Hybrid commercial vehicle energy recovery control method, system and equipment and storage medium

Abstract

The invention relates to the technical field of new energy commercial vehicles, and particularly provides a hybrid commercial vehicle energy recovery control method, system and device and a storage medium, and the method comprises the steps: collecting and processing multi-source information including brake pedal opening, vehicle state and chassis system signals in real time, and obtaining predictive road condition information in front of the vehicle; identifying the intention of a driver according to the multi-source information and the predictive road condition information; whether energy recovery is allowed or not is judged based on the intention of the driver and the pre-recognized driving style, the optimal motor negative torque is calculated through a model prediction control algorithm when energy recovery is allowed, and the torque coefficient is subjected to self-adaptive correction in combination with the load and the road condition obtained in real time; and based on the optimal motor negative torque and the self-adaptive corrected torque coefficient, collaborative distribution of motor braking force and hydraulic braking force is carried out, and an execution instruction is output. Through multi-source information fusion and predictive control, the comprehensive performance of the hybrid commercial vehicle energy recovery system is effectively improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy commercial vehicles, and particularly relates to a hybrid commercial vehicle energy recovery control method, system, device and storage medium. BACKGROUND

[0002] In the field of hybrid commercial vehicles, energy recovery using motor braking is a key technology for improving energy efficiency. Existing methods mainly adjust the recovery torque based on the brake pedal opening, and the strategy is relatively simple. However, the operating conditions of commercial vehicles are complex, and the load and slope change significantly. This simple control method is easy to cause the brake torque output to be inconsistent with the driver's expectation, causing problems such as over-braking or insufficient braking force, which seriously affects the driving comfort and safety of the vehicle. At the same time, due to the failure to comprehensively consider the front road conditions and the individualized operation habits of the driver, the intelligence and adaptability of the system are insufficient, which limits the further improvement of the energy recovery efficiency. Therefore, there is an urgent need for a new energy recovery method that can integrate multi-source information and achieve precise forward-looking control. SUMMARY

[0003] In view of the above shortcomings of the prior art, the application provides a hybrid commercial vehicle energy recovery control method, system, device and storage medium to solve the above technical problems.

[0004] In a first aspect, the application provides a hybrid commercial vehicle energy recovery control method, comprising: real-time collection and processing of multi-source information including brake pedal opening, vehicle state, chassis system signal, to obtain predictive road condition information within a preset distance in front of the vehicle; identifying the driver's intention according to the multi-source information and the predictive road condition information; judging whether to allow energy recovery based on the driver's intention and the pre-identified driving style, and calculating the optimal motor negative torque using a model predictive control algorithm when allowed, and adaptively correcting the torque coefficient in combination with the real-time obtained load and road conditions; based on the optimal motor negative torque and the adaptively corrected torque coefficient, the motor braking force and the hydraulic braking force are cooperatively distributed, and the execution instruction is output.

[0005] In an optional embodiment, obtaining the predictive road condition information within a preset distance in front of the vehicle comprises: accessing the vehicle-mounted navigation and electronic horizon system to obtain the predicted path profile information within a preset distance in front of the vehicle; The predicted path profile information at least includes road slope sequence and road curvature information.

[0006] In an optional embodiment, identifying the driver's intention according to the multi-source information and the predictive road condition information comprises: analyzing the road slope sequence in the predictive road condition information, and if a slope with an absolute value continuously exceeding a preset threshold and a length exceeding a specific distance is identified in front, it is determined that a scenario causing natural change of vehicle speed exists; In combination with the scenario, the brake pedal opening and its change rate collected in real time are interpreted to distinguish the driver braking intention, and the braking intention categories include slight deceleration, regular braking, emergency braking, or intention to maintain vehicle speed; The judgment condition for the intention to maintain vehicle speed is that when the brake pedal opening is small and the change rate is low, and the scenario causing natural change of vehicle speed exists, the braking intention is judged as the intention to maintain vehicle speed.

[0007] In an optional embodiment, the driving style recognition method comprises: calling a pre-trained driver style model; The input features of the driver style model include at least one of the average change rate of the accelerator pedal, the statistical value of the braking deceleration, the cruise speed preference, and the energy recovery use frequency in the historical driving data; The driver style model classifies the driving habits of the driver into style types including economical, comfortable, or aggressive through a clustering algorithm; The driver style model outputs personalized control parameter weights corresponding to each style type, and the control parameter weights at least include comfort weight and economy weight for the model predictive control algorithm.

[0008] In an optional embodiment, whether to allow energy recovery is determined based on the driver intention and the pre-identified driving style, comprising: Basic conditions are judged, and the basic conditions at least include that the transmission is in gear, the battery SOC is lower than the upper limit of charging, the motor and transmission system are fault-free, and the motor speed is within the range in which energy recovery can be performed; If the basic conditions are not met, dynamic optimization is performed in combination with the pre-identified driving style and the predictive road condition information; if the driving style is economical, the motor speed is preferentially attempted to be made to meet the recovery condition by performing downshift operation; Final arbitration is performed based on the driver intention; if the driver intention is identified as emergency braking, energy recovery is prohibited, and the hydraulic braking system is preferentially ensured to provide all braking force.

[0009] In an optional embodiment, the optimal motor negative torque is calculated by using the model predictive control algorithm, and the torque coefficient is adaptively corrected in combination with the real-time obtained load and road condition, comprising: initializing a model predictive controller, loading a prediction model including a vehicle longitudinal dynamics model and a battery model; In each control cycle, starting from the current vehicle state, combining the predictive road condition information and the control parameter weight corresponding to the current driving style type, a rolling optimization is performed in a limited time domain in the future; wherein the objective function of the optimization aims to maximize the energy recovery efficiency, minimize the braking impact, and match the actual total braking force provided by the system with the total braking force demand of the driver; Solving the optimization problem of the objective function, outputting a series of future optimal motor negative torque sequences starting from the current time, and selecting the first control as the optimal motor requested negative torque value at the current time; According to the real-time obtained vehicle load value, the basic negative torque coefficient is determined by table lookup method; Based on real-time road condition information and pre-identified driving style, the basic negative torque coefficient is dynamically corrected; wherein the correction factor at least includes: slope correction factor, which increases the coefficient when descending and decreases the coefficient when ascending; adhesion correction factor, which reduces the coefficient when identifying low adhesion road surface; driver style correction factor, which configures higher coefficient weight for economic driving style.

[0010] In an optional embodiment, based on the optimal motor negative torque and the adaptively corrected torque coefficient, the motor braking force and the hydraulic braking force are cooperatively distributed, and the execution instruction is output, including: The optimal motor negative torque is multiplied by the adaptively corrected final negative torque coefficient, and the product is converted into wheel-end required electric braking force acting on the wheel according to the current transmission system speed ratio; The total braking force demand predicted by the model predictive control algorithm is compared with the wheel-end required electric braking force, and the difference part is compensated by the hydraulic braking system; Real-time monitoring of the electric braking system state, if the electric braking failure is monitored, the total braking force demand is switched to the hydraulic braking system within milliseconds; at the same time, the final determined motor negative torque instruction and the hydraulic braking force instruction are sent to the motor controller and the brake-by-wire system respectively.

[0011] In the second aspect, the application provides a hybrid commercial vehicle energy recovery control system, comprising: A data acquisition module is used for real-time acquisition and processing of multi-source information including brake pedal opening, vehicle state, chassis system signal, and obtaining predictive road condition information within a preset distance in front of the vehicle; An intention recognition module is used for recognizing the driver's intention according to the multi-source information and the predictive road condition information; A parameter calculation module is configured to determine whether to allow energy recovery based on the driver intention and the pre-identified driving style, and calculate an optimal motor negative torque by using a model predictive control algorithm when energy recovery is allowed, and to adaptively correct the torque coefficient in combination with the real-time obtained load and road conditions; An instruction execution module is configured to perform collaborative distribution of motor braking force and hydraulic braking force based on the optimal motor negative torque and the adaptively corrected torque coefficient, and output an execution instruction.

[0012] In a third aspect, a device is provided, comprising: A memory is configured to store a hybrid commercial vehicle energy recovery control program; A processor is configured to implement the steps of the hybrid commercial vehicle energy recovery control method provided in the first aspect when executing the hybrid commercial vehicle energy recovery control program.

[0013] In a fourth aspect, a computer-readable storage medium is provided, and the storage medium stores a hybrid commercial vehicle energy recovery control program. The hybrid commercial vehicle energy recovery control program, when executed by a processor, implements the steps of the hybrid commercial vehicle energy recovery control method provided in the first aspect.

[0014] The hybrid commercial vehicle energy recovery control method, system, device and storage medium provided by the present application have the beneficial effects that, through multi-source information fusion and predictive control, the comprehensive performance of the hybrid commercial vehicle energy recovery system is effectively improved. The beneficial effects mainly include: 1) significantly improving driving comfort, smoothing torque output by the model predictive control algorithm, and effectively avoiding over-braking by combining with driver intention recognition, to ensure linear and natural braking process; 2) maximizing energy recovery efficiency, adaptively correcting torque based on the front road conditions and real-time load, to realize as much energy recovery as possible under the premise of safety; 3) enhancing system safety and adaptability, through intelligent collaborative distribution and safety arbitration mechanism of electric-hydraulic braking, to ensure braking stability under various conditions, and the system has self-learning ability and high intelligence level to adapt to different driving styles. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0016] Figure 1 is a schematic flowchart of a method of an embodiment of the present application.

[0017] Figure 2 is a schematic block diagram of a system of an embodiment of the present application.

[0018] Figure 3 A structural schematic diagram of a device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the scope of protection of the present application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing the specific embodiments only and is not intended to be limiting of the application.

[0021] The hybrid commercial vehicle energy recovery control method provided by the embodiment of the present application is executed by a computer device, and accordingly, the hybrid commercial vehicle energy recovery control system runs in the computer device.

[0022] Figure 1 is a schematic flowchart of the method of an embodiment of the present application. Wherein, Figure 1 The execution subject can be a hybrid commercial vehicle energy recovery control system. According to different needs, the order of steps in the flowchart can be changed, and some can be omitted.

[0023] As Figure 1 shown, the method comprises: S1. Real-time acquisition and processing of multi-source information including brake pedal opening degree, vehicle state, chassis system signal, acquisition of predictive road condition information within a preset distance in front of the vehicle; S2. Identifying the driver's intention according to the multi-source information and the predictive road condition information; S3. Based on the driver's intention and the pre-identified driving style, judging whether to allow energy recovery, and when allowed, calculating the optimal motor negative torque using the model predictive control algorithm, and combining the real-time acquired load and road condition to adaptively correct the torque coefficient; S4. Based on the optimal motor negative torque and the adaptively corrected torque coefficient, performing collaborative distribution of motor braking force and hydraulic braking force, and outputting execution instructions.

[0024] In an embodiment of the present application, based on step S1, a possible embodiment will be given below to non-limitingly describe its specific implementation scheme.

[0025] S101, Real-time acquisition and processing of multi-source information: The system acquires raw signals in real time through the vehicle CAN bus network and dedicated sensors with a sampling frequency of not less than 100 Hz. Specifically, it includes: Driver operation signals: The brake pedal opening is obtained through the brake pedal position sensor, and the controller calculates the rate of change per unit time (i.e., the first derivative).

[0026] Vehicle real-time state signals: Vehicle speed, current gear, and calculated vehicle load value are obtained from the vehicle control unit (VCU); motor speed is obtained from the motor controller (MCU); battery SOC and temperature are obtained from the battery management system (BMS).

[0027] Chassis system signals: Wheel slip ratio and internal estimated road adhesion coefficient are obtained through the electronic stability control system (ESC). The acquired raw signals are first processed through a first-order low-pass digital filter (such as a Butterworth filter with a cutoff frequency of 10 Hz) to suppress high-frequency noise, and finally output a set of verified, highly reliable real-time state vectors.

[0028] S102, Acquisition of predictive road condition information: The system accesses the built-in high-precision map and electronic horizon system through the vehicle gateway. The current GPS coordinates and driving direction of the vehicle are sent to the electronic horizon system to request the predictive path profile information within a predetermined distance (e.g., 1-3 kilometers) in front of the vehicle. The data structure returned by the system at least includes: Road slope sequence: Describes the height change of the road in front of the vehicle at fixed intervals (e.g., one point every 10 meters).

[0029] Road curvature information: Identifies the radius and position of the curve in front of the vehicle.

[0030] This predictive path profile information will be used as the feedforward input of the model predictive control (MPC) algorithm to provide the controller with advanced road condition perception capability.

[0031] In one embodiment of the present application, based on step S2, a possible embodiment will be given below to illustrate the specific implementation of the non-limiting embodiment.

[0032] After obtaining the predictive path profile information in step S102, the system first analyzes the road slope sequence. Specifically, the system sets a slope absolute value threshold (e.g., 2%) and a distance threshold (e.g., 200 meters). When the algorithm detects that there is a slope in front of the vehicle with a continuous distance of more than 200 meters and a slope absolute value of more than 2%, it is determined that the vehicle will enter a scenario that causes the vehicle speed to change naturally (e.g., natural acceleration on a long downhill or natural deceleration on a long uphill).

[0033] Subsequently, the system fuses and interprets this scene judgment result with the real-time collected brake pedal opening and its rate of change. The system internally pre-stores judgment logic for distinguishing intentions, for example: If the brake pedal opening is greater than 70% and the rate of change is extremely high, regardless of the road conditions, it is determined to be emergency braking.

[0034] The key improvement lies in the identification of "intention to maintain vehicle speed": when the system has determined that there is a scene in front of the vehicle that causes natural changes in vehicle speed (such as a long downhill slope), and at the same time detects that the brake pedal opening applied by the driver is small (for example, less than 15%) and the rate of change is low, the system will not simply interpret this operation as slight deceleration, but intelligently judge that the driver's true intention is "intention to maintain vehicle speed", i.e. wants to offset the natural change in vehicle speed through slight braking intervention, rather than intending to significantly reduce the vehicle speed.

[0035] Through this analysis method of combining forward-looking road conditions with real-time operations, the system can more accurately grasp the driver's true intention, providing more intelligent input for subsequent torque decision-making, fundamentally avoiding over-braking or insufficient braking caused by misjudgment, and significantly improving driving comfort and safety. The above threshold values can be calibrated and adjusted according to vehicle type and user preferences.

[0036] The system internally pre-stores calibrated threshold values for interpreting and classifying real-time pedal signals: Judgment logic for emergency braking: Conditions: When the instantaneous value of the brake pedal opening is greater than 70% and its rate of change is higher than a higher threshold value (for example, more than 100% / s), the system immediately determines this behavior as emergency braking.

[0037] Technical meaning: This indicates that the driver is rapidly and forcefully pressing the brake pedal, expecting the vehicle to obtain maximum deceleration. Under this intention, the system will prioritize absolute braking efficiency and stability, usually inhibiting or completely prohibiting energy recovery, and fully activating the hydraulic braking system.

[0038] Judgment logic for regular braking: Conditions: When the brake pedal opening is between 20% and 70% and the rate of change is in a moderate range, it is determined to be regular braking.

[0039] Technical meaning: This represents the normal deceleration or parking operation of the driver under predictable conditions. This is the main working condition of the energy recovery system, and the system will smoothly apply motor negative torque under this intention, and coordinate with the hydraulic brake to balance comfort and recovery efficiency.

[0040] Judgment logic for slight deceleration: Condition: When the brake pedal opening is less than 20% and the rate of change is very low, it is judged as slight deceleration.

[0041] Technical implications: This typically corresponds to slight adjustments when following the vehicle in front in traffic flow or early coasting when approaching an intersection. With this intention, the system applies a small, gentle negative torque from the electric motor, primarily for energy recovery and maintaining a stable vehicle speed, providing gentle braking force.

[0042] It should be noted that the above percentage thresholds are all calibrable parameters and can be adjusted according to the brake pedal characteristics and driving style preferences of different vehicle models.

[0043] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0044] S301. Determine whether energy recovery is allowed based on the driver's intention and the pre-identified driving style.

[0045] First, regarding driver style recognition: The system continuously runs a driver style model in the background. This model can be a pre-trained model based on historical driving data using the K-means clustering algorithm. The model's input feature vector is extracted from historical trip data, including but not limited to: the average rate of change of the accelerator pedal (reflecting acceleration habits), the average braking deceleration (reflecting braking habits), the preferred cruise speed, and the frequency of use of the energy recovery function. Through cluster analysis, the model categorizes the driver's operating habits into typical "economy," "comfort," or "aggressive" types. For example, a driver who frequently uses coasting recovery and prefers smooth acceleration would be labeled as "economy." The model outputs not only type labels but, more importantly, a set of personalized control parameter weights. These weights directly affect the subsequent Model Predictive Control (MPC) algorithm. For example, a higher economy weight is set for "economy" drivers to encourage more energy recovery; a higher comfort weight is set for "comfort" drivers to prioritize braking smoothness.

[0046] Secondly, regarding the access criteria for energy recovery: this criterion involves a three-level decision-making process: Basic condition judgment: Within each control cycle (e.g., 10ms), the system first checks a set of basic hardware and status conditions. This includes: confirming that the transmission is not in neutral (in gear), the battery SOC is below the 95% charging limit, there are no fault codes read from the motor controller and transmission system, and the motor speed is above the minimum speed threshold for allowable energy recovery (e.g., 800RPM). This is the "hard" threshold for energy recovery.

[0047] Dynamic optimization judgment: When the above basic conditions are not met, the system does not simply prohibit recovery, but intelligently optimizes in combination with driving style and predictive road condition information. For example, if the condition is not met due to low motor speed, but the system identifies that the driver's style is "economy type", and there is a long downhill road ahead, the system will preferentially try to send a downshift request to the transmission controller. By downshifting to increase the engine and motor speed, it enters the high-efficiency recovery interval, thereby actively creating conditions for energy recovery, maximizing energy recovery opportunities.

[0048] Final arbitration: After all conditions are met, the system makes a final arbitration based on the highest priority driver's intention. Once the real-time intention is identified as "emergency braking", the system will immediately prohibit energy recovery. This is because there may be a slight delay in the response and exit of the motor brake torque, which may interfere with braking stability in extreme cases. At this time, the system will prioritize ensuring that the hydraulic braking system provides all braking force without interference, ensuring the highest safety.

[0049] S302. Calculate the optimal motor negative torque using the model predictive control algorithm when allowed, and adaptively correct the torque coefficient in combination with the real-time obtained load and road conditions.

[0050] When the system is initialized, the MPC controller is loaded with an integrated prediction model, which includes a vehicle dynamics model that describes the longitudinal motion of the vehicle (its state variables can include vehicle speed, acceleration, etc.) and a battery model that represents the battery charging characteristics.

[0051] In each control cycle (e.g. 10ms), the MPC controller performs the following rolling optimization process: The filtered current vehicle state (such as vehicle speed, motor speed, battery SOC, etc.) is used as the initial state of the optimization.

[0052] Predictive road condition information (such as the slope sequence and curvature ahead) is input as known feedforward disturbance into the prediction model.

[0053] The personalized control parameter weights obtained from the driving style recognition results are integrated into the objective function. For example, for "economy type", increase the weight of the "energy recovery efficiency" term; for "comfort type", significantly increase the weight of the "minimize braking jerk" term.

[0054] In a limited future time domain (e.g. 5 seconds in the future), a constrained optimization problem is solved. The objective function of this problem aims to optimize three objectives simultaneously: maximize energy recovery efficiency, minimize braking jerk to improve comfort, and match the actual total braking force provided by the system to the total braking force demand of the driver.

[0055] Define variables, model, and timing concepts involved in the optimization problem. Optimization is performed within a finite prediction horizon, and only the optimal solution at the current time instant is implemented: State variables: Internal variables that describe the dynamic behavior of the system. We define the state vector as: , is the current vehicle speed; SOC(k) is the current battery state of charge.

[0056] Control variables: Inputs that the system can actively adjust. Here, mainly: , Motor negative torque requested at the current time instant (negative value, indicating braking / energy recovery).

[0057] Disturbance variables: Known or predictable external influences. This is the key innovation point, utilizing navigation information: , represents the road slope at future time instant k provided by the high-definition map.

[0058] Prediction horizon: The length of the future time window within which we perform optimization, denoted as N. For example, if the control period is 0.1 seconds, N=50 implies a prediction of 5 seconds into the future.

[0059] The objective function is the core of MPC, which quantifies "good" control performance. The three proposed optimization objectives (multi-objective optimization) are integrated into a scalar function that can be solved. The weighted sum method is adopted:

[0060] where , , are weight coefficients used to balance the relative importance of the three objectives. Their setting is crucial and can be dynamically adjusted based on the driver's style (for example, a comfort-oriented driver will assign a greater weight to Maximizing the electrical energy recovered into the battery is approximately equivalent to maximizing the recovered power of the motor , where is the motor speed, and η is a coefficient considering the efficiency of the motor and inverter. Since the speed is relatively stable in a short time, it can be simplified to maximize the absolute value of the recovered power. To express it as a minimization problem in optimization, take its negative value:

[0061] Jerk is the rate of change of deceleration. The vehicle deceleration a(k) is directly proportional to the total braking force (Newton's second law). The total braking force is determined by the motor negative torque and slope, among others. Therefore, minimizing the jerk is equivalent to minimizing the drastic change in motor torque between adjacent control periods:

[0062] Total braking force demand of the driver This can be calibrated by brake pedal opening and its rate of change. The goal of MPC is to make the actual total braking force closely track this demand. The square of the deviation is used as a penalty term:

[0063] Constraints ensure that the optimization solution is within the physically realizable and safe range. These are hard constraints that must be satisfied during the solving process. Specifically include: Battery SOC constraint: the battery state of charge cannot exceed the safe range, and the recuperation power should be limited when the SOC is close to the upper limit.

[0064] Vehicle speed constraint: the vehicle speed must be greater than zero and cannot exceed the speed limit.

[0065] Motor capability constraint: the motor torque cannot exceed the negative torque limit specified by its external characteristic curve, and the torque rate of change also has a physical upper limit.

[0066] Tire adhesion constraint: the total braking force cannot exceed the ground adhesion force to prevent wheel lock.

[0067] The above problem constructs a nonlinear multi-objective optimization problem with constraints. The specific solving method depends on the complexity of the model: Quadratic programming: if the system model (vehicle dynamics, battery model) can be linearized near the operating point, and the objective function is a quadratic form of control variables and state variables, then the problem can be transformed into a standard quadratic programming problem. QP has a large number of mature and efficient computing libraries that can be solved (such as OSQP, qpOASES), which is very suitable for real-time control of vehicles.

[0068] Nonlinear programming: if the model is strongly nonlinear and cannot be effectively linearized, then nonlinear programming methods such as sequential quadratic programming or interior point methods are needed. These methods have larger computational load and require higher computational capacity for the controller, but have better accuracy.

[0069] The steps of the "rolling horizon" strategy are as follows: 1. At time k, measure or estimate the current state x(k).

[0070] 2. Based on the current state, solve the above finite horizon optimization problem within the prediction horizon N to obtain the optimal control sequence .

[0071] 3. Only the first control quantity in the sequence is applied to the system (i.e. request the motor to output this torque).

[0072] 4. To next control period k+1, system state is updated, repeat step 1-3.

[0073] After solving this optimization problem, an optimal motor negative torque sequence [T k ,T k+1 ,...,T k+N ] in future time domain is output. The system adopts a “rolling horizon” strategy, only taking the first control amount T k in the sequence as the optimal motor negative torque output at the current time, for the next step execution.

[0074] Secondly, about the adaptive correction of torque coefficient: In order to convert the theoretical optimal torque calculated by MPC into an execution instruction that is more in line with the actual working condition, the system performs the following adaptive correction: Determine the base coefficient: the system obtains the real-time estimated vehicle load value from the vehicle controller (VCU) through the CAN bus, and obtains a base negative torque coefficient K base by querying a pre-set calibration table according to the value. The calibration table ensures the basic braking safety under different loads.

[0075] Dynamic multi-factor correction: the system then introduces multiple correction factors to dynamically adjust K base , forming the final negative torque coefficient K final , whose calculation can be expressed as: K final =K base ×f grade ×f adhesion ×f style .

[0076] Gradient correction factor (f grade ): when the electronic horizon system indicates that there is a long downhill ahead, f grade takes a value greater than 1 (such as 1.1 to 1.3) to enhance the energy recovery strength and assist in controlling the vehicle speed; when climbing uphill, it takes a value less than 1 (such as 0.7 to 0.9) to avoid unnecessary braking intervention.

[0077] Adhesion correction factor (f adhesion ): when the road adhesion coefficient estimated value obtained from the ESC system is lower than the low adhesion threshold (such as 0.3), f adhesion will take a value much smaller than 1 (such as 0.5) to significantly reduce the motor braking force, and to prevent wheel lock first, to ensure braking stability.

[0078] Driver style correction factor (f style ): for “economical” drivers, f styleIt can be set to slightly larger than 1 (such as 1.05) to encourage more energy recovery; for other styles, it remains 1.

[0079] In an embodiment of the present application, based on step S4, a possible embodiment will be given below to specifically illustrate the non-limiting embodiment.

[0080] S401. Wheel end demand electric braking force calculation The system multiplies the optimal motor negative torque T motor_req (output by the model predictive control (MPC) algorithm) (unit: Nm) and the final negative torque coefficient K final obtained after multi-factor adaptive correction, to obtain a motor torque instruction T motor_adj = T motor_req × K final that has been adjusted according to the working condition. Subsequently, according to the current total transmission system speed ratio i total (including the transmission gear ratio and the main reduction ratio) provided by the transmission controller (TCU) and the transmission efficiency η (usually taking a value of 0.95-0.98), the torque is converted into a wheel end demand electric braking force F motor_req (unit: N) acting on the drive wheel, and the calculation formula is:

[0081] wherein r wheel is the rolling radius of the vehicle tire (unit: m).

[0082] S402. Hydraulic braking compensation force calculation and collaborative distribution The system simultaneously obtains the total driver braking force demand F total_req (predicted by the MPC algorithm) (unit: N). F total_req is compared with the calculated F motor_req : If F motor_req <F total_req , it indicates that the motor braking force is insufficient, and the difference ΔF = F total_req -F motor_req will be compensated by the hydraulic braking system. The system sends a request to the electronic hydraulic brake system (EHB / ESC) to generate a corresponding hydraulic braking force F hydraulic_req = ΔF.

[0083] If F motor_req ≥ F total_req , it indicates that the motor braking force can independently meet the braking demand, and F hydraulic_req = 0 is set at this time, and the braking force is completely provided by the motor.

[0084] S403. Safety arbitration and instruction execution The system monitors the electric braking system state in real time through a controller area network (CAN) bus or a hard-wired signal, including fault codes of a motor controller (MCU), communication states and torque feedback values. If the electric braking system fails (such as the MCU error, the actual torque and the command torque deviation exceeds the limit or the communication timeout), the safety arbitration module will trigger the fault protection strategy in a very short time (for example, less than 50 milliseconds). The strategy will immediately set the motor negative torque command to zero, and synchronously distribute all braking force demand F total_req to the hydraulic braking system, ensuring that the braking efficiency is not interrupted and the vehicle safety is guaranteed.

[0085] In the normal case without failure, the system will finally determine the T motor_adj command sent to the motor controller, and the F hydraulic_req command sent to the line control braking system, so as to realize the precise, smooth and safe collaborative control of the electric-hydraulic braking force.

[0086] In some embodiments, the hybrid commercial vehicle energy recovery control system can include a plurality of function modules composed of computer program segments. The computer programs of each program segment in the hybrid commercial vehicle energy recovery control system can be stored in the memory of the computer device and executed by at least one processor to perform the functions of the hybrid commercial vehicle energy recovery control (see Figure 1 for detailed description).

[0087] In this embodiment, the hybrid commercial vehicle energy recovery control system can be divided into a plurality of function modules according to the functions it performs, as shown in Figure 2 . The module referred to in the present application refers to a series of computer program segments that can be executed by at least one processor and can complete a fixed function, which are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0088] The data acquisition module is used to acquire and process multiple source information in real time, including brake pedal opening, vehicle state, chassis system signal, and predictive road condition information within a preset distance in front of the vehicle; The intention recognition module is used to recognize the driver's intention according to the multiple source information and the predictive road condition information; The parameter calculation module is used to judge whether to allow energy recovery based on the driver's intention and the pre-recognized driving style, and calculate the optimal motor negative torque when allowed, and adaptively correct the torque coefficient combined with the real-time acquired load and road condition; The instruction execution module is used to perform collaborative distribution of motor braking force and hydraulic braking force based on the optimal motor negative torque and the adaptively corrected torque coefficient, and output the execution instruction.

[0089] Figure 3 The hybrid commercial vehicle energy recovery control method provided by the embodiments of the present application can be applied to a device. Those skilled in the art can understand that the device structure involved in the embodiments of the present application does not constitute a limitation on the device, and the device can include more or fewer components than shown, or combine certain components, or different component arrangements. In the embodiments of the present application, the device includes but is not limited to a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The device can also represent various forms of mobile devices, such as a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0090] The device 300 can include a processor 310, a memory 320, and a communication unit 330. These components communicate through one or more buses, and those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation on the present application, and it can be a bus structure or a star structure, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0091] The present application also provides a computer storage medium, wherein the computer storage medium can store a program, and the program can include some or all steps in the embodiments provided by the present application when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0092] Those skilled in the art can clearly understand that the technology in the embodiments of the present application can be realized by means of software and necessary general hardware platforms. Based on such understanding, the technical solutions in the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. Various storage media that can store program codes include a number of instructions to make a computer device (which can be a personal computer, a server, or a second device, a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application.

[0093] The same or similar parts among various embodiments in the specification can be referred to each other. Especially, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0094] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, another division manner can be used. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed systems or modules can be indirect coupling or communication connection through some interfaces. The coupling or communication connection can be electrical, mechanical or other forms.

[0095] The modules described as separate components can or can not be physically separated. The components displayed as modules can or can not be physical modules, i.e. can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0096] In addition, the functional modules in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0097] Although the present application is described in detail with reference to the preferred embodiments by referring to the accompanying drawings, the present application is not limited thereto. Various equivalent modifications or replacements can be made to the embodiments of the present application by those skilled in the art without departing from the spirit and essence of the present application, and these modifications or replacements should be within the scope of the present application. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, and these changes or replacements should be within the protection scope of the present application.

Claims

1. A hybrid commercial vehicle energy recovery control method, characterized by, The method comprises the following steps: Real-time acquisition and processing of multi-source information including brake pedal opening, vehicle state, chassis system signal, to obtain predictive road condition information within a preset distance in front of the vehicle; Identifying the driver's intention according to the multi-source information and the predictive road condition information; Based on the driver's intention and the pre-identified driving style, it is judged whether energy recovery is allowed, and when allowed, the optimal motor negative torque is calculated using the model predictive control algorithm, and the torque coefficient is adaptively corrected in combination with the real-time obtained load and road condition; Based on the optimal motor negative torque and the adaptively corrected torque coefficient, the motor braking force and the hydraulic braking force are cooperatively distributed, and the execution instruction is output.

2. The method of claim 1, wherein, The method for obtaining the predictive road condition information within a preset distance in front of the vehicle comprises the following steps: Accessing the vehicle-mounted navigation and electronic horizon system to obtain the predicted path profile information within a preset distance in front of the vehicle; The predicted path profile information at least includes road slope sequence and road curvature information.

3. The method of claim 1, wherein, The method for identifying the driver's intention according to the multi-source information and the predictive road condition information comprises the following steps: Analyzing the road slope sequence in the predictive road condition information, if a slope with an absolute value continuously exceeding a preset threshold and a length exceeding a certain distance is identified in front of the vehicle, it is determined that there is a scenario causing natural change of vehicle speed; In combination with the scenario, the real-time collected brake pedal opening and its change rate are interpreted to distinguish the driver's braking intention, the braking intention categories include slight deceleration, regular braking, emergency braking or intention to maintain vehicle speed; Wherein, the judgment condition of intention to maintain vehicle speed is: when the brake pedal opening is small and the change rate is low, and there is a scenario causing natural change of vehicle speed, the braking intention is judged as intention to maintain vehicle speed.

4. The method of claim 1, wherein, The method for identifying the driving style comprises the following steps: Calling a pre-trained driver style model; The input features of the driver style model include at least one of the average change rate of the accelerator pedal, the statistical value of the braking deceleration, the cruise speed preference and the energy recovery use frequency in the historical driving data; The driver style model classifies the driver's operation habit into style types including economy type, comfort type or aggressive type through clustering algorithm; The driver style model outputs personalized control parameter weights corresponding to each style type, the control parameter weights at least include comfort weight and economy weight for model predictive control algorithm.

5. The method of claim 4, wherein, The method for judging whether energy recovery is allowed based on the driver's intention and the pre-identified driving style comprises the following steps: Performing basic condition judgment, the basic conditions at least include: the gearbox is in gear state, the battery SOC is lower than the upper limit of charging, the motor and transmission system are fault-free, and the motor speed is within the range where energy recovery can be executed; If the basic conditions are not met, dynamic optimization judgment is performed in combination with the pre-identified driving style and the predictive road condition information; if the driving style is economy type, it is preferred to try to execute downshift operation to make the motor speed meet the recovery condition; Performing final arbitration based on the driver's intention; if the driver's intention is identified as emergency braking, energy recovery is prohibited, and the hydraulic braking system is preferred to provide all braking force.

6. The method of claim 4, wherein, An optimal motor negative torque is calculated by using a model predictive control algorithm, and a torque coefficient is adaptively corrected in combination with a real-time acquired load and road condition, including: An initialized model predictive controller is loaded with a predictive model including a vehicle longitudinal dynamics model and a battery model; In each control period, starting from the current vehicle state, in combination with predictive road condition information and control parameter weights corresponding to the current driving style type, a rolling optimization is performed in a limited time domain in the future; wherein the objective function of the optimization aims to maximize energy recovery efficiency, minimize braking impact, and match the actual total braking force provided by the system with the total braking force demand of the driver; An optimization problem of the objective function is solved, and a series of future optimal motor negative torque sequences starting from the current time are output, and the first control quantity is selected as the optimal motor requested negative torque value at the current time; According to the real-time acquired vehicle load value, the basic negative torque coefficient is determined by table lookup method; Based on real-time road condition information and pre-identified driving style, the basic negative torque coefficient is dynamically corrected; wherein the correction factor at least includes: a slope correction factor, which increases the coefficient when descending and decreases the coefficient when ascending; an adhesion correction factor, which reduces the coefficient when identifying a low adhesion road surface; and a driver style correction factor, which configures a higher coefficient weight for an economic driving style.

7. The method of claim 6, wherein, Based on the optimal motor negative torque and the adaptively corrected torque coefficient, the motor braking force and the hydraulic braking force are cooperatively distributed, and an execution instruction is output, including: The optimal motor negative torque is multiplied by the adaptively corrected final negative torque coefficient, and the product is converted into a wheel end demand electric braking force acting on the wheel according to the current transmission system speed ratio; The total braking force demand predicted by the model predictive control algorithm is compared with the wheel end demand electric braking force, and the difference is compensated by the hydraulic braking system; Real-time monitoring of the electric braking system state, if the electric braking failure is monitored, the total braking force demand is switched to the hydraulic braking system within milliseconds; at the same time, the final determined motor negative torque instruction and the hydraulic braking force instruction are sent to the motor controller and the brake-by-wire system respectively.

8. A hybrid commercial vehicle energy recovery control system, characterized by, Including: A data acquisition module for real-time acquisition and processing of multi-source information including brake pedal opening, vehicle state, chassis system signal, and acquisition of predictive road condition information within a preset distance in front of the vehicle; An intention recognition module for recognizing the driver's intention based on the multi-source information and predictive road condition information; A parameter calculation module for determining whether energy recovery is allowed based on the driver's intention and pre-identified driving style, and calculating an optimal motor negative torque by using a model predictive control algorithm when allowed, in combination with real-time acquired load and road condition for adaptive correction of the torque coefficient; An instruction execution module for cooperatively distributing the motor braking force and the hydraulic braking force based on the optimal motor negative torque and the adaptively corrected torque coefficient, and outputting an execution instruction.

9. A hybrid commercial vehicle energy recovery control device, characterized by comprising: Including: A memory for storing a hybrid commercial vehicle energy recovery control program; A processor for executing the hybrid commercial vehicle energy recovery control program to implement the steps of the hybrid commercial vehicle energy recovery control method of any one of claims 1-7.

10. A computer readable storage medium storing a computer program, characterized in that, A readable storage medium having a hybrid commercial vehicle energy recovery control program stored thereon, the hybrid commercial vehicle energy recovery control program, when executed by a processor, implements the steps of the hybrid commercial vehicle energy recovery control method of any one of claims 1-7.

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