Shift control methods, devices, equipment and storage media

Abstract

This application relates to the field of gear shift control technology, and discloses a gear shift control method, device, equipment, and storage medium. The method includes: upon detecting a basic gear shift command, acquiring the current vehicle's driving state data; obtaining the current vehicle mass and current road slope based on the driving state data using an adaptive capacitive Kalman filter algorithm; determining the current load level corresponding to the current vehicle based on the current vehicle mass and a preset load grading strategy, and determining the current slope level corresponding to the current vehicle based on the current road slope and a preset slope grading strategy; determining the current operating condition level based on the current load level and the current slope level; and intervening in the basic gear shift command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs gear shift control according to the target gear command.

Description

Technical Field

[0001] This application relates to the field of gear shift control technology, and in particular to a gear shift control method, device, equipment and storage medium. Background Technology

[0002] With the rapid development of green logistics and the new energy vehicle industry, electric commercial vehicles are increasingly widely used in transportation and urban delivery. Compared with passenger cars, electric commercial vehicles have significant unique operating conditions: their driving conditions are complex and varied, including frequent starts and stops, and driving on slopes; at the same time, the vehicle's weight fluctuates greatly depending on the cargo loading status, with weight changes typically reaching several times the curb weight. Currently, electric commercial vehicles widely use automated manual transmissions (AMT) as the core component of their powertrain systems.

[0003] In existing technologies, mainstream AMT (Automated Manual Transmission) shift control strategies are mostly based on a fixed-parameter "vehicle speed-drive demand" two-parameter shifting rule. These strategies typically pre-define shift lookup table logic during vehicle calibration based on an assumed fixed vehicle mass, without incorporating real-time changes in vehicle mass and external environmental parameters such as road gradients into the closed-loop control of shift decisions. In actual operation, traditional fixed-parameter shifting strategies often struggle to adapt to dynamic changes in vehicle status and the external environment when facing complex conditions such as varying loads and inclines, leading to significant deviations between the shifting logic and the actual operating requirements of the vehicle. Summary of the Invention

[0004] The main objective of this application is to provide a shift control method that aims to solve the technical problem of how to reduce the deviation between shift control commands and the actual operating requirements of the vehicle.

[0005] To achieve the above objectives, this application proposes a shift control method, the method comprising: Upon detecting a basic shift command, the current vehicle driving status data is acquired, and the current vehicle mass and current road gradient are obtained based on the driving status data using an adaptive capacitive Kalman filter algorithm. The current load level of the current vehicle is determined based on the current vehicle mass and the preset load classification strategy, and the current slope level of the current vehicle is determined based on the current road slope and the preset slope classification strategy. The current operating condition level is determined based on the current load level and the current slope level. The basic shift command is then processed according to the current operating condition level to obtain the target gear command, so that the current vehicle performs shift control according to the target gear command.

[0006] In one embodiment, the step of obtaining the current vehicle mass and current road gradient based on the driving state data using an adaptive capacitive Kalman filter algorithm includes: The collected driving status data is filtered and preprocessed to obtain preprocessed observation data; The system state equations are constructed based on the random walk model, and the nonlinear observation equations are constructed based on the longitudinal dynamics of the vehicle. Based on the preprocessed observation data, the system state equation and the nonlinear observation equation are recursively estimated using an adaptive capacitive Kalman filter algorithm to obtain the current vehicle mass and the current road slope.

[0007] In one embodiment, the step of obtaining the current vehicle mass and current road slope by performing state recursion estimation on the system state equation and the nonlinear observation equation using an adaptive capacitive Kalman filter algorithm based on the preprocessed observation data includes: Obtain the state estimate and covariance matrix of the previous time step; Based on the state estimate of the previous time step and the covariance matrix of the previous time step, the initial volume points are generated according to the sphere diameter volume criterion. Substitute the initial volume point into the system state equation for time update to obtain the predicted state mean and predicted covariance. The target volume point is obtained based on the predicted state mean and predicted covariance, and the target volume point is substituted into the nonlinear observation equation to perform observation prediction and obtain predicted observation data. Obtain the process noise covariance matrix, determine the innovation sequence based on the predicted observation data and the preprocessed observation data, and adjust the process noise covariance based on the innovation sequence; Based on the information sequence and the adjusted process noise covariance matrix, the predicted state mean and predicted covariance are updated to obtain a real-time estimate of the vehicle quality as the current vehicle quality. Accelerometer data is acquired from the preprocessed observation data, and based on the acceleration sensor data and the vehicle longitudinal dynamics model, a real-time estimate of the road slope is obtained through an extended Kalman filter observer as the current road slope.

[0008] In one embodiment, before the steps of determining the current load level of the current vehicle based on the current vehicle mass and a preset load grading strategy, and determining the current slope level of the current vehicle based on the current road slope and a preset slope grading strategy, the method further includes: Obtain the set curb weight and maximum permissible total weight, and construct load determination thresholds for three load levels: light load, medium load, and heavy load based on the curb weight and the maximum permissible total weight. Obtain the set slope critical threshold, and construct slope determination thresholds for two slope levels, namely small slope and large slope, based on the slope critical threshold.

[0009] In one embodiment, the step of intervening in the basic shift command based on the current operating condition level to obtain the target gear command includes: When the current operating condition level is heavy load and steep uphill condition, the anti-cycle shifting strategy is executed; When the current working condition level is heavy load downhill working condition, a forced locking strategy is executed; When the current operating condition level is a light-load downhill condition, an active downshift strategy is executed.

[0010] In one embodiment, the step of implementing the anti-cyclic shifting strategy includes: Monitor the current vehicle speed. When the current vehicle speed reaches the upshift threshold of the basic shifting rule, forcibly block the upshift request and keep the current gear unchanged.

[0011] In one embodiment, the step of executing the forced locking strategy includes: It forcibly prohibits upshifting and downshifting operations, locks the current gear, and maintains the mechanical connection of the power transmission system.

[0012] Furthermore, to achieve the above objectives, this application also proposes a shift control device, the device comprising: The perception module is used to acquire the current vehicle driving status data when a basic shift command is detected, and to obtain the current vehicle mass and current road slope based on the driving status data through an adaptive capacitive Kalman filter algorithm. The working condition determination module is used to determine the current load level of the current vehicle based on the current vehicle mass and a preset load classification strategy, and to determine the current slope level of the current vehicle based on the current road slope and a preset slope classification strategy. An intervention module is used to determine the current operating condition level based on the current load level and the current slope level, and to intervene in the basic gear shifting command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs gear shifting control according to the target gear command.

[0013] In addition, to achieve the above objectives, this application also proposes an apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the shift control method described above when executed by the processor.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium that is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the shift control method described above.

[0015] This embodiment proposes a shift control method, device, equipment, and storage medium. The method includes: upon detecting a basic shift command, acquiring current vehicle driving status data; obtaining the current vehicle mass and current road slope based on the driving status data using an adaptive capacitive Kalman filter algorithm; determining the current load level corresponding to the current vehicle based on the current vehicle mass and a preset load grading strategy, and determining the current slope level corresponding to the current vehicle based on the current road slope and a preset slope grading strategy; determining the current operating condition level based on the current load level and the current slope level; and intervening in the basic shift command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs shift control according to the target gear command.

[0016] The shift control method and device of this application can be equipped with an adaptive intervention mechanism based on vehicle mass and road slope. Upon detecting a basic shift command, the system acquires the current vehicle's driving status data, estimates the current vehicle mass and road slope in real time using an adaptive capacitive Kalman filter algorithm, and determines the corresponding load level and slope level according to preset load grading and slope grading strategies. Then, during actual shift control, the system comprehensively determines the current operating condition level based on the load level and slope level, and intervenes in the basic shift command based on this operating condition level to generate a target gear command adapted to the current operating condition. Compared to existing shifting strategies that rely solely on conventional parameters such as vehicle speed and throttle opening for gear selection without considering the impact of changes in vehicle mass and road gradient on power demand, leading to unreasonable shifting logic, insufficient power, or decreased fuel economy during heavy-load or hilly driving, this application introduces an adaptive capacitive Kalman filter algorithm to achieve dynamic real-time estimation of vehicle mass and gradient. Furthermore, it intelligently intervenes in shifting commands based on a working condition classification mechanism. Consequently, in heavy-load or hilly driving scenarios, the vehicle can directly obtain optimized gear control that is more suited to the current working conditions, reducing the deviation between shifting control commands and the actual operating needs of the vehicle. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 This is a flowchart of the first embodiment of the shift control method proposed in this application; Figure 3 This is a flowchart of a second embodiment of the shift control method proposed in this application. Figure 4 This is a flowchart of the third embodiment of the gear shift control method proposed in this application; Figure 5 A diagram of a shift control device provided in an embodiment of this application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0022] Reference Figure 1 , Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0023] like Figure 1As shown, the device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may be connected to a display screen; optionally, the user interface 1003 may include a standard wired interface or a wireless interface. In this application, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0024] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0025] like Figure 1 As shown, the memory 1005, which is identified as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a shift control program.

[0026] exist Figure 1 In the device shown, the network interface 1004 is mainly used to connect to the backend server and communicate with the backend server; the user interface 1003 is mainly used to connect to the user equipment; the device calls the shift control program stored in the memory 1005 through the processor 1001 and executes the steps of the shift control method provided in the embodiments of this application.

[0027] Understandably, with the rapid development of green logistics and the new energy vehicle industry, electric commercial vehicles are increasingly widely used in transportation and urban delivery. Compared with passenger cars, electric commercial vehicles have significant unique operating conditions: their driving conditions are complex and varied, including frequent starts and stops, and driving on slopes; at the same time, the vehicle's weight fluctuates greatly depending on the cargo loading status, with weight changes typically reaching several times the curb weight. Currently, electric commercial vehicles widely use automated manual transmissions (AMT) as the core component of their powertrain systems.

[0028] In existing technologies, mainstream AMT (Automated Manual Transmission) shift control strategies are mostly based on a fixed-parameter "vehicle speed-drive demand" two-parameter shifting rule. These strategies typically pre-define shift lookup table logic during vehicle calibration based on an assumed fixed vehicle mass, without incorporating real-time changes in vehicle mass and external environmental parameters such as road gradients into the closed-loop control of shift decisions. In actual operation, traditional fixed-parameter shifting strategies often struggle to adapt to dynamic changes in vehicle status and the external environment when facing complex conditions such as varying loads and inclines, leading to significant deviations between the shifting logic and the actual operating requirements of the vehicle.

[0029] Therefore, to solve the above-mentioned technical problems, this embodiment proposes a shift control method, which includes: upon detecting a basic shift command, acquiring the current vehicle's driving state data; obtaining the current vehicle mass and current road slope based on the driving state data using an adaptive capacitive Kalman filter algorithm; determining the current load level corresponding to the current vehicle based on the current vehicle mass and a preset load grading strategy, and determining the current slope level corresponding to the current vehicle based on the current road slope and a preset slope grading strategy; determining the current operating condition level based on the current load level and the current slope level; and intervening in the basic shift command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs shift control according to the target gear command.

[0030] The shift control method and device in this embodiment can be equipped with an adaptive intervention mechanism based on vehicle mass and road slope. Upon detecting a basic shift command, it acquires the current vehicle's driving status data, estimates the current vehicle mass and road slope in real time using an adaptive capacitive Kalman filter algorithm, and determines the corresponding load level and slope level according to preset load grading and slope grading strategies. Then, during actual shift control, the system comprehensively determines the current operating condition level based on the load level and slope level, and intervenes in the basic shift command based on this operating condition level to generate a target gear command adapted to the current operating condition. Compared to existing shifting strategies that rely solely on conventional parameters such as vehicle speed and throttle opening for gear selection without considering the impact of changes in vehicle mass and road gradient on power demand, leading to unreasonable shifting logic, insufficient power, or decreased fuel economy during heavy-load or hilly driving, this embodiment introduces an adaptive capacitive Kalman filter algorithm to dynamically estimate vehicle mass and gradient in real time. Furthermore, it intelligently intervenes in shifting commands based on a working condition classification mechanism. Consequently, in heavy-load or hilly driving scenarios, the vehicle can directly obtain optimized gear control that is more suited to the current working conditions, reducing the deviation between shifting control commands and the actual operating needs of the vehicle.

[0031] For ease of understanding, the following is combined with Figures 2 to 5The shift control method provided in the embodiments of this application, as well as the shift control method, apparatus, device and storage medium provided in the following embodiments, will be described in detail.

[0032] This application provides a gear shifting control method, referring to... Figure 2 , Figure 2 This is a flowchart of the first embodiment of the shift control method proposed in this application.

[0033] like Figure 2 As shown, the method includes: Step S10: Upon detecting a basic shift command, acquire the current vehicle driving status data, and obtain the current vehicle mass and current road gradient based on the driving status data using an adaptive capacitive Kalman filter algorithm.

[0034] It should be noted that the executing entity in this embodiment can be a multifunctional machine device with shift control, such as a shift control device, or a device capable of performing the above functions. This embodiment uses a shift control device (hereinafter referred to as the device) for description.

[0035] Furthermore, it needs to be explained that the aforementioned basic shift command can refer to the initial gear shift request generated by the transmission control unit based on conventional shift patterns. These conventional shift patterns typically employ a two-parameter shift graph of "vehicle speed - accelerator pedal opening." The aforementioned driving state data can refer to various dynamic parameters collected during vehicle operation via onboard sensors and the controller area network bus, including but not limited to motor output torque, motor speed, vehicle speed, longitudinal acceleration, transmission ratio, final drive ratio, and accelerator pedal opening. The aforementioned adaptive capacitive Kalman filter algorithm is a nonlinear filtering method based on capacitive transformation, used for recursive estimation of state variables in strongly nonlinear systems. Its core lies in generating volume points through the spherical diameter volume criterion and introducing an adaptive adjustment mechanism to dynamically adjust the process noise covariance matrix based on the innovation energy to cope with sudden changes in system parameters.

[0036] In its implementation, upon detecting a basic shift command, the aforementioned device immediately triggers a safety correction process for shift control. First, the device acquires current driving status data via the controller local area network bus, including motor output torque, motor speed, vehicle speed, longitudinal acceleration, and accelerator pedal opening. Next, the device preprocesses the acquired driving status data, employing a sliding window mean filter to suppress high-frequency random noise and occasional pulse interference, setting the window length to N, and smoothing the original sequence. Subsequently, the device uses a single-state Kalman filter to perform a secondary filter on the smoothed data, further removing residual noise and outputting observations with a high signal-to-noise ratio.

[0037] After data preprocessing, the aforementioned device inputs the preprocessed observations into a pre-built adaptive ductile Kalman filter algorithm module. In the adaptive ductile Kalman filter algorithm, the device establishes a system state equation based on vehicle longitudinal dynamics, including vehicle mass and drag coefficient, with state transitions using a random walk approach. Based on the current state estimate and its covariance matrix, the device generates a set of volume points using the spherical diameter volume criterion. These volume points are then substituted into the state transition equation to complete time updates, obtaining the predicted state mean and predicted covariance. Next, the device regenerates the volume points corresponding to the predicted state and substitutes them into a nonlinear observation function based on Newton's second law to obtain the predicted observations and define the observation innovation sequence. The device calculates the innovation energy. When the innovation energy exceeds a preset threshold, a mismatch is determined between the current model and the actual operating conditions. The process noise covariance matrix is ​​dynamically amplified to enhance the filter's ability to track mass mutations. When the innovation energy falls back to the normal range, the original noise setting is restored to ensure estimation stability. Through this iterative process, the device obtains a real-time estimate of the current vehicle mass. Meanwhile, the aforementioned devices construct an extended Kalman filter observer based on acceleration sensors and vehicle longitudinal dynamics in parallel to independently calculate the road slope, achieving decoupled estimation of mass and slope, and finally outputting the current vehicle mass and the current road slope.

[0038] Step S20: Determine the current load level of the current vehicle based on the current vehicle mass and the preset load classification strategy, and determine the current slope level of the current vehicle based on the current road slope and the preset slope classification strategy.

[0039] It should be explained that the aforementioned preset load classification strategy can refer to a pre-defined judgment rule that maps continuous mass estimates to a finite number of discrete load levels. This judgment rule constructs an asymmetric weighted threshold based on the vehicle's curb weight and maximum permissible gross weight, classifying load states into three levels: light load, medium load, and heavy load. Similarly, the aforementioned preset slope classification strategy can refer to a pre-defined judgment rule that maps continuous road slope estimates to a finite number of discrete slope levels. This judgment rule sets a critical threshold based on highway engineering design specifications, classifying road slopes into two levels: gentle slope and steep slope.

[0040] In its implementation, after obtaining the current vehicle mass and the current road slope through the adaptive capacitive Kalman filter algorithm, the device will input the continuous estimated values ​​into the logic mapping layer for discretization and hierarchical processing. This is to avoid frequent switching of the shift logic or even control jitter caused by small fluctuations in the continuous estimated values ​​within the critical range.

[0041] The aforementioned device acquires a pre-stored preset load classification strategy, which defines load classification thresholds. Specifically, the device sets the vehicle's curb weight as M_empty, the maximum permissible gross vehicle weight as M_max, and defines a heavy load determination threshold as M_heavy. This heavy load determination threshold is obtained by asymmetric weighting of the curb weight and the maximum gross vehicle weight, enabling the vehicle to be identified earlier when it is close to full load. Simultaneously, a light load determination upper limit threshold is defined as M_light. The device compares the current vehicle weight with the aforementioned thresholds and performs discretization determination according to preset mapping rules: when the current vehicle weight is less than or equal to M_light, the device determines the current load level as light load; when the current vehicle weight is greater than M_light but less than M_heavy, the device determines the current load level as medium load; and when the current vehicle weight is greater than or equal to M_heavy, the device determines the current load level as heavy load.

[0042] Simultaneously, the aforementioned device acquires a pre-stored preset slope classification strategy, which defines slope classification thresholds. Specifically, based on highway engineering design specifications and commercial vehicle power performance requirements, the device sets a road slope judgment threshold of 9%, which corresponds to approximately 5.14 degrees, as the dividing threshold between large and small slopes under general highway conditions. The device compares the current road slope with the aforementioned threshold and performs discretization judgment according to preset mapping rules: when the current road slope is less than 9%, the device determines the current slope level as a flat road or a small slope condition; when the current road slope is greater than or equal to 9%, the device determines the current slope level as a large slope condition. The device stores the determined current load level and current slope level as discrete condition states.

[0043] Step S30: Determine the current operating condition level based on the current load level and the current slope level, and intervene in the basic gear shift command according to the current operating condition level to obtain the target gear command, so that the current vehicle performs gear shift control according to the target gear command.

[0044] It should be explained that the above rendering can be the process of converting a graphic structure into a visual image, specifically including calculating the final position of each standard graphic element on the canvas, drawing the shape and outline of the standard graphic elements, filling in colors and text content, and drawing connecting lines. The target chart mentioned above can be a complete QC chart presented to the user interface after rendering, such as cause-and-effect diagrams, system diagrams, relationship diagrams, tree diagrams, and other non-numerical quality management charts. This target chart has complete visual elements and logical structure.

[0045] In its implementation, after determining the current load level and the current gradient level, the aforementioned equipment combines the two to determine the current operating condition level. Specifically, based on the vehicle quality logic status flag and the road gradient logic status flag output by the logic mapping layer, the equipment combines them to form a discrete operating condition state. This discrete operating condition state includes combinations of light load and small gradient, light load and large gradient, medium load and small gradient, medium load and large gradient, heavy load and small gradient, and heavy load and large gradient.

[0046] Next, the aforementioned device intervenes in the detected basic shift command based on the determined current operating condition level. Within the dual-layer shift control logic architecture built inside the transmission control unit, the safety correction logic operates with the highest priority, performing consistency verification and safety arbitration on the basic shift command.

[0047] When the current operating condition is determined to be a combination of heavy load and steep gradient, and the vehicle is traveling uphill, the aforementioned equipment triggers the heavy load steep slope uphill judgment condition. The equipment performs a power pre-verification step, reading the maximum output torque at the target gear from the motor characteristic graph based on the current motor speed and pedal opening, and calculating the corresponding wheel-end driving force. Simultaneously, the equipment calculates the current driving resistance based on the real-time estimated current vehicle mass and current road gradient, including gradient gravity resistance and rolling resistance. If it is determined that the wheel-end driving force after upshifting is insufficient to overcome the current driving resistance, the equipment performs a forced overwrite operation, intercepting the upshift command from the basic shift logic, forcibly maintaining the current gear, and marking the "heavy load slope mode activated" status internally or on the instrument panel.

[0048] When the current operating condition is determined to be heavy load and the vehicle is traveling downhill, the aforementioned equipment triggers the heavy load downhill safety strategy. This equipment unconditionally overrides the basic shifting logic, forcibly locking the current gear and prohibiting any upshift or downshift command output, ensuring that the motor maintains a stable mechanical connection with the wheels and sustains continuous and reliable regenerative braking capability.

[0049] When the current operating condition is determined to be a combination of light load and steep gradient, and the vehicle is traveling downhill, the aforementioned equipment triggers an active downshift strategy. If the vehicle is currently in a high gear, the aforementioned equipment triggers an active downshift command, increasing the motor speed by lowering the gear and improving the motor's regenerative braking efficiency under light load conditions.

[0050] When the current operating condition level is determined to be other combinations, the above equipment does not interfere with the basic shift command and outputs the basic shift command directly as the target gear command.

[0051] The aforementioned device sends the target gear command obtained through intervention processing to the transmission actuator, so that the current vehicle performs shift control according to the target gear command.

[0052] Furthermore, before the steps of determining the current load class corresponding to the current vehicle based on the current vehicle mass and a preset load classification strategy, and determining the current slope class corresponding to the current vehicle based on the current road slope and a preset slope classification strategy, the method further includes: Obtain the set curb weight and maximum permissible total weight, and construct load determination thresholds for three load levels: light load, medium load, and heavy load based on the curb weight and the maximum permissible total weight. Obtain the set slope critical threshold, and construct slope determination thresholds for two slope levels, namely small slope and large slope, based on the slope critical threshold.

[0053] It should be explained that the aforementioned curb weight refers to the complete vehicle weight of an electric commercial vehicle when it is not loaded with cargo, not carrying passengers, has all working fluids added, and carries onboard tools and auxiliary equipment such as a spare tire. It is one of the vehicle weight benchmark parameters. The aforementioned maximum permissible gross vehicle weight refers to the maximum permissible driving weight of an electric commercial vehicle in its design, including the total weight of the curb weight, cargo weight, passenger weight, and onboard items. It is also one of the vehicle weight benchmark parameters. The aforementioned light load, medium load, and heavy load levels refer to three discrete load states based on the actual loaded weight of the vehicle. Light load indicates that the vehicle is unloaded or lightly loaded; medium load indicates that the vehicle is under medium load; and heavy load indicates that the vehicle is near or fully loaded. The aforementioned gradient threshold refers to the gradient numerical limit used to distinguish between small and large gradient driving conditions. This threshold is set based on highway engineering design specifications and the power performance requirements of commercial vehicles.

[0054] In practical implementation, before the above-mentioned equipment makes discretization determinations on load level and slope level, it is necessary to pre-construct a determination threshold system as the basis for the hierarchical classification of the logical mapping layer.

[0055] The aforementioned device acquires the pre-set curb weight M_empty and maximum permissible gross weight M_max, and stores these two weight reference parameters in the non-volatile memory of the transmission control unit. Next, the device constructs load judgment thresholds for three load levels—light load, medium load, and heavy load—based on the curb weight and maximum permissible gross weight. Specifically, the device defines the upper limit threshold for light load judgment as M_light, which is obtained by weighting the curb weight and maximum permissible gross weight. Its expression is M_light = α × M_empty + (1-α) × M_max, where α is a weighting coefficient, ranging from 0 to 1. The aforementioned device defines the heavy load determination threshold as M_heavy. This heavy load determination threshold is also obtained by weighting the curb weight and the maximum permissible gross weight, and its expression is M_heavy=β×M_empty+(1-β)×M_max, where β is a weighting coefficient, with a value between 0 and 1, and β is less than α. This makes the heavy load determination threshold biased towards the fully loaded side, so that the vehicle can be identified as heavily loaded earlier when it is close to the fully loaded condition. The aforementioned device stores the upper limit threshold of light load determination M_light and the heavy load determination threshold M_heavy as the load determination thresholds. The mass range less than or equal to M_light corresponds to the light load level, the mass range greater than M_light and less than M_heavy corresponds to the medium load level, and the mass range greater than or equal to M_heavy corresponds to the heavy load level.

[0056] Simultaneously, the aforementioned device acquires a pre-set slope threshold θ_threshold, which is set at 9% based on highway engineering design specifications and commercial vehicle power performance requirements, corresponding to an angle of approximately 5.14 degrees. Based on this slope threshold, the device constructs slope determination thresholds for two slope levels: small slope and large slope. Specifically, the device uses the slope threshold θ_threshold as a dividing point, defining slope ranges below the threshold as small slope levels, and slope ranges greater than or equal to the threshold as large slope levels.

[0057] This embodiment introduces an adaptive capacitive Kalman filter algorithm to achieve dynamic real-time estimation of vehicle mass and slope, and intelligently intervenes in shift commands based on a working condition classification mechanism. As a result, in heavy-load or slope driving scenarios, the vehicle can directly obtain optimized gear control that is more suitable for the current working conditions, reducing the deviation between shift control commands and the actual operating needs of the vehicle.

[0058] Based on the first embodiment, in the second embodiment, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3This is a flowchart of a second embodiment of the shift control method proposed in this application. Further, the step of obtaining the current vehicle mass and current road gradient based on the driving state data using an adaptive capacitive Kalman filter algorithm includes: Step S11: Perform filtering preprocessing on the collected driving status data to obtain preprocessed observation data.

[0059] It should be explained that the aforementioned filtering preprocessing refers to a series of operations that suppress and smooth the noise of the raw signals acquired by the sensor. This series of operations includes a two-stage processing flow of sliding window mean filtering and single-state Kalman filtering, used to remove high-frequency random noise and occasional impulse interference. The sliding window mean filtering method refers to a filtering method that sets a fixed-length window and calculates the arithmetic mean of the data sequence within the window as the output value at the current moment. Its expression is that the filtered value equals the sum of all sampled values ​​within the window divided by the window length. The single-state Kalman filtering method refers to a Kalman filtering algorithm designed for a single state variable. This algorithm is based on a state-space model and recursively estimates the system state through two steps: prediction and update. It performs a second filtering on the data based on sliding window mean filtering to further remove residual noise. The aforementioned observed data refers to the data sequence with a high signal-to-noise ratio output after filtering preprocessing.

[0060] In its implementation, the device sets the sliding window length to N, where N is a pre-defined positive integer representing the number of consecutive sampling points involved in the mean calculation. The device performs sliding window mean filtering on each type of driving state data in the collected raw data sequence. Taking motor output torque as an example, the device forms a window data sequence with the motor output torque values ​​of the current moment and the previous N-1 moments, calculates the arithmetic mean of this window data sequence, and outputs the calculated mean as the filtered motor output torque at the current moment. As time progresses, the device slides the window forward, removing the earliest data point and adding the latest data point, recalculating the mean, thus achieving continuous filtering.

[0061] Next, the device constructs a state model and an observation model for a single-state Kalman filter on the data sequence after sliding window mean filtering. The device defines the state equation of the single-state Kalman filter as the current state value equals the previous state value plus process noise, and the observation equation as the current observation value equals the current state value plus observation noise. The device performs the prediction step of the single-state Kalman filter, predicting the prior state estimate and prior covariance at the current moment based on the previous state estimate and state covariance. Subsequently, the device performs the update step of the single-state Kalman filter, calculating the Kalman gain, using the current sliding window mean filter output value as the observation value to correct the prior state estimate, obtaining the current posterior state estimate, and updating the state covariance. The device uses the posterior state estimate output by the single-state Kalman filter as the observation data after secondary filtering. The aforementioned device performs two-stage preprocessing on each type of driving state data: sliding window mean filtering and single-state Kalman filtering, to obtain the filtered sequence of all observation data. This filtered sequence is then output as the preprocessed observation data to the adaptive capacitive Kalman filter algorithm module.

[0062] Step S12: Construct the system state equation based on the random walk model, and construct the nonlinear observation equation based on the vehicle longitudinal dynamics.

[0063] In its implementation, the aforementioned device constructs the system state equation based on a random walk model. The device selects the vehicle mass *m* and drag coefficient *C_d* as system state variables, defining the state vector as *x=[m,C_d]^T*. Considering that the vehicle mass and drag coefficient of an electric commercial vehicle remain approximately constant over short timescales, but may undergo abrupt changes under loading and unloading conditions, the device employs a random walk model to describe the state transition process. The device constructs the system state equation as follows: the current state vector equals the previous state vector plus the process noise vector, mathematically expressed as *x_k=x_{k-1}+w_{k-1}*, where *x_k* represents the state vector at time *k*, *x_{k-1}* represents the state vector at time *k-1*, and *w_{k-1}* represents the process noise vector at time *k-1*, which follows a normal distribution with zero mean and a covariance matrix of Q. The device stores the system state equation in an adaptive capacitive Kalman filter algorithm module for state prediction during the time update step.

[0064] Meanwhile, the aforementioned device constructs a nonlinear observation equation based on vehicle longitudinal dynamics. It establishes a balance between driving force, driving resistance, and vehicle longitudinal acceleration according to Newton's second law. The device defines the driving force as the force acting on the wheel end after the motor output torque is amplified by the gearbox and final drive, expressed as F_drive=T_m×i_g×i_0×η / r_w, where T_m is the motor output torque, i_g is the gearbox ratio, i_0 is the final drive ratio, η is the transmission efficiency, and r_w is the equivalent tire radius. The aforementioned equipment defines driving resistance as the sum of gradient gravity resistance, rolling resistance, and air resistance. The gradient gravity resistance is expressed as F_grade=m×g×sin(θ), the rolling resistance is expressed as F_roll=m×g×f_r×cos(θ), and the air resistance is expressed as F_air=0.5×ρ×C_d×A×v^2, where g is the acceleration due to gravity, θ is the road gradient, f_r is the rolling resistance coefficient, ρ is the air density, A is the frontal area, and v is the vehicle speed.

[0065] The aforementioned device constructs a nonlinear observation equation based on the longitudinal dynamic equilibrium relationship of the vehicle. It uses the vehicle's longitudinal acceleration as an observation and establishes an observation function h(x_k) to describe the nonlinear relationship between the state variables and the observation. The nonlinear observation equation is constructed as follows: the observation vector at the current time is equal to a nonlinear function of the state vector at the current time plus an observation noise vector. Its mathematical expression is z_k = h(x_k) + v_k, where z_k represents the observation vector at time k, h(x_k) represents the nonlinear observation function at time k, and v_k represents the observation noise vector at time k. This observation noise vector follows a normal distribution with a mean of zero and a covariance matrix of R. The nonlinear observation function is specifically defined as h(x_k) = [T_m×i_g×i_0×η / r_w - m×g×sin(θ) - m×g×f_r×cos(θ) - 0.5×ρ×C_d×A×v^2] / m, which expresses the longitudinal acceleration generated by the vehicle under the action of driving force and driving resistance.

[0066] Step S13: Based on the preprocessed observation data, the system state equation and the nonlinear observation equation are recursively estimated using an adaptive capacitive Kalman filter algorithm to obtain the current vehicle mass and the current road slope.

[0067] It should be explained that the aforementioned adaptive capacitive Kalman filter algorithm can refer to a nonlinear filtering method based on capacitive transformation. This algorithm generates capacitive points to approximate the posterior state distribution of the nonlinear system using the spherical diameter volume criterion, and introduces an innovation-driven adaptive adjustment mechanism to dynamically adjust the process noise covariance matrix to adapt to sudden changes in system parameters. The aforementioned state recursive estimation can refer to the process of iteratively recursively applying the filtering algorithm between time update and observation update steps, predicting state variables based on the system state equation, and then correcting the prediction results using the observed values ​​based on the nonlinear observation equation, gradually approximating the true values ​​of the state variables. The aforementioned innovation can refer to the difference between the actual observed value and the predicted observed value, used to characterize the degree of deviation between the model prediction and the actual observation; its mathematical expression is the actual observed value minus the predicted observed value.

[0068] Further, the step of obtaining the current vehicle mass and current road slope by performing state recursion estimation on the system state equation and the nonlinear observation equation based on the preprocessed observation data using an adaptive capacitive Kalman filter algorithm includes: Obtain the state estimate and covariance matrix of the previous time step; Based on the state estimate of the previous time step and the covariance matrix of the previous time step, the initial volume points are generated according to the sphere diameter volume criterion. Substitute the initial volume point into the system state equation for time update to obtain the predicted state mean and predicted covariance. The target volume point is obtained based on the predicted state mean and predicted covariance, and the target volume point is substituted into the nonlinear observation equation to perform observation prediction and obtain predicted observation data. Obtain the process noise covariance matrix, determine the innovation sequence based on the predicted observation data and the preprocessed observation data, and adjust the process noise covariance based on the innovation sequence; Based on the information sequence and the adjusted process noise covariance matrix, the predicted state mean and predicted covariance are updated to obtain a real-time estimate of the vehicle quality as the current vehicle quality. Accelerometer data is acquired from the preprocessed observation data, and based on the acceleration sensor data and the vehicle longitudinal dynamics model, a real-time estimate of the road slope is obtained through an extended Kalman filter observer as the current road slope.

[0069] It should be explained that the aforementioned spherical radial volume criterion can refer to a numerical integration criterion used to generate volume points. This criterion is based on the principle of spherical radial integration, and approximates the Gaussian weighted integral of the nonlinear function by selecting a set of volume points with equal weights. Each volume point is obtained by multiplying the square root of the state dimension by the unit volume point vector. The aforementioned initial volume points can refer to a set of points representing the state distribution generated based on the state estimate and its covariance matrix at the previous time step, used to transmit the uncertainty of the system state in the time update step. The aforementioned time update can refer to the process of using the system state equation to perform state transitions on the volume points, thereby obtaining the prior estimate of the state at the next time step. The aforementioned predicted state mean can refer to the weighted average of the prior state estimates obtained after the time update.

[0070] Furthermore, the aforementioned prediction covariance can refer to a measure of the uncertainty of the prior state estimate obtained after time updates. The aforementioned target volume points can refer to a set of volume points regenerated based on the predicted state mean and prediction covariance, used to transmit the uncertainty of observations in the observation update step. The aforementioned observation prediction can refer to the process of mapping the target volume points using nonlinear observation equations to obtain predicted observation values. The aforementioned innovation sequence can refer to the sequence of differences between preprocessed actual observation data and predicted observation data, used to characterize the degree of deviation between model predictions and actual observations. The aforementioned process noise covariance matrix can refer to a matrix describing the uncertainty of process noise in the system state equations, used to reflect the influence of model errors and external disturbances on state transitions. The aforementioned extended Kalman filter observer can refer to a state observer based on the extended Kalman filter algorithm, which linearizes the nonlinear system through a first-order Taylor expansion to achieve a recursive estimate of the system state, used for independently solving road slopes.

[0071] In its specific implementation, the device performs the following iterative process when performing state recursive estimation using the adaptive capacitive Kalman filter algorithm.

[0072] The aforementioned device acquires the state estimate and covariance matrix from the previous time step. Specifically, the device reads the state estimate output after filtering from the storage unit. The values ​​of _{k-1} and the covariance matrix P_{k-1} are used as the initial values ​​for the current filtering iteration.

[0073] Next, the device generates initial volume points based on the state estimate and covariance matrix of the previous time step according to the spherical volume criterion. The device sets the state dimension to n and generates 2n initial volume points. The device calculates the square root matrix S_{k-1} of the covariance matrix of the previous time step, satisfying P_{k-1}=S_{k-1}×S_{k-1}^T. The device defines a unit volume point vector ξ_i, where i ranges from 1 to 2n. The device generates initial volume points X_{i,k-1} according to the spherical volume criterion, with the expression X_{i,k-1}= _{k-1}+S_{k-1}×ξ_i.

[0074] Subsequently, the aforementioned device substitutes the initial volume points into the system state equation for time updates, obtaining the predicted state mean and predicted covariance. The device substitutes each initial volume point X_{i,k-1} into the system state equation, which is x_k=x_{k-1}+w_{k-1}. Assuming the process noise w_{k-1} has zero mean, the predicted volume point X_{i,k|k-1}=X_{i,k-1} is obtained. The device then calculates the weighted average of all predicted volume points to obtain the predicted state mean. _{k|k-1}=(1 / 2n)×ΣX_{i,k|k-1}.

[0075] The above equipment calculates the predicted covariance: P_{k|k-1}=(1 / 2n)×Σ(X_{i,k|k-1}- _{k|k-1})×(X_{i,k|k-1}- _{k|k-1})^T+Q_{k-1}, where Q_{k-1} is the process noise covariance matrix of the previous time step.

[0076] Next, the aforementioned device obtains the target volume point based on the predicted state mean and predicted covariance, and substitutes the target volume point into the nonlinear observation equation to perform observation prediction, obtaining predicted observation data. The device performs square root decomposition on the predicted covariance matrix P_{k|k-1} to obtain the square root matrix S_{k|k-1}. The device then generates the target volume point X_{i,k|k-1}^*= The above device substitutes each target volume point into the nonlinear observation equation, z_k=h(x_k)+v_k, where h(x_k) is a nonlinear observation function constructed based on vehicle longitudinal dynamics, resulting in the predicted observation value Z_{i,k|k-1}=h(X_{i,k|k-1}^*). The device then calculates the weighted average of all predicted observation values ​​to obtain the predicted observation data. _{k|k-1}=(1 / 2n)×ΣZ_{i,k|k-1}.

[0077] The aforementioned device acquires the process noise covariance matrix, determines the innovation sequence based on the predicted observation data and the preprocessed observation data, and adjusts the process noise covariance according to the innovation sequence. The device retrieves the process noise covariance matrix Q_{k-1} at the current time from the storage unit. The device also acquires the preprocessed actual observation data z_k and calculates the innovation sequence e_k = z_k - The above-mentioned device calculates the innovation energy, which is defined as e_k^T × e_k. The device compares the innovation energy with a preset threshold. When the innovation energy exceeds the preset threshold, it determines that there is a mismatch between the current model and the actual operating conditions. The device dynamically amplifies the process noise covariance matrix, adjusting it as Q_k = κ × Q_{k-1}, where κ is an adaptive adjustment factor greater than 1. When the innovation energy falls back to the normal range, the device restores the original process noise covariance matrix setting, i.e., Q_k = Q_{k-1}.

[0078] The aforementioned device updates the predicted state mean and predicted covariance based on the innovation sequence and the adjusted process noise covariance matrix to obtain a real-time estimate of the vehicle mass as the current vehicle mass. The device calculates the innovation covariance matrix as follows: P_{zz,k|k-1}=(1 / 2n)×Σ(Z_{i,k|k-1}- _{k|k-1})×(Z_{i,k|k-1}- _{k|k-1})^T+R_k; Where R_k is the observation noise covariance matrix. The above equipment calculates the cross-covariance matrix P_{xz,k|k-1}=(1 / 2n)×Σ(X_{i,k|k-1}^*- _{k|k-1})×(Z_{i,k|k-1}- The above device calculates the Kalman gain K_k = P_{xz,k|k-1} × P_{zz,k|k-1}^{-1}. The above device uses the Kalman gain to correct the mean of the predicted state, obtaining an updated state estimate. _k= The covariance matrix of the updated state of the above equipment is: _{k|k-1}+K_k×e_k. P_k=P_{k|k-1}-K_k×P_{zz,k|k-1}×K_k^T.

[0079] The above equipment is based on the updated state estimate. The vehicle mass component is extracted from _k and used as a real-time estimate of the vehicle mass, i.e., the current vehicle mass.

[0080] Simultaneously, the aforementioned device acquires acceleration sensor data from the preprocessed observation data and, based on the acceleration sensor data and the vehicle longitudinal dynamics model, obtains a real-time estimate of the road slope as the current road slope through an extended Kalman filter observer. Specifically, the device extracts the acceleration sensor measurement value a_sensor from the preprocessed observation data. The device constructs an extended Kalman filter observer based on vehicle longitudinal dynamics, selecting the road slope θ as the state variable and establishing the state equation θ_k=θ_{k-1}+w_θ,k, where w_θ,k is the process noise. The device establishes the observation equation a_sensor=a_vehicle+g×sin(θ)+v_θ, where a_vehicle is the vehicle longitudinal acceleration and v_θ is the observation noise. The device recursively estimates the road slope through the prediction and update steps of the extended Kalman filter to obtain a real-time estimate of the road slope as the current road slope.

[0081] Based on the first and second embodiments, in the third embodiment, the content that is the same as or similar to that in Embodiments 1 and 2 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 This is a flowchart of a third embodiment of the shift control method proposed in this application. Further, the step of intervening in the basic shift command based on the current operating condition level to obtain the target gear command includes: Step S31: When the current working condition level is heavy load and steep uphill working condition, execute the anti-cycle shifting strategy; Step S32: When the current working condition level is heavy load downhill working condition, execute the forced locking strategy; Step S33: When the current working condition level is light load downhill working condition, execute the active downshift strategy.

[0082] It should be explained that the aforementioned heavy-load and steep uphill conditions refer to a vehicle operating at a heavy load level with a steep incline, and traveling uphill. Under these conditions, the vehicle must overcome significant gravity and rolling resistance, resulting in a high load on the drive system. The aforementioned anti-cycle shifting strategy refers to a control mechanism that prevents repeated shifting between adjacent gears during heavy-load uphill driving. This mechanism forcibly blocks upshift requests from the basic shifting logic, maintaining the current gear and cutting off the "upshift-deceleration-downshift" cycle. The aforementioned heavy-load downhill conditions refer to a vehicle operating at a heavy load level with a downhill direction. Under these conditions, the vehicle speed naturally increases due to gravity. The aforementioned forced gear-locking strategy refers to a control mechanism that ensures braking continuity during heavy-load downhill driving. This mechanism forcibly prohibits any upshifting or downshifting operations, locking the current gear, maintaining the mechanical connection of the powertrain, and ensuring the continuous availability of regenerative braking capability. The aforementioned light-load downhill driving condition refers to a state where the vehicle is lightly loaded and traveling downhill. Under this condition, the vehicle's mass is relatively small, and the braking demand is relatively low. The aforementioned active downshifting strategy refers to a control mechanism used to improve the efficiency of regenerative braking under light-load downhill conditions. This mechanism triggers an active downshifting command when the vehicle is in a high gear, lowering the gear to increase the motor speed and allowing the motor to enter a more efficient regenerative braking operating range.

[0083] In its implementation, after determining the current operating condition level, the aforementioned device executes the corresponding shift intervention strategy based on the determined current operating condition level. Within the dual-layer shift control logic architecture built inside the transmission control unit, the safety correction logic operates with the highest priority, arbitrating and overriding the basic shift commands.

[0084] The aforementioned equipment detects that the vehicle is traveling uphill, and the current load level output by the logic mapping layer is heavy load condition, and the current gradient level is steep gradient condition. The equipment monitors the shift commands generated by the basic shift logic. When the basic shift logic generates an upshift command solely based on vehicle speed and accelerator pedal opening, the equipment triggers a safety correction logic. The equipment performs a power pre-verification, reading the maximum output torque at the target gear from the motor characteristic graph based on the current motor speed and accelerator pedal opening, and calculating the corresponding wheel-end driving force. The equipment calculates the current driving resistance based on the real-time estimated current vehicle mass and current road gradient, including gradient gravity resistance and rolling resistance. The equipment compares the wheel-end driving force with the current driving resistance. If it determines that the wheel-end driving force after upshifting is insufficient to overcome the current driving resistance, the equipment performs a forced overwrite operation, intercepting the upshift command from the basic shift logic, forcibly maintaining the current gear, and marking "hillside heavy load mode activated" status internally or on the instrument panel. Through the aforementioned anti-cycle shifting strategy, the device logically cuts off the "upshift-decelerate-downshift" cycle path, preventing the vehicle from repeatedly switching between adjacent gears.

[0085] The aforementioned equipment detects that the vehicle is traveling downhill, and the current load level output by the logic mapping layer is heavy load. The equipment triggers a heavy-load downhill safety strategy. Regardless of the shift command generated by the current basic shift logic, the equipment's safety correction logic unconditionally overrides the basic shift logic, forcibly locking the current gear and prohibiting any upshift or downshift command from being output to the transmission actuator. Through this forced gear-locking strategy, the equipment ensures that the motor maintains a stable mechanical connection with the wheels, avoiding power interruption during gear shifting, maintaining continuous and reliable regenerative braking capability, and preventing the risk of power interruption and loss of vehicle speed due to gear shifting.

[0086] The aforementioned equipment detects that the vehicle is traveling downhill, and the current load level output by the logic mapping layer is light load. The equipment obtains the current gear information; if the current gear is in a high gear range, it triggers an active downshift command, generating a downshift request. The equipment sends this active downshift command as the target gear command to the transmission actuator, increasing the motor speed by lowering the gear and allowing the motor to enter a higher speed operating range. When the motor operates at a higher speed, its regenerative braking efficiency improves, providing more effective regenerative braking torque to assist the mechanical braking system in maintaining vehicle speed and reducing the load on the mechanical braking system.

[0087] Furthermore, the step of implementing the anti-cyclic shifting strategy includes: Monitor the current vehicle speed. When the current vehicle speed reaches the upshift threshold of the basic shifting rule, forcibly block the upshift request and keep the current gear unchanged.

[0088] It should be explained that the aforementioned basic shifting rules refer to the conventional shifting logic pre-stored in the transmission control unit. This shifting logic typically uses a two-parameter shifting graph of "vehicle speed - accelerator pedal opening." At a given accelerator pedal opening, an upshift command is triggered when the vehicle speed reaches a preset upshift speed threshold. This upshift threshold refers to the critical vehicle speed value that triggers an upshift operation at a specific accelerator pedal opening. This threshold is preset based on a fixed vehicle mass during the vehicle calibration phase. The aforementioned forced blocking of upshift requests refers to the operation of safety correction logic to intercept and suppress the upshift command generated by the basic shifting logic. This operation does not change the judgment result of the basic shifting logic, but prevents the upshift command from being transmitted to the transmission actuator at the output end. The aforementioned maintaining the current gear means that the transmission control unit maintains the currently engaged gear state, does not perform any shifting operation, and allows the vehicle to continue traveling at the current gear ratio.

[0089] In its implementation, the aforementioned equipment, after determining that the current operating condition is heavy load and steep uphill condition, executes an anti-cycle shifting strategy, which specifically includes monitoring vehicle speed and blocking upshift requests.

[0090] The aforementioned device continuously monitors the vehicle's current speed during operation. It acquires the vehicle speed signal from the speed sensor via the controller's local area network bus, or obtains the motor speed from the motor controller and calculates the current vehicle speed by combining this information with the current gear ratio. The device then compares the current vehicle speed in real time with the upshift thresholds stored in the basic shifting rules.

[0091] When the aforementioned equipment determines that the current operating condition is heavy load and steep uphill, it activates the anti-cyclic shifting monitoring logic. When the equipment detects that the current vehicle speed reaches the upshift threshold corresponding to the current accelerator pedal opening in the basic shifting logic, the basic shifting logic generates an upshift request based on the regular shifting rules. Upon receiving the upshift request from the basic shifting logic, the equipment's safety correction logic sets an upshift request interception flag. When the equipment determines that the current condition is heavy load and steep uphill, it activates the upshift request interception flag. In the activated state, the equipment intercepts all upshift requests output by the basic shifting logic, preventing the upshift requests from being transmitted to the transmission actuator. The equipment maintains the current gear and does not perform any upshifting operation.

[0092] After the aforementioned equipment maintains the current gear position, the vehicle continues to travel at the gear ratio corresponding to the current gear. Since the current gear is a lower gear with a larger gear ratio, the motor output torque, after being amplified by the transmission system, can provide a larger wheel-end driving force, which is sufficient to overcome the slope gravity resistance and rolling resistance under heavy load and steep gradient conditions, maintaining the vehicle's continuous climbing ability.

[0093] The aforementioned equipment continuously monitors changes in the current operating condition level. When the current operating condition level no longer meets the requirements for heavy load and steep uphill conditions, the aforementioned equipment restores the upshift request interception flag to an inactive state, restores the normal output of the basic shift logic, and allows the upshift request to be transmitted to the transmission actuator.

[0094] Accordingly, the step of executing the forced locking strategy includes: It forcibly prohibits upshifting and downshifting operations, locks the current gear, and maintains the mechanical connection of the power transmission system.

[0095] It should be noted that locking the current gear can mean that the transmission control unit maintains the currently engaged gear, keeping the transmission ratio unchanged and not responding to any external shift commands. The mechanical connection of the powertrain system can refer to the rigid mechanical transmission link formed from the drive motor output shaft through the transmission, drive shaft, final drive, differential, and wheels. This link remains continuous when the gear is locked, ensuring a constant direct mechanical connection between the motor and the wheels. Regenerative braking can refer to a braking method where the drive motor operates in generator mode during vehicle deceleration or downhill conditions, converting the vehicle's kinetic energy into electrical energy and storing it in the battery. This braking method relies on a stable mechanical connection between the motor and the wheels.

[0096] In its implementation, after determining that the current operating condition is a heavy-load downhill condition, the aforementioned device executes a forced gear-locking strategy, specifically including: The device sets a shift command interception flag in its safety correction logic. When the device determines that the current condition is a heavy-load downhill condition, it activates the shift command interception flag. In the activated state, the device unconditionally intercepts all shift commands generated by the basic shift logic within the transmission control unit, including upshift and downshift commands. The device prevents any shift commands from being transmitted to the transmission actuator, ensuring that the transmission does not perform any gear-shifting actions.

[0097] The aforementioned device performs a lock operation on the current gear. Specifically, the device acquires the information of the currently engaged gear in the transmission and locks that gear as the target gear. The device outputs a control command to the transmission actuator to maintain the current gear, or maintains the current state of the transmission actuator while intercepting all shift commands. During the lock period, the device does not respond to any shift requests from the basic shift logic, and the transmission maintains the current gear ratio regardless of changes in current vehicle speed or accelerator pedal opening.

[0098] The aforementioned device maintains the mechanical connection of the powertrain system by locking the current gear. Specifically, when the gearbox gear is locked, the drive motor output shaft and the gearbox input shaft maintain a fixed transmission ratio connection, forming a continuous mechanical transmission link between the gearbox output shaft, drive shaft, final drive, differential, and wheels. This mechanical connection ensures that the drive motor and wheels are always in a rigid connection state, eliminating any power interruption caused by gear shifting.

[0099] While maintaining the mechanical connection of the powertrain system, the drive motor continuously provides regenerative braking torque. When the vehicle is traveling under heavy load downhill conditions, the wheels drive the motor to rotate. The motor operates in generator mode, converting mechanical energy into electrical energy, generating reverse braking torque that acts on the transmission system, assisting the mechanical braking system in controlling vehicle speed. The device continuously monitors changes in the current operating condition level. When the current operating condition level no longer meets the requirements for heavy load downhill driving, the device resets the shift command interception flag to an inactive state, restoring the normal output of the basic shift logic.

[0100] This embodiment also provides a first embodiment of a shift control device; please refer to [reference needed]. Figure 5 , Figure 5 This is a diagram of a shift control device provided in an embodiment of this application. The shift control device includes: The perception module is used to acquire the current vehicle driving status data when a basic shift command is detected, and to obtain the current vehicle mass and current road slope based on the driving status data through an adaptive capacitive Kalman filter algorithm. The working condition determination module is used to determine the current load level of the current vehicle based on the current vehicle mass and a preset load classification strategy, and to determine the current slope level of the current vehicle based on the current road slope and a preset slope classification strategy. An intervention module is used to determine the current operating condition level based on the current load level and the current slope level, and to intervene in the basic gear shifting command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs gear shifting control according to the target gear command.

[0101] The shift control device provided in this embodiment, employing the shift control method described in the above embodiments, can solve the technical problem of reducing the deviation between shift control commands and the actual operating requirements of the vehicle. Compared with the prior art, the beneficial effects of the shift control device provided in this embodiment are the same as those of the shift control method described in the above embodiments, and other technical features in the shift control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0102] This embodiment provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the shift control method in the above embodiment.

[0103] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0104] The aforementioned computer-readable storage medium may be included in the shift control device; or it may exist independently and not assembled into the shift control device.

[0105] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the shift control device, cause the shift control device to perform shift control.

[0106] Computer program code for performing the operations of this embodiment can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the system, method, and display according to various embodiments of this embodiment. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The modules described in this embodiment can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0109] The readable storage medium provided in this embodiment is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-described shift control method, and can solve the technical problem of how to reduce the deviation between the shift control instructions and the actual operating requirements of the vehicle. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are the same as the beneficial effects of the shift control method provided in the above embodiments, and will not be repeated here.

[0110] The above descriptions are only some embodiments and do not limit the patent scope of this embodiment. All equivalent structural transformations made based on the technical concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A gear shifting control method, characterized in that, The method includes: Upon detecting a basic shift command, the current vehicle driving status data is acquired, and the current vehicle mass and current road gradient are obtained based on the driving status data using an adaptive capacitive Kalman filter algorithm. The current load level of the current vehicle is determined based on the current vehicle mass and the preset load classification strategy, and the current slope level of the current vehicle is determined based on the current road slope and the preset slope classification strategy. The current operating condition level is determined based on the current load level and the current slope level. The basic shift command is then processed according to the current operating condition level to obtain the target gear command, so that the current vehicle performs shift control according to the target gear command.

2. The method as described in claim 1, characterized in that, The step of obtaining the current vehicle mass and current road gradient based on the driving state data using an adaptive capacitive Kalman filter algorithm includes: The collected driving status data is filtered and preprocessed to obtain preprocessed observation data; The system state equations are constructed based on the random walk model, and the nonlinear observation equations are constructed based on the longitudinal dynamics of the vehicle. Based on the preprocessed observation data, the system state equation and the nonlinear observation equation are recursively estimated using an adaptive capacitive Kalman filter algorithm to obtain the current vehicle mass and the current road slope.

3. The method as described in claim 2, characterized in that, The step of obtaining the current vehicle mass and current road slope by performing state recursion estimation on the system state equation and the nonlinear observation equation based on the preprocessed observation data using an adaptive capacitive Kalman filter algorithm includes: Obtain the state estimate and covariance matrix of the previous time step; Based on the state estimate of the previous time step and the covariance matrix of the previous time step, the initial volume points are generated according to the sphere diameter volume criterion. Substitute the initial volume point into the system state equation for time update to obtain the predicted state mean and predicted covariance. The target volume point is obtained based on the predicted state mean and predicted covariance, and the target volume point is substituted into the nonlinear observation equation to perform observation prediction and obtain predicted observation data. Obtain the process noise covariance matrix, determine the innovation sequence based on the predicted observation data and the preprocessed observation data, and adjust the process noise covariance based on the innovation sequence; Based on the information sequence and the adjusted process noise covariance matrix, the predicted state mean and predicted covariance are updated to obtain a real-time estimate of the vehicle quality as the current vehicle quality. Accelerometer data is acquired from the preprocessed observation data, and based on the acceleration sensor data and the vehicle longitudinal dynamics model, a real-time estimate of the road slope is obtained through an extended Kalman filter observer as the current road slope.

4. The method as described in claim 1, characterized in that, Before the steps of determining the current load class of the current vehicle based on the current vehicle mass and a preset load classification strategy, and determining the current slope class of the current vehicle based on the current road slope and a preset slope classification strategy, the method further includes: Obtain the set curb weight and maximum permissible total weight, and construct load determination thresholds for three load levels: light load, medium load, and heavy load based on the curb weight and the maximum permissible total weight. Obtain the set slope critical threshold, and construct slope determination thresholds for two slope levels, namely small slope and large slope, based on the slope critical threshold.

5. The method as described in claim 1, characterized in that, The step of intervening in the basic shift command based on the current operating condition level to obtain the target gear command includes: When the current operating condition level is heavy load and steep uphill condition, the anti-cycle shifting strategy is executed; When the current working condition level is heavy load downhill working condition, a forced locking strategy is executed; When the current operating condition level is a light-load downhill condition, an active downshift strategy is executed.

6. The method as described in claim 5, characterized in that, The steps for implementing the anti-cyclic shifting strategy include: Monitor the current vehicle speed. When the current vehicle speed reaches the upshift threshold of the basic shifting rule, forcibly block the upshift request and keep the current gear unchanged.

7. The method as described in claim 5, characterized in that, The steps for implementing the forced locking strategy include: It forcibly prohibits upshifting and downshifting operations, locks the current gear, and maintains the mechanical connection of the power transmission system.

8. A gear shifting control device, characterized in that, The device includes: The perception module is used to acquire the current vehicle driving status data when a basic shift command is detected, and to obtain the current vehicle mass and current road slope based on the driving status data through an adaptive capacitive Kalman filter algorithm. The working condition determination module is used to determine the current load level of the current vehicle based on the current vehicle mass and a preset load classification strategy, and to determine the current slope level of the current vehicle based on the current road slope and a preset slope classification strategy. An intervention module is used to determine the current operating condition level based on the current load level and the current slope level, and to intervene in the basic gear shifting command according to the current operating condition level to obtain a target gear command, so that the current vehicle performs gear shifting control according to the target gear command.

9. A gear shifting control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the shift control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the shift control method as described in any one of claims 1 to 7.

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