dynamic compensation mechanism, reduces sudden braking deceleration, and significantly improves the driving comfort and braking safety of the vehicle.

[0052] In summary, the technical solution of the present invention solves the problems of insufficient energy recovery, excessive shifting of hydraulic retarder, excessively high temperature of battery and brake drum, extended braking distance when unloaded or fully loaded, and sudden changes in vehicle braking deceleration caused by discontinuity in total braking torque when braking demand changes abruptly.

[0053] Based on the above reasons, this invention can be widely applied in fields such as braking control of pure electric commercial vehicles, intelligent driving assistance systems, and vehicle energy management. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the method of the present invention.

[0056] Figure 2 This is a flowchart of the slope recognition process of the present invention.

[0057] Figure 3 This is a diagram showing the relationship between the braking forces of the front and rear axles in this invention.

[0058] Figure 4 This is a flowchart of the rear axle braking force distribution process of the present invention.

[0059] Figure 5 This is a flowchart of the motor dynamic compensation control of the present invention. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0061] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. 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 explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or device.

[0062] like Figure 1 As shown, the present invention provides a cooperative braking control method for pure electric commercial vehicles, comprising: S1. Collect wheel-end braking force signals, vehicle mass signals, and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, use vehicle longitudinal speed and road slope as system state variables, and combine wheel-end braking force signals, vehicle mass signals, and vehicle speed signals to estimate road slope in real time. S2. Calculate the total required braking torque of the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. S3. Based on the real-time estimated road gradient and deceleration, calculate the braking intensity. Using the front and rear axle braking force distribution curves specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, obtain the front and rear axle braking force distribution curves under different braking intensities, vehicle weights, and road gradients, and determine the front and rear axle braking force distribution scheme. S4. Using model predictive control, the rear axle braking torque is distributed to motor regenerative braking, hydraulic retarder braking and friction braking. S5. Real-time monitoring of the response status of each braking system and the deceleration deviation of the whole vehicle. Dynamically calculate the motor compensation torque to compensate for the torque lag caused by sudden changes in braking demand and dynamically compensate the braking force of the rear axle.

[0063] In specific implementation, as a preferred embodiment of the present invention, such as Figure 2 As shown, step S1 includes: S11. Establish the system state-space equations, selecting vehicle longitudinal velocity and road gradient as system state variables, and establish the vehicle longitudinal dynamics equations. Discretize these equations using the forward Euler method to obtain the state transition equations, as follows:

[0064] in, express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; Indicates the sampling time interval; express The longitudinal acceleration of the vehicle at any given moment. ,in express Braking force at the wheel end at all times Indicates the overall vehicle weight. This represents the conversion factor for the rotational mass of a vehicle. Represents gravitational acceleration. express Rolling resistance coefficient at time t, Indicates the air drag coefficient. Indicates the windward area. Indicates air density, express Road gradient at any given time; express Time-based process noise; S12. Selecting vehicle speed and gradient as observation values, the measurement equation is obtained as follows:

[0065] in, express Measurement noise at specific times; S13. Calculate the current prior state using the optimal estimate from the previous time step, and linearize the state transition equation to obtain the Jacobian matrix. Then, the covariance of the prior error is obtained as follows:

[0066] in, It represents the prior error covariance, which is used to measure the uncertainty in the predicted state. It is the process error and the error of the estimated value. This represents the posterior error covariance; Represents the Jacobian matrix; The covariance matrix represents the prediction noise; S14. Perform posterior state update, calculate Kalman gain and posterior error covariance, and obtain the optimal estimate of the slope:

[0067]

[0068]

[0069] in, Kalman gain represents the difference between the predicted and actual values. Represents the measurement matrix. express The covariance of the observed values ​​at time t represents the uncertainty of the state; express The posterior error covariance at time 1. Represents a second-order identity matrix; express The posterior state estimate at time t; express The prior state estimate at time t; S15. Adaptively update the measurement noise covariance. The noise estimator is as follows:

[0070] in, express The adaptive weighting coefficients at each time step; Indicates the forgetting factor, usually ; express Estimation of the mean residual at time step; express The observation vector at time; express The covariance of the observations at time t; express The new information of the moment .

[0071] In a specific implementation, as a preferred embodiment of the present invention, step S2 includes: S21. Based on the deviation between the target vehicle speed and the actual vehicle speed The brake pedal opening is generated using a PI controller, as follows:

[0072] in, Indicates the brake pedal opening; This indicates the deviation between the target vehicle speed and the actual vehicle speed. Indicates the proportionality coefficient; Indicates the integral coefficient; S22. Based on the brake pedal opening and the maximum ground adhesion, calculate the total braking force requirement using the following formula:

[0073] in, Indicates total braking force demand; Indicates the road surface adhesion coefficient; Indicates the road slope angle; This indicates the rolling radius of the wheel.

[0074] In specific implementation, as a preferred embodiment of the present invention, such as Figure 3 As shown, step S3 includes: S31. Calculate the braking intensity under downhill conditions based on the vehicle deceleration and road slope angle, using the following formula:

[0075] in, Indicates braking intensity; Indicates the vehicle's deceleration; S32. Develop curves that meet ECE regulations under both unloaded and fully loaded vehicle operating conditions. The front and rear axle braking force distribution curve constrained by the line group and the ideal front and rear axle braking force distribution curve, wherein: The ECE regulatory curve is as follows:

[0076] The cable set is as follows:

[0077] The ideal front and rear axle braking force distribution curves are as follows:

[0078] In the above formula, Indicates the braking force on the front axle of the vehicle; Indicates the braking force on the rear axle of the vehicle; This indicates the distance from the car's center of gravity to the rear axle; Indicates the height of the car's center of gravity; Indicates the weight of the car; Indicates the wheelbase of the car; Indicates the road surface adhesion coefficient; S33. Based on the relationship between braking intensity and a set threshold, determine the front and rear axle braking force distribution scheme, including: When the braking intensity is greater than 0 and less than or equal to 0.1, the ECE regulations do not restrict the distribution of braking force between the front and rear axles. All braking force is provided by the rear axle to improve the energy recovery rate of the motor, and the front axle does not provide braking force. When the braking intensity is greater than 0.1 and less than or equal to 0.25, the ECE regulations begin to impose restrictions on the front and rear axle braking force distribution, with the rear axle braking force remaining unchanged and the front axle braking force gradually increasing; When the braking intensity is greater than 0.25 and less than or equal to 0.7, the braking force of the front and rear axles increases with a line segment with a fixed slope, and the endpoint is the intersection of the equal intensity braking force curve when the braking intensity is equal to 0.7 and the ideal front and rear axle braking force distribution curve. When the braking intensity is greater than 0.7 and less than or equal to 1, the braking force of the front and rear axles increases along a line segment with a fixed slope, ending at the intersection of the equal-intensity braking force curve when the braking intensity is equal to 1 and the ideal front and rear axle braking force distribution curve.

[0079] In specific implementation, as a preferred embodiment of the present invention, such as Figure 4 As shown, in step S4, the nonlinear problem of rear axle braking force distribution under multiple constraints is transformed into an online solution problem within a finite time using the model predictive control method, including: S41. Establish a prediction model to predict future vehicle states, determine the prediction time domain and control time domain, and perform rolling derivation to obtain the state prediction sequence within the prediction time domain, specifically including: The prediction model is established as follows;

[0080] Wherein, vehicle speed is the state vector. The regenerative braking torque distribution coefficient and the hydraulic retarder gear position are the control inputs. Battery SOC, temperature, and brake drum temperature are the output variables. Disturbance variables .

[0081] The state equation can be written as:

[0082] in, This indicates the regenerative braking torque of the motor; Indicates the torque of the hydraulic retarder; Indicates the braking torque of the brake drum; The dynamic characteristics of each braking system are represented using a first-order inertial model, as follows:

[0083]

[0084]

[0085] in, , , These represent the response time constants of each braking component; This indicates the braking torque required for the rear axle; set up To control the time domain, For the prediction of the time domain, the state prediction sequence within the future prediction time domain is as follows:

[0086] In the above formula, Represents the state transition function; S42. Construct a cost function that includes multiple control objectives such as maximum regenerative braking, minimum battery and brake drum temperature rise, and minimum hydraulic retarder shift counts. Apply physical characteristic constraints to each braking system to ensure the normal operation of each braking component. Specifically, this includes: Cost function of maximum regenerative braking Cost function expressed in terms of maximum braking power and minimum battery temperature rise The cost function of minimum brake drum temperature rise, expressed in terms of battery temperature rise rate. The cost function of minimizing the number of gear shifts in a hydraulic retarder, expressed in terms of friction braking power. Expressed as the difference between the constant 1 and the Kronecker function, with the two independent variables being the current gear and the previous gear of the hydraulic retarder, the cost function for each term is:

[0087]

[0088]

[0089]

[0090] in, This indicates the motor's output torque; Indicates the motor speed; Indicates motor efficiency; Indicates wheel speed; Represent the Kronecker function, when and When they are equal, the value is 1; when they are equal, the value is 1. and When they are not equal, the value is 0; Cost function based on maximum braking energy recovery Cost function for minimum battery temperature rise Cost function of minimum brake drum temperature rise Cost function of minimum hydraulic retarder shift times The overall cost function is designed as follows:

[0091] in, , , and These represent the weighting coefficients of each cost function; Considering the limitations imposed by the physical characteristics of the braking system, the constraint functions are as follows:

[0092] in, This indicates the maximum torque of the motor; Indicates the battery's maximum charging power; Indicates the gear ratio of the transmission; Indicates the transmission ratio of the main reducer; This indicates the maximum friction braking torque; S43. The optimization problem is solved using dynamic programming in a rolling manner. The first step of the optimal control sequence is sent to the braking system. The state variables are battery SOC and vehicle speed. The optimization objectives are to maximize braking energy recovery, minimize battery temperature rise, minimize brake drum temperature rise, and limit hydraulic retarder shifting. Specifically, this includes: The objective function is described as follows:

[0093] To solve the optimization problem, we first discretize the state and control variables at each stage in the prediction time domain. Then, we work backward from each stage in the prediction time domain to the final stage to solve for the optimal cost function and optimal control value, as follows:

[0094] in, Indicates from the current stage To the final stage The optimal cost function; This represents the optimal control value.

[0095] In specific implementation, as a preferred embodiment of the present invention, such as Figure 5 As shown, in step S5, a combined feedforward prediction and feedback closed-loop control is adopted to achieve dynamic compensation of the motor's torque when braking demand changes abruptly. The motor's speed is used to compensate for the slowness of the hydraulic retarder and friction braking. After the braking demand stabilizes, the motor smoothly exits to the original optimized allocation point, including: S51. Feedforward control calculates the transient torque deviation between the desired braking torque and the actual output torque, and adds it to the motor control command, ensuring the system's rapid response. The formula for feedforward control compensation torque is as follows:

[0096] in, Indicates the feedforward compensation torque; This indicates the required braking torque for the rear axle; This indicates the actual output torque of the motor; This indicates the actual output torque of the hydraulic retarder; This indicates the actual output torque of the brake drum; S52. The feedback control adopts gain-adaptive inverse PI control. The feedback correction torque is calculated by the deviation between the desired deceleration and the actual deceleration of the vehicle, ensuring the accuracy and robustness of the system. The formula for the feedback control compensation torque is as follows:

[0097] in, Indicates feedback compensation torque; Indicates the adaptive scaling factor; Indicates the adaptive integral coefficient; This indicates the deviation between the vehicle's expected deceleration and its actual deceleration. S53. An adaptive mechanism is introduced to adjust the PI coefficient value in real time. When the deviation is large, the value is increased. To track quickly and limit Prevent overshoot; when the deviation is small, reduce... To avoid high-frequency oscillations, increase Eliminate steady-state error, and The adaptive value selection formula is as follows:

[0098]

[0099] in, express The minimum value; express The maximum value; and Both represent shape factors; express The maximum value; S54. The feedforward compensation torque and feedback correction torque are superimposed on the steady-state reference target torque of the motor to obtain the final control command of the motor, as follows:

[0100] in, This indicates the final control command for the motor; This represents the steady-state reference target torque of the motor.

[0101] This invention also provides a cooperative braking control system for pure electric commercial vehicles based on the above-mentioned cooperative braking control method for pure electric commercial vehicles, comprising: a slope estimation module, a braking demand calculation module, a front and rear axle braking force distribution module, a rear axle braking force distribution module, and a motor braking dynamic compensation module, wherein: The slope estimation module is used to collect wheel-end braking force signals, vehicle mass signals and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, it uses the vehicle longitudinal speed and road slope as system state variables, and combines the wheel-end braking force signals, vehicle mass signals and vehicle speed signals to estimate the road slope in real time. The braking demand calculation module is used to calculate the total braking torque required by the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. The front and rear axle braking force distribution module is used to calculate the braking intensity based on the real-time estimated road gradient and deceleration. Using the front and rear axle braking force distribution curve specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, it obtains the front and rear axle braking force distribution curves under different braking intensities, vehicle weights and road gradients, and determines the front and rear axle braking force distribution scheme. The rear axle braking force distribution module is used to distribute the rear axle braking torque to the motor regenerative braking, hydraulic retarder braking and friction braking using a model predictive control method. The motor braking dynamic compensation module is used to monitor the response status of each braking system and the deceleration deviation of the whole vehicle in real time. It dynamically calculates the motor compensation torque to compensate the rear axle braking force in response to torque lag caused by sudden changes in braking demand.

[0102] The embodiments of the present invention are described simply because they correspond to those in the embodiments above. For any similarities, please refer to the descriptions in the embodiments above, which will not be elaborated here.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated braking control of a pure electric commercial vehicle, characterized in that, include: S1. Collect wheel-end braking force signals, vehicle mass signals, and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, use vehicle longitudinal speed and road slope as system state variables, and combine wheel-end braking force signals, vehicle mass signals, and vehicle speed signals to estimate road slope in real time. S2. Calculate the total required braking torque of the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. S3. Based on the real-time estimated road gradient and deceleration, calculate the braking intensity. Using the front and rear axle braking force distribution curves specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, obtain the front and rear axle braking force distribution curves under different braking intensities, vehicle weights, and road gradients, and determine the front and rear axle braking force distribution scheme. S4. Using model predictive control, the rear axle braking torque is distributed to motor regenerative braking, hydraulic retarder braking and friction braking. S5. Real-time monitoring of the response status of each braking system and the deceleration deviation of the whole vehicle. Dynamically calculate the motor compensation torque to compensate for the torque lag caused by sudden changes in braking demand and dynamically compensate the braking force of the rear axle.

2. The method for coordinated braking control of a pure electric commercial vehicle according to claim 1, characterized in that, Step S1 includes: S11. Establish the system state-space equations, selecting vehicle longitudinal velocity and road gradient as system state variables, and establish the vehicle longitudinal dynamics equations. Discretize these equations using the forward Euler method to obtain the state transition equations, as follows: in, express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; express The longitudinal velocity of the vehicle at any given moment; express Road gradient at any given time; Indicates the sampling time interval; express The longitudinal acceleration of the vehicle at any given moment. ,in express Braking force at the wheel end at all times Indicates the overall vehicle weight. This represents the conversion factor for the rotational mass of a vehicle. Represents gravitational acceleration. express Rolling resistance coefficient at time t, Indicates the air drag coefficient. Indicates the windward area. Indicates air density, express Road gradient at any given time; express Time-based process noise; S12. Selecting vehicle speed and gradient as observation values, the measurement equation is obtained as follows: in, express Measurement noise at specific times; S13. Calculate the current prior state using the optimal estimate from the previous time step, and linearize the state transition equation to obtain the Jacobian matrix. Then, the covariance of the prior error is obtained as follows: in, It represents the prior error covariance, which is used to measure the uncertainty in the predicted state. It is the process error and the error of the estimated value. This represents the posterior error covariance; Represents the Jacobian matrix; The covariance matrix represents the prediction noise; S14. Perform posterior state update, calculate Kalman gain and posterior error covariance, and obtain the optimal estimate of the slope: in, Kalman gain represents the difference between the predicted and actual values. Represents the measurement matrix. express The covariance of the observed values ​​at time t represents the uncertainty of the state; express The posterior error covariance at time 1. Represents a second-order identity matrix; express The posterior state estimate at time t; express The prior state estimate at time t.

3. The method for coordinated braking control of a pure electric commercial vehicle according to claim 2, characterized in that, Step S1 also includes: S15. Adaptively update the measurement noise covariance. The noise estimator is as follows: in, express The adaptive weighting coefficients at each time step; Indicates the forgetting factor; express Estimation of the mean residual at time step; express The observation vector at time; express The covariance of the observations at time t; express The new information of the moment .

4. The method for coordinated braking control of a pure electric commercial vehicle according to claim 1, characterized in that, Step S2 includes: S21. Based on the deviation between the target vehicle speed and the actual vehicle speed The brake pedal opening is generated using a PI controller, as follows: in, Indicates the brake pedal opening; This indicates the deviation between the target vehicle speed and the actual vehicle speed. Indicates the proportionality coefficient; Indicates the integral coefficient; S22. Based on the brake pedal opening and the maximum ground adhesion, calculate the total braking force requirement using the following formula: in, Indicates total braking force demand; Indicates the road surface adhesion coefficient; Indicates the road slope angle; This indicates the rolling radius of the wheel.

5. The method for coordinated braking control of a pure electric commercial vehicle according to claim 1, characterized in that, Step S3 includes: S31. Calculate the braking intensity under downhill conditions based on the vehicle deceleration and road slope angle, using the following formula: in, Indicates braking intensity; Indicates the vehicle's deceleration; S32. Develop curves that meet ECE regulations under both unloaded and fully loaded vehicle operating conditions. The front and rear axle braking force distribution curve constrained by the line group and the ideal front and rear axle braking force distribution curve, wherein: The ECE regulatory curve is as follows: The cable set is as follows: The ideal front and rear axle braking force distribution curves are as follows: In the above formula, Indicates the braking force on the front axle of the vehicle; Indicates the braking force on the rear axle of the vehicle; This indicates the distance from the car's center of gravity to the rear axle; Indicates the height of the car's center of gravity; Indicates the weight of the car; Indicates the wheelbase of the car; Indicates the road surface adhesion coefficient; S33. Determine the front and rear axle braking force distribution scheme based on the relationship between braking intensity and the set threshold.

6. The method for coordinated braking control of a pure electric commercial vehicle according to claim 5, characterized in that, Step S33 includes: When the braking intensity is greater than 0 and less than or equal to 0.1, the ECE regulations do not restrict the distribution of braking force between the front and rear axles. All braking force is provided by the rear axle to improve the energy recovery rate of the motor, and the front axle does not provide braking force. When the braking intensity is greater than 0.1 and less than or equal to 0.25, the ECE regulations begin to impose restrictions on the front and rear axle braking force distribution, with the rear axle braking force remaining unchanged and the front axle braking force gradually increasing; When the braking intensity is greater than 0.25 and less than or equal to 0.7, the braking force of the front and rear axles increases with a line segment with a fixed slope, and the endpoint is the intersection of the equal intensity braking force curve when the braking intensity is equal to 0.7 and the ideal front and rear axle braking force distribution curve. When the braking intensity is greater than 0.7 and less than or equal to 1, the braking force of the front and rear axles increases along a line segment with a fixed slope, ending at the intersection of the equal-intensity braking force curve when the braking intensity is equal to 1 and the ideal front and rear axle braking force distribution curve.

7. The method for coordinated braking control of a pure electric commercial vehicle according to claim 1, characterized in that, Step S4 includes: S41. Establish a prediction model to predict future vehicle states, determine the prediction time domain and control time domain, and perform rolling derivation to obtain the state prediction sequence within the prediction time domain, specifically including: The prediction model is established as follows; Wherein, vehicle speed is the state vector. The regenerative braking torque distribution coefficient and the hydraulic retarder gear position are the control inputs. Battery SOC, temperature, and brake drum temperature are the output variables. Disturbance variables . The state equation can be written as: in, This indicates the regenerative braking torque of the motor; Indicates the torque of the hydraulic retarder; Indicates the braking torque of the brake drum; The dynamic characteristics of each braking system are represented using a first-order inertial model, as follows: in, , , These represent the response time constants of each braking component; This indicates the braking torque required for the rear axle; set up To control the time domain, For the prediction of the time domain, the state prediction sequence within the future prediction time domain is as follows: In the above formula, Represents the state transition function; S42. Construct a cost function that includes multiple control objectives such as maximum regenerative braking, minimum battery and brake drum temperature rise, and minimum hydraulic retarder shift counts. Apply physical characteristic constraints to each braking system to ensure the normal operation of each braking component. Specifically, this includes: Cost function of maximum regenerative braking Cost function expressed in terms of maximum braking power and minimum battery temperature rise The cost function of minimum brake drum temperature rise, expressed in terms of battery temperature rise rate. The cost function of minimizing the number of gear shifts in a hydraulic retarder, expressed in terms of friction braking power. Expressed as the difference between the constant 1 and the Kronecker function, with the two independent variables being the current gear and the previous gear of the hydraulic retarder, the cost function for each term is: in, This indicates the motor's output torque; Indicates the motor speed; Indicates motor efficiency; Indicates wheel speed; Represent the Kronecker function, when and When they are equal, the value is 1; when they are equal, the value is 1. and When they are not equal, the value is 0; Cost function based on maximum braking energy recovery Cost function for minimum battery temperature rise Cost function of minimum brake drum temperature rise Cost function of minimum hydraulic retarder shift times The overall cost function is designed as follows: in, , , and These represent the weighting coefficients of each cost function; Considering the limitations imposed by the physical characteristics of the braking system, the constraint functions are as follows: in, This indicates the maximum torque of the motor; Indicates the battery's maximum charging power; Indicates the gear ratio of the transmission; Indicates the transmission ratio of the main reducer; This indicates the maximum friction braking torque; S43. The optimization problem is solved using dynamic programming in a rolling manner. The first step of the optimal control sequence is sent to the braking system. The state variables are battery SOC and vehicle speed. The optimization objectives are to maximize braking energy recovery, minimize battery temperature rise, minimize brake drum temperature rise, and limit hydraulic retarder shifting. Specifically, this includes: The objective function is described as follows: To solve the optimization problem, we first discretize the state and control variables at each stage in the prediction time domain. Then, we work backward from each stage in the prediction time domain to the final stage to solve for the optimal cost function and optimal control value, as follows: in, Indicates from the current stage To the final stage The optimal cost function; This represents the optimal control value.

8. The method for coordinated braking control of a pure electric commercial vehicle according to claim 1, characterized in that, In step S5, a combined feedforward prediction and feedback closed-loop control is adopted to achieve dynamic compensation of the motor's torque when braking demand changes abruptly. The motor's speed is used to compensate for the slowness of the hydraulic retarder and friction braking. After the braking demand stabilizes, the motor smoothly exits to the original optimized allocation point, including: S51. Feedforward control calculates the transient torque deviation between the desired braking torque and the actual output torque, and adds it to the motor control command, ensuring the system's rapid response. The formula for feedforward control compensation torque is as follows: in, Indicates the feedforward compensation torque; This indicates the required braking torque for the rear axle; This indicates the actual output torque of the motor; This indicates the actual output torque of the hydraulic retarder; This indicates the actual output torque of the brake drum; S52. The feedback control adopts gain-adaptive inverse PI control. The feedback correction torque is calculated by the deviation between the desired deceleration and the actual deceleration of the vehicle, ensuring the accuracy and robustness of the system. The formula for the feedback control compensation torque is as follows: in, Indicates feedback compensation torque; Indicates the adaptive scaling factor; Indicates the adaptive integral coefficient; This indicates the deviation between the vehicle's expected deceleration and its actual deceleration. S53. An adaptive mechanism is introduced to adjust the PI coefficient value in real time. When the deviation is large, the value is increased. To track quickly and limit Prevent overshoot; when the deviation is small, reduce... To avoid high-frequency oscillations, increase Eliminate steady-state error, and The adaptive value selection formula is as follows: in, express The minimum value; express The maximum value; and Both represent shape factors; express The maximum value; S54. The feedforward compensation torque and feedback correction torque are superimposed on the steady-state reference target torque of the motor to obtain the final control command of the motor, as follows: in, This indicates the final control command for the motor; This represents the steady-state reference target torque of the motor.

9. A cooperative braking control system for a pure electric commercial vehicle based on the cooperative braking control method for pure electric commercial vehicles according to any one of claims 1-8, characterized in that, include: The system includes a slope estimation module, a braking demand calculation module, a front and rear axle braking force distribution module, a rear axle braking force distribution module, and a motor braking dynamic compensation module, among which: The slope estimation module is used to collect wheel-end braking force signals, vehicle mass signals and vehicle speed signals. Based on the adaptive extended Kalman filter algorithm, it uses the vehicle longitudinal speed and road slope as system state variables, and combines the wheel-end braking force signals, vehicle mass signals and vehicle speed signals to estimate the road slope in real time. The braking demand calculation module is used to calculate the total braking torque required by the vehicle based on the real-time estimated road gradient, combined with the brake pedal opening and the vehicle weight. The front and rear axle braking force distribution module is used to calculate the braking intensity based on the real-time estimated road gradient and deceleration. Using the front and rear axle braking force distribution curve specified by ECE regulations as boundary constraints and maximizing the rear axle braking torque as the optimization objective, it obtains the front and rear axle braking force distribution curves under different braking intensities, vehicle weights and road gradients, and determines the front and rear axle braking force distribution scheme. The rear axle braking force distribution module is used to distribute the rear axle braking torque to the motor regenerative braking, hydraulic retarder braking and friction braking using a model predictive control method. The motor braking dynamic compensation module is used to monitor the response status of each braking system and the deceleration deviation of the whole vehicle in real time. It dynamically calculates the motor compensation torque to compensate the rear axle braking force in response to torque lag caused by sudden changes in braking demand.

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