Trailer mass self-learning method and device based on energy recovery closed loop

Abstract

The invention provides a trailer mass self-learning method and device based on an energy recovery closed loop. The method comprises the following steps: determining whether energy recovery state monitoring data generated by a tractor in a preset sliding window meets a mass estimation triggering condition or not; if the energy recovery state monitoring data meets the mass estimation triggering condition, vehicle operation data generated by the tractor in a preset time window are collected, and a mass estimation sequence is formed; carrying out trailer mass calculation on the mass estimation sequence based on an energy conservation principle to obtain first trailer mass; carrying out trailer mass calculation on the mass estimation sequence by adopting a recursive least square method with a forgetting factor based on the kinetic energy change of the whole vehicle to obtain second trailer mass; and performing consistency arbitration on the first trailer quality and the second trailer quality, and determining the effective trailer quality corresponding to the trailer based on the blanking result. Through real-time online learning of the trailer quality, automatic and accurate estimation of the trailer quality is realized, and the efficiency and safety of subsequent energy management are guaranteed.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a trailer quality self-learning method and device based on energy recovery closed loop. Background Technology

[0002] In existing vehicle towing technologies, trailer mass is a core input parameter for regenerative energy management and control, directly determining regenerative torque, power limit, braking force distribution, and SOC balancing strategy, ultimately affecting regeneration efficiency, braking safety, and range performance. Trailer mass typically relies on manual input by the driver or the addition of extra sensors (such as pressure or weight sensors), which increases operational complexity and cost.

[0003] Furthermore, traditional methods struggle to update trailer mass in real time during vehicle operation, especially in electric vehicles where energy recovery during coasting or braking is crucial for improving energy efficiency. However, inaccurate trailer mass directly impacts the optimization of energy recovery strategies. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide at least one trailer mass self-learning method and device based on energy recovery closed loop, which reduces costs and achieves automatic and accurate estimation of trailer mass through real-time online learning of trailer mass, thereby ensuring the efficiency and safety of subsequent energy recovery management.

[0005] This application mainly includes the following aspects: In a first aspect, embodiments of this application provide a trailer mass self-learning method based on an energy recovery closed loop. The method includes: determining whether the energy recovery status monitoring data generated by the tractor within a preset sliding window meets the mass estimation trigger condition, wherein the mass estimation trigger condition indicates that the vehicle is in a stable coasting energy recovery state; if the energy recovery status monitoring data meets the mass estimation trigger condition, then collecting vehicle operation data generated by the tractor within a preset time window and forming a mass estimation sequence; calculating the trailer mass of the mass estimation sequence based on the principle of energy conservation to obtain a first trailer mass; calculating the trailer mass of the mass estimation sequence using a recursive least squares method with a forgetting factor based on the change in the vehicle's kinetic energy to obtain a second trailer mass; and performing consistency arbitration on the first trailer mass and the second trailer mass, and determining the effective trailer mass corresponding to the trailer based on the arbitration result.

[0006] In one possible implementation, the energy recovery status monitoring data includes the gear position of the tractor, accelerator pedal opening, brake pedal opening, battery allowable charging power, steering wheel angle, longitudinal acceleration, and altitude. The mass estimation trigger conditions include: the gear is forward, the accelerator pedal opening is 0, the brake pedal opening is 0, the battery allowable charging power is greater than a preset power threshold, the absolute value of the steering wheel angle is less than a preset angle threshold, the longitudinal acceleration is greater than a preset lower acceleration limit and less than a preset upper acceleration limit, and the altitude corresponding to the start time point and the end time point within the preset sliding window are the same. The preset lower acceleration limit and the preset upper acceleration limit are determined by the design acceleration under the energy recovery level of the tractor.

[0007] In one possible implementation, the vehicle operating data includes DC power data corresponding to the battery management system. The step of calculating the trailer mass based on the energy conservation principle to obtain the first trailer mass includes: establishing an energy conservation equation between the recovered electrical energy and the change in the vehicle's kinetic energy.

[0008] This represents the total mechanical energy output by the motor. Indicates the energy conversion coefficient. This represents the energy consumed by the tractor to overcome resistance, taking into account air resistance, rolling resistance, and gradient resistance. This represents the change in vehicle kinetic energy within a preset sliding window, determined by the total mass of the tractor and the vehicle's speed. The energy conservation equation is expanded using a mass estimation sequence to obtain the core energy balance equation concerning the tractor's mass. The core energy balance equation is solved to obtain the first total mass corresponding to the tractor and trailer. The first trailer mass corresponding to the trailer is determined from the first total mass.

[0009] In one possible implementation, the total mechanical energy output by the motor is determined by: performing discrete integration on the DC power data to obtain the cumulative recovered energy of the battery pack within a preset sliding window; and determining the total mechanical energy output by the motor shaft as the product of the cumulative recovered energy, the fixed efficiency coefficient under high-voltage line loss, the average or integral equivalent value of the inverter within a preset time window, and the average or integral equivalent value of the motor efficiency within a preset time window.

[0010] In one possible implementation, the change in vehicle kinetic energy is determined using the following formula:

[0011]

[0012] in, This represents the change in the vehicle's kinetic energy. This indicates the initial gross vehicle weight (GVW) of the tractor and trailer. This indicates the initial vehicle speed corresponding to the preset sliding window start time. This indicates the end speed corresponding to the preset end time of the sliding window. This indicates the final speed after correction to account for minor slope interference. This represents the correction factor, which is determined by looking up a table based on the standard deviation of vehicle speed within a preset sliding window. This indicates the design acceleration at the energy recovery level. Indicates the preset duration of the sliding window.

[0013] In one possible implementation, the energy consumed to overcome resistance is determined by the following formula:

[0014]

[0015]

[0016] in, Indicates the time of the tractor air resistance below, Indicates the time of the tractor The rolling resistance below, Indicates the time of the tractor Decrease the vehicle speed, Indicates air density, Indicates the drag coefficient. Indicates the frontal area of ​​the tractor unit. This indicates the initial gross vehicle weight (GVW) of the tractor and trailer. Represents gravitational acceleration. This represents the rolling resistance coefficient.

[0017] In one possible implementation, the step of determining the first trailer mass corresponding to the trailer from the first total mass includes: determining the difference between the first total mass and the half-loaded mass of the vehicle as the first trailer mass; or, determining the difference between the first total mass and the target mass estimated based on the suspension travel height as the first trailer mass.

[0018] In one possible implementation, the step of calculating the trailer mass using a recursive least squares method with a forgetting factor based on the change in the vehicle's kinetic energy to obtain the second trailer mass includes: constructing a linear regression equation based on the trailer's longitudinal force balance formula and the mass estimation sequence.

[0019] in, , Representing the observed quantity, This represents the motor recovery torque of the tractor unit at time K. This indicates the reduction ratio from the tractor motor to the wheel end. This indicates the radius of the tractor's wheels. Indicates the air drag coefficient. , This indicates the speed of the tractor unit at time K. Represents the regression vector. , This represents the longitudinal acceleration of the tractor unit at time K. Represents gravitational acceleration. Represents the vector of parameters to be estimated. , This indicates the second gross vehicle weight corresponding to the tractor and trailer. This represents the composite rolling resistance parameter. The system noise at time K is represented; the linear regression equation is recursively estimated, and the second total mass corresponding to the tractor and trailer is extracted from the parameter estimates corresponding to the parameter vector to be estimated obtained after the linear regression equation converges; the second trailer mass corresponding to the trailer is determined from the second total mass.

[0020] In one possible implementation, the step of arbitrating the consistency of the first trailer mass and the second trailer mass, and determining the effective trailer mass corresponding to the trailer based on the arbitration result, includes: calculating the absolute difference between the first trailer mass and the second trailer mass; if the absolute difference is greater than or equal to a preset mass tolerance, then the effective trailer mass corresponding to the trailer is not updated; if the absolute difference is less than the preset mass tolerance, then the weighted average between the first trailer mass and the second trailer mass is taken as the effective trailer mass of the trailer and updated.

[0021] Secondly, this application also provides a trailer mass self-learning device based on an energy recovery closed loop. The device includes: a trigger determination module, used to determine whether the energy recovery status monitoring data generated by the tractor within a preset sliding window meets the mass estimation trigger condition, wherein the mass estimation trigger condition indicates that the vehicle is in a stable coasting energy recovery state; a data acquisition module, used to acquire vehicle operation data generated by the tractor within a preset time window and form a mass estimation sequence if the energy recovery status monitoring data meets the mass estimation trigger condition; a first calculation module, used to calculate the trailer mass of the mass estimation sequence based on the principle of energy conservation to obtain a first trailer mass; a second calculation module, used to calculate the trailer mass of the mass estimation sequence based on the change of vehicle kinetic energy using a recursive least squares method with a forgetting factor to obtain a second trailer mass; and an arbitration module, used to arbitrate the consistency between the first trailer mass and the second trailer mass, and determine the effective trailer mass corresponding to the trailer based on the arbitration result.

[0022] This application provides a trailer mass self-learning method and apparatus based on an energy recovery closed loop. The method includes: determining whether the energy recovery status monitoring data generated by the tractor within a preset sliding window meets the mass estimation trigger condition; if the energy recovery status monitoring data meets the mass estimation trigger condition, collecting vehicle operation data generated by the tractor within a preset time window and forming a mass estimation sequence; calculating the trailer mass of the mass estimation sequence based on the principle of energy conservation to obtain a first trailer mass; calculating the trailer mass of the mass estimation sequence using a recursive least squares method with a forgetting factor based on the change in the vehicle's kinetic energy to obtain a second trailer mass; and performing consistency arbitration on the first and second trailer masses, determining the effective trailer mass corresponding to the trailer based on the arbitration result. By learning the trailer mass online in real time, automatic and accurate estimation of the trailer mass is achieved, ensuring the efficiency and safety of subsequent energy management.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a trailer mass self-learning method based on an energy recovery closed loop provided in an embodiment of this application is shown. Figure 2 A flowchart illustrating a step for determining the mass of a first trailer according to an embodiment of this application is shown; Figure 3 A flowchart illustrating a step for determining the mass of a second trailer according to an embodiment of this application is shown; Figure 4 This illustration shows a functional block diagram of a trailer mass self-learning device based on an energy recovery closed loop provided in an embodiment of this application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0027] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0028] In existing vehicle towing technologies, trailer mass estimation is a key link in achieving precise control and energy efficiency optimization of the vehicle towing system. However, current mainstream solutions still have many technical bottlenecks. Traditional trailer mass estimation usually relies on two methods: one is for the driver to manually input the load mass. This method not only requires the driver to know the precise weight information of the trailer in advance, but also requires a tedious parameter entry operation before each towing mission. It is very easy for human negligence (such as input errors or forgetting to enter data) to lead to deviations in the initial mass data. The other method is to directly collect mass data by installing additional hardware sensors (such as pressure sensors installed at the trailer hook, weight sensors placed at the tires, or load sensors in the vehicle suspension system). Although this method can improve the measurement accuracy to a certain extent, the purchase, installation, and calibration costs of additional sensors are high. At the same time, the long-term operation of the sensors is easily affected by vehicle vibration, harsh road conditions (such as mud and bumps), and environmental corrosion, which leads to an increased equipment failure rate and increased difficulty in later maintenance.

[0029] Furthermore, the core drawback of traditional methods lies in the difficulty of updating the trailer mass in real time during the vehicle's dynamic operation. Neither manually input fixed values ​​nor quasi-static measurements from some sensor solutions can respond to dynamic changes in the trailer load (such as shifts in the load's center of gravity during driving, sloshing of liquid loads, or temporary addition / unloading of auxiliary equipment). This limitation of static estimation directly leads to performance degradation in the vehicle's core control system: in the energy recovery stage, especially in electric vehicles, the efficiency of energy recovery during coasting or braking highly depends on accurate sensing of the trailer mass. Inaccurate trailer mass will prevent the battery management system from matching the optimal recovery torque, resulting in insufficient energy recovery at best, and excessive recovery power leading to motor overcurrent or battery overcharging at worst, affecting battery life.

[0030] In adaptive cruise control (ACC) and braking systems, deviations in trailer mass (the sum of the trailer's own mass and the load mass on the trailer) can cause the vehicle to be unable to accurately calculate braking distance and acceleration response, easily leading to problems such as following too closely, vehicle "nodding" during braking, or mismatched power output during traction, reducing driving safety and comfort.

[0031] Based on this, this application provides a trailer mass self-learning method and apparatus based on an energy recovery closed loop. By learning the trailer mass online in real time, it reduces costs, achieves automatic and accurate estimation of trailer mass, and ensures the efficiency and safety of subsequent energy management, as detailed below: Please see Figure 1 , Figure 1 A flowchart illustrating a trailer mass self-learning method based on an energy recovery closed loop, as provided in an embodiment of this application, is shown. Figure 1 As shown, the method provided in this application embodiment includes the following steps: S100. Determine whether the energy recovery status monitoring data generated by the tractor within the preset sliding window meets the mass estimation trigger condition.

[0032] S200. If the energy recovery status monitoring data meets the mass estimation triggering conditions, the vehicle operation data generated by the tractor within the preset time window will be collected and a mass estimation sequence will be formed.

[0033] S300. Based on the principle of energy conservation, the trailer mass is calculated from the mass estimation sequence to obtain the first trailer mass.

[0034] S400. Based on the change in the kinetic energy of the whole vehicle, the recursive least squares method with a forgetting factor is used to calculate the trailer mass of the mass estimation sequence, and the second trailer mass is obtained.

[0035] S500: Arbitrate the consistency of the first trailer mass and the second trailer mass, and determine the effective trailer mass based on the arbitration result.

[0036] In one specific embodiment, in step S100, the preset starting time point corresponding to the sliding window is... The preset end time point for the sliding window is The number of time points within the preset sliding window is set according to actual needs. Each time the preset sliding window slides, the corresponding energy recovery status monitoring data is read, and it is determined whether the energy recovery status monitoring data meets the mass estimation trigger condition. If the mass estimation trigger condition is met, the vehicle operation data generated by the tractor within the preset time window is collected and a mass estimation sequence is formed. If the mass estimation trigger condition is not met, the preset sliding window is slid according to the preset step size and the process returns to re-execute step S100.

[0037] Preferably, the energy recovery status monitoring data includes, but is not limited to, at least one of the following: tractor gear position, accelerator pedal opening, brake pedal opening, battery allowable charging power, steering wheel angle, longitudinal acceleration, and altitude.

[0038] The mass estimation trigger condition is used to determine whether the tractor is in a stable coasting energy recovery state. The technical solution provided in this application calculates the trailer mass by having the tractor in a stable coasting energy recovery state. This is because when the vehicle is in this condition, it has stable longitudinal acceleration and no mechanical braking heat consumption. This avoids introducing complex mechanical energy calculations during the calculation process and improves the efficiency and accuracy of trailer mass calculation.

[0039] In addition, this application combines the trailer mass obtained from the energy conservation calculation with the trailer mass obtained from the analysis of the change in the vehicle's kinetic energy, and arbitrates the two results to further ensure the accuracy of the determined trailer mass, so as to further improve the management accuracy and efficiency of the subsequent energy recovery process.

[0040] For each time point within the preset sliding window, the tractor is in drive (D), the accelerator pedal opening is 0, the brake pedal opening is 0, the battery charging power is greater than a preset power threshold, the absolute value of the steering wheel angle is less than a preset angle threshold, the longitudinal acceleration is greater than a preset lower acceleration limit and less than a preset upper acceleration limit, and the altitude corresponding to the start time point and the end time point within the preset sliding window are equal. The preset lower acceleration limit and the preset upper acceleration limit are determined by the design acceleration of the energy recovery level to which the tractor belongs.

[0041] In one specific embodiment, the preset lower limit value of acceleration = Preset acceleration upper limit = , This indicates the design acceleration of the tractor unit under its energy recovery class; an example design acceleration is provided. , It represents the acceleration due to gravity.

[0042] In a preferred embodiment, in step S200, the vehicle operating data includes, but is not limited to, at least one of the following: motor recovery torque, longitudinal acceleration, and DC power data output by the battery management system. The vehicle operating data can be collected through the vehicle CAN bus. The collected vehicle operating data is filtered and validated before the subsequent mass calculation process is started.

[0043] In a specific example, the DC power data includes battery pack voltage and battery pack current, within a preset sliding window. ~ Internally, the battery pack voltage of the tractor is collected at a preset frequency (e.g., 10Hz) at each moment. and battery pack current The DC power of the battery pack can be further obtained. Furthermore, data was collected on the tractor unit within a preset sliding window. ~ Within, the motor recovery torque and longitudinal acceleration at each moment. .

[0044] In a preferred embodiment, please refer to Figure 2 , Figure 2 A flowchart illustrating a step for determining the mass of a first trailer according to an embodiment of this application is shown. Figure 2 As shown, step S300 includes: S3001. Establish the energy conservation equation between the recovered electrical energy and the change in the vehicle's kinetic energy.

[0045] S3002. The energy conservation equation is expanded using the mass estimation sequence to obtain the core energy balance equation for the mass of the tractor.

[0046] S3003. Solve the core energy balance equation to obtain the first total mass corresponding to the tractor and trailer.

[0047] S3004. Determine the first trailer mass corresponding to the first trailer based on the first total mass.

[0048] In this application, the mass of the tractor is the sum of the tractor's own weight and the weight of its load, the mass of the trailer is the sum of the trailer's own weight and the load on the trailer, and the total mass of the tractor and trailer is the sum of the tractor's mass and the trailer's mass.

[0049] In step S3001, within the preset sliding window t0~tn, based on the law of conservation of energy (mechanical energy output by the motor shaft + work done by the tractor against resistance = change in kinetic energy of the tractor), the energy conservation equation is created as follows:

[0050] This represents the total mechanical energy output by the motor. Indicates the energy conversion coefficient. This represents the energy consumed to overcome resistance, taking into account air resistance, rolling resistance, and slope resistance. This represents the change in the vehicle's kinetic energy within a preset sliding window, determined by the total mass of the tractor and the vehicle's speed.

[0051] In a preferred embodiment, the total mechanical energy output by the motor is determined by the following formula:

[0052]

[0053] in, Indicates the preset duration of the sliding window. Indicates within the preset sliding window The cumulative recovered energy of the battery pack is obtained after performing discrete integration. To account for high-voltage line losses, a pre-specified fixed efficiency coefficient is used (exemplary, such as any value in the range of 0.98 to 0.995). This represents the average or integral equivalent value of the inverter within a preset time window. This represents the average or integral equivalent value of the motor efficiency within a preset time window.

[0054] In another preferred embodiment, the change in vehicle kinetic energy is determined by the following formula:

[0055]

[0056] in, This represents the change in the total kinetic energy of the tractor unit. This represents the initial total mass of the tractor and trailer, which is determined by the law of energy conservation. This indicates the initial vehicle speed corresponding to the preset sliding window start time. This indicates the end speed corresponding to the preset end time of the sliding window. This indicates the final speed after correction to account for minor slope interference. This represents the correction factor, which is determined by looking up a table based on the standard deviation of vehicle speed within a preset sliding window. This indicates the design acceleration at the energy recovery level. Indicates the preset duration of the sliding window.

[0057] According to the theoretical design, the preset end speed corresponds to the end time of the sliding window. It should equal However, in actual operation, there are minor inclines that can interfere with the speed, so it is necessary to adjust the final speed. Corrections were made to improve calculation accuracy.

[0058] In one specific embodiment, The corresponding value range is (0, 1). Specifically, the preset correction coefficient table is retrieved. This table records the mapping relationship between different correction coefficients and different vehicle speed standard deviations. The larger the vehicle speed standard deviation, the better. The larger the value, the more accurate the calculation of the vehicle speed standard deviation within the preset sliding window is. Based on the calculated vehicle speed standard deviation, the preset correction coefficient table is consulted to determine the target correction coefficient.

[0059] In a preferred embodiment, the energy consumed to overcome resistance is determined by the following formula. :

[0060]

[0061]

[0062]

[0063] in, Indicates the time of the tractor air resistance below, Indicates the time of the tractor The rolling resistance below, Indicates the time of the tractor Downhill slope resistance, Indicates the time of the tractor Decrease the vehicle speed, Indicates air density, Indicates the drag coefficient. Indicates the frontal area of ​​the tractor unit. 1 represents the initial gross vehicle weight corresponding to the tractor and trailer. Represents gravitational acceleration. Indicates the rolling resistance coefficient, for example, The rolling resistance coefficient of asphalt pavement can be taken as 0.018.

[0064] In one specific embodiment, The road surface slope is indicated by the requirement that the vehicle be in a stable coasting energy recovery state due to the mass estimation triggering conditions. Therefore, the road surface slope in this application is... Since we take a value close to 0°, we default to this setting. Therefore, the tractor unit will be at the designated time. The rolling resistance below The solution can be expressed as follows:

[0065] In another specific embodiment, slope resistance It is a disturbance force that is difficult to measure accurately in real time, but solving for the energy consumed in overcoming the resistance requires... In the energy integral equation, the work done by the slope resistance during the time interval t0 to tn is expressed as:

[0066] in, The height change is within a preset sliding window. However, in this application, the mass estimation trigger condition restricts the vehicle to be in a stable coasting energy recovery state. Therefore, this application... ≈ 0, thus the work done by the slope resistance during time t0~tn will be canceled out. Therefore, after integration, we get:

[0067] Expanding the energy conservation equation using the above formula yields the following core energy balance equation:

[0068] in, This indicates that the vehicle is in The distance traveled within a given time period.

[0069] Solving the above core energy balance equations yields the first total mass. .

[0070] In a preferred embodiment, in step S3004, the mass of the first trailer is determined using the following formula. :

[0071] This indicates the half-load mass of the tractor or the target mass estimated based on the suspension travel height.

[0072] In steps S3001 to S3004, by integrating the changes in the DC energy output from the battery and the kinetic energy of the vehicle, the instantaneous interference terms introduced into the differential equation by factors such as road bumps are eliminated, making the quality estimation more stable and reliable.

[0073] In a preferred embodiment, please refer to Figure 3 , Figure 3 A flowchart illustrating a step for determining the mass of a second trailer according to an embodiment of this application is shown. Figure 3 As shown, step S400 includes: S4001. Based on the longitudinal force balance formula and mass estimation sequence of the trailer, a linear regression equation is constructed.

[0074] S4002. Perform recursive estimation on the linear regression equation, and extract the second total mass corresponding to the tractor and trailer from the parameter estimates corresponding to the parameter vector obtained after the linear regression equation converges.

[0075] S4003. Determine the second trailer mass corresponding to the trailer based on the second total mass.

[0076] In one specific embodiment, in step S4001, the following linear regression equation is constructed:

[0077] in, , Representing the observed quantity, This represents the motor recovery torque of the tractor unit at time K, where K corresponds one-to-one with time t within a preset sliding window. This indicates the reduction ratio from the tractor motor to the wheel end. This indicates the radius of the tractor's wheels. Indicates the air drag coefficient. , This indicates the speed of the tractor unit at time K. Represents the regression vector. , This represents the longitudinal acceleration of the tractor unit at time K. Represents gravitational acceleration. Represents the vector of parameters to be estimated. , This indicates the second gross vehicle weight corresponding to the tractor and trailer. This represents the composite rolling resistance parameter. Let K represent the system noise at time K, and T represent the transpose.

[0078] In a preferred embodiment, step S4002 includes: D1. Initialize the vector of parameters to be estimated. covariance matrix Let the forgetting factor be λ.

[0079] D2. At each sampling time K, calculate the gain matrix: .

[0080] D3. Update the parameter estimates of the vector of parameters to be estimated:

[0081] D4. Update the covariance matrix:

[0082] Perform steps D2 to D4 as described above until the linear regression equation converges.

[0083] Furthermore, from the parameter estimation corresponding to the converged linear regression equation Extract the second total mass .

[0084] In a preferred embodiment, the mass of the second trailer is determined in step S4003. The method is similar to step S3004, and will not be elaborated on here.

[0085] In a preferred embodiment, such as Figure 1 As shown, step S500 includes: Calculate the mass of the first trailer With the weight of the second trailer The absolute difference between ,like If a preset quality tolerance is used, the effective trailer quality corresponding to the trailer will not be updated, and the process will return to step S100. If the preset mass tolerance is not met, the weighted average mass between the first trailer mass and the second trailer mass is calculated, and the weighted average mass is updated to the non-volatile memory as the effective trailer mass. After the update, the process returns to step S100.

[0086] In one specific embodiment, in this application, the trailer mass is the core input data of the energy recovery management and control process, directly determining the control of parameters such as recovery torque, and ultimately affecting energy recovery efficiency, braking safety, and range performance. This application updates energy recovery-related data by updating the effective trailer mass. For example, the effective trailer mass calculated in the current calculation is compared with the effective trailer mass calculated in the previous calculation. If the effective trailer mass increases, the recovery torque is increased; if the effective trailer mass decreases, the recovery torque is decreased to ensure energy recovery efficiency.

[0087] This application can also adjust the adaptive cruise following distance of the tractor vehicle based on the real-time updated effective trailer mass to ensure braking safety; for example, the greater the effective trailer mass, the greater the following distance.

[0088] Based on the same application concept, this application also provides a trailer quality self-learning device based on energy recovery closed loop, which corresponds to the trailer quality self-learning method based on energy recovery closed loop provided in the above embodiments. Since the principle of the device in this application is similar to the trailer quality self-learning method based on energy recovery closed loop in the above embodiments of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0089] Please see Figure 4 , Figure 4 This diagram illustrates a functional block diagram of a trailer mass self-learning device based on an energy recovery closed loop, as provided in an embodiment of this application. Figure 4 As shown, the device includes: The trigger determination module 600 is used to determine whether the energy recovery status monitoring data generated by the tractor within the preset sliding window meets the mass estimation trigger conditions. The data acquisition module 610 is used to collect vehicle operation data generated by the tractor within a preset time window and form a quality estimation sequence if the energy recovery status monitoring data meets the quality estimation triggering conditions. The first calculation module 620 is used to calculate the trailer mass based on the principle of energy conservation of the mass estimation sequence to obtain the first trailer mass. The second calculation module 630 is used to calculate the trailer mass based on the change of the vehicle's kinetic energy using the recursive least squares method with a forgetting factor, and obtain the second trailer mass. Arbitration module 640 is used to conduct consistency arbitration on the mass of the first trailer and the mass of the second trailer, and to determine the effective trailer mass corresponding to the trailer based on the arbitration result.

[0090] Based on the same application concept, please refer to Figure 5 , Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 5 As shown, the electronic device 70 includes a processor 701, a memory 702, and a bus 703. The memory 702 stores machine-readable instructions that can be executed by the processor 701. When the electronic device 70 is running, the processor 701 and the memory 702 communicate through the bus 703. The machine-readable instructions are executed by the processor 701 to perform the steps of the trailer quality self-learning method based on energy recovery closed loop provided in any of the above embodiments.

[0091] Based on the same concept, this application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the steps of the trailer quality self-learning method based on energy recovery closed loop provided in the above embodiments.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0095] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A trailer mass self-learning method based on energy recovery closed loop, characterized in that, The method includes: Determine whether the energy recovery status monitoring data generated by the tractor within the preset sliding window meets the mass estimation trigger condition. The mass estimation trigger condition indicates that the vehicle is in a stable coasting energy recovery state. If the energy recovery status monitoring data meets the mass estimation triggering condition, then the vehicle operation data generated by the tractor within the preset time window is collected and a mass estimation sequence is formed. Based on the principle of energy conservation, the trailer mass is calculated from the mass estimation sequence to obtain the first trailer mass. Based on the change in the vehicle's kinetic energy, a recursive least squares method with a forgetting factor is used to calculate the trailer mass of the mass estimation sequence, thus obtaining the second trailer mass. The mass of the first trailer and the mass of the second trailer are subject to consistency arbitration, and the effective trailer mass corresponding to the trailer is determined based on the arbitration result.

2. The method according to claim 1, characterized in that, The energy recovery status monitoring data includes the corresponding gear of the tractor, accelerator pedal opening, brake pedal opening, battery allowable charging power, steering wheel angle, longitudinal acceleration, and altitude. The quality estimation triggering conditions include: The specified gear is forward, accelerator pedal opening is 0, brake pedal opening is 0, battery charging power is greater than a preset power threshold, the absolute value of steering wheel angle is less than a preset angle threshold, longitudinal acceleration is greater than a preset lower acceleration limit and less than a preset upper acceleration limit, and the altitude corresponding to the start time point and the end time point within the preset sliding window are the same. The preset lower limit and preset upper limit of acceleration are determined by the design acceleration of the energy recovery class to which the tractor belongs.

3. The method according to claim 1, characterized in that, The vehicle operating data includes the DC power data corresponding to the battery management system. The step of calculating the trailer mass based on the principle of energy conservation to obtain the first trailer mass includes: Establish the energy conservation equation between the recovered electrical energy and the change in the vehicle's kinetic energy: This represents the total mechanical energy output by the motor. Indicates the energy conversion coefficient. This represents the energy consumed by the tractor to overcome resistance, taking into account air resistance, rolling resistance, and gradient resistance. It represents the change in the vehicle's kinetic energy within a preset sliding window, determined by the total mass of the tractor and the vehicle's speed. The energy conservation equation is expanded using the mass estimation sequence to obtain the core energy balance equation for the mass of the tractor. Solving the core energy balance equation yields the first total mass of the tractor and trailer. The mass of the first trailer corresponding to the first trailer is determined from the first total mass.

4. The method according to claim 3, characterized in that, The total mechanical energy output by the motor is determined in the following way: Discrete integration is performed on the DC power data to obtain the cumulative recovered energy of the battery pack within a preset sliding window; The total mechanical energy output by the motor shaft is determined by multiplying the accumulated recovered energy, the fixed efficiency coefficient under high-voltage line loss, the average or integral equivalent value of the inverter within a preset time window, and the average or integral equivalent value of the motor efficiency within a preset time window.

5. The method according to claim 3, characterized in that, The change in the vehicle's kinetic energy is determined using the following formula: in, This represents the change in the vehicle's kinetic energy. This indicates the initial gross vehicle weight (GVW) of the tractor and trailer. This indicates the initial vehicle speed corresponding to the preset sliding window start time. This indicates the end speed corresponding to the preset end time of the sliding window. This indicates the final speed after correction to account for minor slope interference. This represents the correction factor, which is determined by looking up a table based on the standard deviation of vehicle speed within a preset sliding window. This indicates the design acceleration at the energy recovery level. Indicates the preset duration of the sliding window.

6. The method according to claim 3, characterized in that, The energy consumed to overcome resistance is determined by the following formula: in, Indicates the time of the tractor air resistance below, Indicates the time of the tractor The rolling resistance below, Indicates the time of the tractor Decrease the speed, Indicates air density, Indicates the drag coefficient. Indicates the frontal area of ​​the tractor unit. This indicates the initial gross vehicle weight (GVW) of the tractor and trailer. Represents gravitational acceleration. This represents the rolling resistance coefficient.

7. The method according to claim 3, characterized in that, The steps for determining the first trailer mass corresponding to the first trailer based on the first total mass include: The difference between the first total mass and the half-loaded mass of the vehicle is determined as the first trailer mass; Alternatively, the difference between the first total mass and the target mass estimated based on the suspension travel height can be determined as the first trailer mass.

8. The method according to claim 1, characterized in that, The steps for calculating the trailer mass based on the change in vehicle kinetic energy using a recursive least squares method with a forgetting factor to obtain the second trailer mass include: Based on the longitudinal force balance formula of the trailer and the aforementioned mass estimation sequence, a linear regression equation is constructed: in, , Representing the observed quantity, This represents the motor recovery torque of the tractor unit at time K. This indicates the reduction ratio from the tractor motor to the wheel end. This indicates the radius of the tractor's wheels. Indicates the air drag coefficient. , This indicates the speed of the tractor unit at time K. Represents the regression vector. , This represents the longitudinal acceleration of the tractor unit at time K. Represents gravitational acceleration. Represents the vector of parameters to be estimated. , This indicates the second gross vehicle weight corresponding to the tractor and trailer. This represents the composite rolling resistance parameter. Indicates the system noise at time K; The linear regression equation is recursively estimated, and the second total mass corresponding to the tractor and trailer is extracted from the parameter estimates corresponding to the parameter vector to be estimated obtained after the linear regression equation converges. The second trailer mass corresponding to the trailer is determined from the second total mass.

9. The method according to claim 1, characterized in that, The steps for arbitrating the consistency of the first trailer mass and the second trailer mass, and determining the effective trailer mass corresponding to the trailer based on the arbitration result, include: Calculate the absolute difference between the mass of the first trailer and the mass of the second trailer; If the absolute difference is greater than or equal to the preset quality tolerance, the effective trailer quality corresponding to the trailer will not be updated. If the absolute difference is less than the preset mass tolerance, the weighted average between the first trailer mass and the second trailer mass is taken as the effective trailer mass and updated.

10. A trailer mass self-learning device based on an energy recovery closed loop, characterized in that, The device includes: The trigger determination module is used to determine whether the energy recovery status monitoring data generated by the tractor within the preset sliding window meets the mass estimation trigger condition. The mass estimation trigger condition indicates that the vehicle is in a stable coasting energy recovery state. The data acquisition module is used to collect vehicle operation data generated by the tractor within a preset time window and form a mass estimation sequence if the energy recovery status monitoring data meets the mass estimation triggering condition. The first calculation module is used to calculate the trailer mass based on the principle of energy conservation of the mass estimation sequence to obtain the first trailer mass. The second calculation module is used to calculate the trailer mass of the mass estimation sequence based on the change of the vehicle's kinetic energy using a recursive least squares method with a forgetting factor, so as to obtain the second trailer mass. The arbitration module is used to conduct consistency arbitration on the mass of the first trailer and the mass of the second trailer, and determine the effective trailer mass corresponding to the trailer based on the arbitration result.

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