Vehicle wheel damping control strategy safety system based on low-voltage battery system

Abstract

The invention discloses a vehicle wheel damping control strategy safety system based on a low-voltage battery system, which relates to the technical field of vehicle automatic control, and comprises a signal acquisition and processing module used for acquiring vehicle driving data and denoising by an edge computing node; the control logic module is used for inputting the acquired vehicle driving into a data neural network model for risk assessment; the third-stage damping module is used for controlling a damping combination structure to execute different damping modes according to the obtained risk assessment result in cooperation with the multi-stage trigger logic; the energy management module is used for recovering electric energy when the third-stage damping module is used for braking; the low-voltage battery system and emergency power supply module is used for emergently supplying power to the third-stage damping module; wherein the three-stage damping module can execute a basic damping mode, an enhanced damping mode or a limit damping mode, and through a multi-stage damping mechanism, the safety and stability of the vehicle under the emergency condition are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle automatic control, and particularly relates to a vehicle wheel damping control strategy safety system based on a low-voltage battery system. Background Art

[0002] In the existing technical solutions, the automatic control for driving safety during vehicle driving is for aspects such as vehicle speed, vehicle acceleration, and vehicle steering during vehicle driving, and is directly achieved by controlling the motor speed of the vehicle, as well as the braking force and kinetic energy recovery during braking. Since the existing vehicles do not have control over the damping system, the safety control function coverage of the vehicles is insufficient, mainly focusing on the control strategy of energy recovery, lacking direct intervention measures for the dynamic driving safety of the vehicle, and unable to provide instant damping force assistance during emergency braking or out-of-control situations. Further, it leads to low system integration, focusing on the energy recovery system itself, with limited synergy with other vehicle systems, low integration, unable to fully exert the synergy between systems, and at the same time, the application scenarios are limited. Its application scenarios are mainly concentrated on energy recovery and fuel-saving effects, applicable to relatively stable driving conditions, and unable to interfere with dangerous situations in advance. Summary of the Invention

[0003] Object of the Invention: The technical problem to be solved by the present invention is to provide a vehicle wheel damping control strategy safety system based on a low-voltage battery system, which solves the problem of insufficient function coverage in the existing vehicle driving safety control and the lack of control for the damping system.

[0004] Technical Solution

[0005] To solve the above problems, the technical solution provided by the present invention is as follows:

[0006] A vehicle wheel damping control strategy safety system based on a low-voltage battery system, comprising

[0007] A signal acquisition and processing module, which is used to obtain vehicle driving data and denoise it by an edge computing node;

[0008] A control logic module, which is used to input the collected vehicle driving into a data neural network model for risk assessment;

[0009] A three-stage damping module, which is used to execute different damping modes for the obtained risk assessment result in cooperation with a multi-level trigger logic to control a damping combination structure;

[0010] An energy management module, which is used to recover electric energy when the three-stage damping module brakes;

[0011] A low-voltage battery system and an emergency power supply module, which are used to supply emergency power to the three-stage damping module;

[0012] Among them, the three - stage damping module can execute the basic damping mode, enhanced damping mode or extreme damping mode.

[0013] Further, the signal acquisition and processing module includes sensors for collecting different vehicle driving information and edge computing nodes arranged at different positions on the vehicle body. The edge computing nodes are equipped with vehicle data processing chips, and data pre - processing algorithms are provided in the vehicle data processing chips. The data pre - processing algorithms include the Kalman filtering algorithm and the dynamic sliding window analysis algorithm.

[0014] Further, the neural network model consists of an input layer, a hidden layer and an output layer. The input layer includes 12 neurons corresponding to 12 - dimensional time - series data of vehicle driving information; the hidden layer adopts a three - layer gated recurrent unit structure; the output layer contains one neuron, and outputs a value between 0.1 and 1.

[0015] Further, for the risk value between 0.1 and 1 output by the neural network model, 0.4 is used as the bottom - line value of the first - level threshold, 0.6 is used as the bottom - line value of the second - level threshold, and 0.8 is used as the bottom - line value of the third - level threshold; when the risk value output by the neural network model is between 0.4 and 0.6, it is determined that the first - level threshold is breached; when the risk value is between 0.6 and 0.8, it is determined that the second - level threshold is breached; when the risk value is greater than 0.8, it is determined that the third - level threshold is breached.

[0016] Further, the multi - level trigger logic controls the three - stage damping module to enter different damping modes. When the output value of the neural network model breaches the first - level threshold, the multi - level trigger logic controls the three - stage damping module to start the basic damping mode; when the output value of the neural network model breaches the second - level threshold, the multi - level trigger logic controls the three - stage damping module to activate the enhanced damping mode; when the output value of the neural network model breaches the third - level threshold, the multi - level trigger logic controls the three - stage damping module to trigger the extreme damping mode.

[0017] Further, the damping combination structure includes a suspension damping part and a brake damping part. The basic damping mode and the enhanced damping mode directly act on the suspension damping part, and the extreme damping mode simultaneously controls the suspension damping part and the brake damping part.

[0018] Further, the suspension damping part is a magnetorheological damper, and the brake damper includes a piezoelectric ceramic locking mechanism and a backup friction plate.

[0019] Further, for the basic damping mode: the control module of the magnetorheological damper dynamically adjusts the current applied to the damper coil through pulse width modulation technology. By maintaining the current in the damper coil within the range of 0.5 - 1.2 A, the damping force of the suspension damper smoothly varies within the range of 500 - 1500 N. And in this stage, the piston of the suspension damper adopts a progressive slotted design;

[0020] For the enhanced damping mode: the high-speed solenoid valve opens the hydraulic auxiliary circuit, causing the suspension damping fluid pressure to jump from the base value to 3000 N within 50 ms. The secondary control circuit activates the embedded piezoelectric stack to precisely adjust the opening and closing angle of the damping valve plate, realizing the gradient enhancement of the suspension damping force. The distribution ratio of the suspension damping forces of the front and rear wheels of the vehicle is 1:1.2 ~ 1:1.5;

[0021] For the extreme damping mode: the brake damping triggers the piezoelectric ceramic locking mechanism to apply a pre-tightening force of 2000 N to the vehicle brake disc. The piston rod of the suspension damper is instantaneously locked by the mechanical limit claws made of shape memory alloy, and the electromagnetic clutch device is driven to press the spare friction plate group against the brake disc within 10 ms, generating a directional braking torque.

[0022] Further, the training mode of the neural network model is as follows: the recorded vehicle driving information that has occurred is segmented at intervals of 30. The vehicle information in the first second interval of each 30 - second interval is used as input data for the neural network model to identify the change in the vehicle driving state within 1 second through the change in the received data, and calculate the change value of the vehicle driving data through data. The change in the vehicle driving state in the 30 - second interval is converted into a value between 0.1 - 1 in the form of risk data as the output end of the neural network model. The data change value in the first second interval of the interval obtained by the neural network model is fitted with the output value of the vehicle driving state in the subsequent 30 seconds, so as to obtain the judgment strategy between the change in the vehicle driving data in the first second interval of the 30 - second interval and the vehicle driving risk value in the 30 - second interval, and the training of the neural network model is completed through continuous verification and repetition.

[0023] Further, the energy management module integrates a regenerative braking energy recovery device, which converts the kinetic energy of the wheel deceleration into low - voltage DC electrical energy through a three - phase inverter.

[0024] Beneficial Effects

[0025] Adopting the technical solution provided by the present invention, compared with the prior art, it has the following beneficial effects:

[0026] Wheel damping control: Through a multi-level damping mechanism, the safety and stability of the vehicle in emergency situations are comprehensively improved. During emergency braking, the system rapidly increases the damping force to assist the braking system and shorten the braking distance; when the vehicle is out of control, the vehicle attitude is stabilized by enhancing the damping force to ensure driving safety.

[0027] System integration: The deep integration of damping control with other vehicle safety systems (such as ABS and ESC). In emergency situations, the damping control system works in coordination with ABS to prevent wheel lock-up; it works in coordination with ESC to stabilize the vehicle attitude and provide more comprehensive safety protection. This deep integration ensures efficient cooperation between systems and improves the overall safety performance.

[0028] Wider application scenarios: including dangerous situations such as emergency braking, high-speed driving, and downhill sections. Through the multi-level damping mechanism and energy recovery function, the system can provide effective safety protection under different working conditions. For example, it rapidly increases the damping force during emergency braking, maintains vehicle stability during high-speed driving, and prevents speed loss during downhill sections to ensure the safety and stability of the vehicle under various complex working conditions. Description of the Drawings

[0029] Figure 1 It is a schematic structural diagram of Embodiment 1 of the present invention. Detailed Implementation Modes

[0030] To make the technical solution of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0031] Embodiment 1

[0032] Combined with the attached Figure 1 , a vehicle wheel damping control strategy safety system based on a low-voltage battery system includes,

[0033] A signal acquisition and processing module for obtaining vehicle driving data and denoising by an edge computing node;

[0034] A control logic module for inputting the collected vehicle driving into a data neural network model for risk assessment;

[0035] A three-level damping module for executing different damping modes for the obtained risk assessment results in cooperation with a multi-level trigger logic to control the damping combination structure;

[0036] An energy management module for recovering electric energy when the three-level damping module performs braking;

[0037] A low-voltage battery system and an emergency power supply module for supplying emergency power to the three-level damping module.

[0038] Preferably, the low-voltage battery system is a 48V battery system.

[0039] The signal acquisition and processing module consists of several different types of sensors and edge computing nodes for processing the data of the sensors.

[0040] The vehicle information is collected by sensors on functional components at different positions on the vehicle body. The data to be collected are key parameters reflecting the vehicle driving state, such as the speed, acceleration, steering angle, brake pedal pressure, throttle pedal opening, vehicle attitude angle, and vehicle surrounding environment information during vehicle driving. The acceleration includes the lateral acceleration and longitudinal acceleration of the vehicle; the vehicle attitude angle includes the pitch angle, yaw angle, and roll angle of the vehicle. Each different type of information can be collected separately and in real time by multiple sensors, and the accuracy of the vehicle real-time information is ensured by comparing the information collected by multiple sensors.

[0041] The sensors for collecting vehicle real-time information include high-precision wheel speed sensors based on the Hall effect principle, six-axis inertial measurement units composed of integrated accelerometers and gyroscopes, brake and throttle state monitors using laser displacement sensing technology, and millimeter-wave radar environment perception devices.

[0042] The edge computing node is equipped with a vehicle data processing chip. The vehicle data processing chip is provided with a data preprocessing algorithm, which consists of a Kalman filtering algorithm and a dynamic sliding window analysis algorithm. The Kalman filtering algorithm eliminates the information noise collected by the sensor group, and the dynamic sliding window analysis algorithm calculates the key features that the vehicle needs to further combine and compare to calculate through the denoised vehicle real-time data, including the vehicle speed volatility, acceleration change gradient, etc.

[0043] The sensors located at various positions of the vehicle transmit the collected information to the edge computing node in real time through the CAN bus. The edge computing node uploads the real-time vehicle information processed by the data preprocessing algorithm to the neural network model through the FlexRay protocol, realizing the full-link low-latency transmission from the original signal to the decision input. The overall processing delay of the data from collection to transmission to the neural network model is less than 20 ms.

[0044] The control logic module consists of a neural network model for calculating the driving risk of the vehicle in the next 30 seconds. The vehicle data of the signal acquisition and processing module are transmitted to the control logic module for risk calculation.

[0045] The collected vehicle information is denoised and then transmitted to the neural network model. The neural network model predicts the next risk probability of the vehicle based on the obtained real-time vehicle information.

[0046] The neural network model for predicting risk probability is a deep learning model constructed based on the LSTM (Long Short-Term Memory Network) architecture. It mainly predicts the risk probability within the next 30 seconds of the vehicle by receiving vehicle information.

[0047] The neural network model consists of an input layer, a hidden layer, and an output layer.

[0048] Input layer: It contains 12 neurons, which subdivide the received vehicle data corresponding to the 12-dimensional time series data of the input. These data include vehicle speed, acceleration, deceleration, steering angle, brake pedal pressure, throttle opening, lateral acceleration, longitudinal acceleration, vehicle attitude angles (pitch angle, yaw angle, roll angle), and the density of the surrounding environment, which are key parameters reflecting the vehicle driving state.

[0049] Hidden layer: It adopts a three-layer gated recurrent unit (GRU) structure. GRU is a simplified version of the LSTM unit. It combines the forget gate and the input gate into an update gate, with faster training speed and better performance. Each GRU unit contains a candidate hidden state, an update gate, a reset gate, etc. Through these structures, it controls the flow and update of information, so as to effectively capture the time series characteristics and long-term dependencies of the vehicle driving state.

[0050] Output layer: It contains one neuron, which outputs a value between 0.1 and 1, representing the probability of the occurrence of vehicle driving risk within the next 30 seconds. The closer the output value is to 1, the higher the risk; the closer it is to 0, the lower the risk.

[0051] The training mode of the neural network model is as follows:

[0052] The recorded vehicle driving information that has occurred is segmented at intervals of 30, and the vehicle information in the 1-second interval before each interval is used as input data. The vehicle information in the 1-second interval before is transmitted to the neural network model in the initial stage. The vehicle data within the 1-second interval is completely input to the input end of the neural network model, enabling the neural network model to identify the change in the vehicle driving state within 1 second through the change in the received data, and calculating the change value of the vehicle driving data through the data.

[0053] The change in the driving state of the vehicle in the 30 - second interval is converted into a value between 0.1 and 1 in the form of risk data as the output of the neural network model. If the driving state of the vehicle is stable without change or has a small change within the known subsequent 30 seconds, a value between 0.1 and 0.4 can be selected as the output value of the neural network model according to the vehicle state. If the vehicle driving changes within the subsequent 30 seconds and is at an abnormal level, a value in the range of 0.4 - 0.6 can be selected as the output value of the neural network model according to the vehicle state. If the vehicle driving changes within the subsequent 30 seconds and is at a dangerous level, a value in the range of 0.6 - 0.8 can be selected as the output value of the neural network model according to the vehicle state. If the vehicle driving changes within the subsequent 30 seconds and is at an extremely dangerous level, a value in the range of 0.8 - 1 can be selected as the output value of the neural network model according to the vehicle state.

[0054] The change value of the data in the 1 - second interval before the interval obtained by the neural network model is fitted with the output value of the vehicle driving state in the subsequent 30 seconds, so as to obtain a judgment strategy between the change in the vehicle driving data in the 1 - second interval before the 30 - second interval and the vehicle driving risk value in the 30 - second interval. By continuously adding the vehicle driving data in the 1 - second interval and the vehicle risk value in the 30 - second interval to the neural network model, the neural network model is continuously trained to continuously improve the accuracy of the judgment strategy. Finally, the neural network model can accurately correspond the risk value of the vehicle in the 30 - second interval predicted by the change in the vehicle driving data in the previous 1 - second period to the risk value of the known subsequent 30 - second vehicle driving state, thus completing the training of the neural network model.

[0055] The risk value output by the neural network model is between 0.1 and 1, and 0.4 is used as the bottom - line value of the first - level threshold, 0.6 is used as the bottom - line value of the second - level threshold, and 0.8 is used as the bottom - line value of the third - level threshold. When the risk value output by the neural network model is between 0.4 and 0.6, it is considered to break through the first - level threshold; when the risk value is between 0.6 and 0.8, it is considered to break through the second - level threshold; when the risk value is greater than 0.8, it is considered to break through the third - level threshold. The threshold including or not including the bottom - line value of the level threshold for the breakthrough level can be specified according to different vehicle types and usage environments.

[0056] The control logic module controls the three - stage damping module to execute different damping modes through multi - level trigger logic.

[0057] The breakthrough level determined by the risk value output by the neural network model is coordinated with the multi - level trigger logic, and the multi - level trigger logic controls the three - stage damping structure of the three - stage damping module to enter different damping modes. When breaking through the first - level threshold, the basic damping mode is started; when breaking through the second - level threshold, the enhanced damping mode is activated; when breaking through the third - level threshold, the extreme damping mode is triggered.

[0058] The control instructions of the multi-level trigger logic are synchronously sent to the three-level damping structure as the actuator through the dual redundant communication channels (CAN / FlexRay). At the same time, the control instructions of the multi-level trigger logic and the ABS and ESC systems use the standardized CAN protocol communication protocol for data exchange, share key information such as vehicle wheel speed, steering angle, yaw rate, etc. in real time, and coordinately adjust the vehicle's braking force distribution and posture stability control according to the pre-set joint control strategy. For example, during emergency braking, it cooperates with ABS to determine the optimal damping force of each wheel to avoid wheel locking; when the vehicle understeers or oversteers, it cooperates with ESC to adjust the damping force of the inner and outer wheels to enhance the steering stability of the vehicle.

[0059] The three-level damping module uses the risk output value of the neural network model of the control logic module to control the three-level damping structure to execute different damping modes according to the output value:

[0060] The risk value is 0.1-0.4, which does not exceed the first-level threshold bottom line value and belongs to the low-risk range. The vehicle driving state is relatively stable and the risk is low. At this time, the system maintains normal monitoring status and does not trigger any damping mode.

[0061] The risk value is 0.4-0.6, breaking the first-level threshold bottom line value, belonging to the first-level risk range. The vehicle's driving state is abnormal to a certain extent, and there is a certain risk. The multi-level trigger logic triggers the basic damping mode, provides basic damping force, makes preliminary adjustments to the vehicle's driving state, and enhances vehicle stability.

[0062] The risk value is 0.6-0.8, exceeding the bottom line of the second-level threshold value, belonging to the second-level risk range. The vehicle's driving state is relatively dangerous and the risk is high. The multi-level trigger logic immediately starts the enhanced damping mode, quickly increases the damping force, assists the braking system, shortens the braking distance, and works with the vehicle's ESP system to stabilize the vehicle's posture.

[0063] The risk value is 0.8-1.0, exceeding the bottom line of the third-level threshold value, belonging to the third-level risk range. The vehicle's driving state is extremely dangerous, and there is a serious risk of loss of control or extreme situations such as brake failure. The multi-level trigger logic directly triggers the extreme damping mode, providing maximum damping force in multiple dimensions, and taking multi-dimensional hard intervention measures on the vehicle to prevent the vehicle speed from getting out of control and ensure that the vehicle is within the safe speed range.

[0064] The damping combination structure is composed of a suspension damping part and a brake damping part. The basic damping mode and the enhanced damping mode act directly on the suspension damping part, and the extreme damping mode controls the suspension damping part and the brake damping part at the same time.

[0065] The suspension damping part is a magnetorheological damper. As the core component of the normal operation of the system, the suspension damper adopts the intelligent adjustment function of the magnetorheological damper to achieve continuous control. The brake damper is a piezoelectric ceramic locking mechanism and a spare friction plate. The wheels are decelerated through the piezoelectric ceramic locking mechanism and the spare friction plate.

[0066] The specific working conditions of the three-level damping mode are:

[0067] Basic damping mode: provides basic damping force, suitable for normal driving conditions.

[0068] When the vehicle is in a normal driving condition with a speed of ≤120km / h and a lateral acceleration of <0.3g, the control module of the magnetorheological damper dynamically adjusts the current applied to the damper coil through pulse width modulation (PWM) technology, and maintains the current on the damper coil in the range of 0.5-1.2A, so that the damping force of the suspension damper changes smoothly within the range of 500-1500N. At this stage, the suspension damper piston adopts a progressive slot design, which, combined with the characteristics of non-Newtonian fluid, can effectively absorb high-frequency micro-vibrations of the road surface, making the body amplitude less than 2mm, while maintaining the stiffness of the suspension system within the preset safety range of 8-12kN / m. The control algorithm automatically compensates for the damping deviation caused by tire wear or load changes by comparing the data difference between the wheel speed sensor and the body posture sensor in real time, with a compensation accuracy of ±3%.

[0069] Enhanced damping mode: Rapidly increase damping force in emergency braking or overspeeding, assist the braking system, and shorten the braking distance. When the system detects an emergency braking signal, that is, the brake pedal travel rate is greater than 80mm / s or the predicted risk value exceeds the secondary threshold, the suspension damping part immediately starts multi-mode collaborative control.

[0070] First, the hydraulic auxiliary circuit is opened through the high-speed solenoid valve, and the response time of the high-speed solenoid valve is less than 5ms, so that the suspension damping fluid pressure (damping force) jumps from the basic value to 3000N within 50ms. At the same time, the secondary control circuit activates the embedded piezoelectric stack, whose displacement resolution is 0.1μm, and precisely adjusts the opening and closing angle of the damping valve plate to achieve gradient enhancement of the suspension damping force. The adjustable range of the suspension damping valve plate opening and closing angle is 0°-15°. At this stage, the system establishes data communication with the electronic stability program (ESP), and dynamically distributes the damping force of each wheel according to the speed difference of the four wheels, so that the suspension damping force distribution ratio of the front and rear wheels of the vehicle is 1:1.2~1:1.5, which improves the support for the vehicle body and reduces the swing amplitude of the vehicle body. The energy recovery system is linked to improve the braking energy conversion efficiency, and the peak braking energy recovery power reaches 8kW.

[0071] Extreme Damping Mode: Provides maximum damping force in case of brake failure or extreme situations to prevent the vehicle speed from getting out of control. When the system determines that there is a risk of loss of control, that is, when the risk value breaks through the third-level threshold and lasts for more than 200 ms, or when a brake system failure signal is received, the extreme damping mode synchronously controls the suspension damping and brake damping to perform multi-dimensional hard intervention. First, the brake damping triggers the piezoelectric ceramic locking mechanism to apply a pre-tightening force of 2000 N to the vehicle wheel brake disc, causing the vehicle to decelerate initially, and instantaneously locks the piston rod of the suspension damper through a mechanical limit claw made of shape memory alloy (deformation recovery rate of 99.8%) to maximize the suppression of vehicle body swing.

[0072] At the same time, the three-level dedicated power module activates the supercapacitor discharge with an instantaneous current of 300 A, driving the electromagnetic clutch device to press the spare friction plate group against the brake disc within 10 ms to generate a directional braking torque, and the peak braking torque of the spare friction plate is 500 N / m. In the design, the extreme damping mode is only activated in extreme situations, and the energy recovery braking is preferentially enabled. Only when the energy recovery braking fails or is insufficient, the friction braking is superimposed to minimize the impact on the kinetic energy recovery efficiency. During this process, the system forcibly cuts off the power supply of non-essential on-vehicle electrical equipment, preferentially guarantees the energy supply of the damping control system through the 48V high-voltage bus, and activates an independent gyroscope for secondary verification of the vehicle body attitude (sampling rate of 1 kHz) to ensure the precise execution of the intervention action.

[0073] The three-level damping mode is not three completely independent damping devices, but three different control levels or control stages in a complete damping system. They act together on the same set of wheel damping devices, and through precise control of the magnitude and adjustment method of the damping force, achieve all-round control of the vehicle driving state.

[0074] The three-level damping mode executes the basic damping mode: Under normal driving conditions, it mainly plays the role of basic damping to maintain the stability of vehicle driving. Through the intelligent adjustment of the magnetorheological damper of the suspension damping, the damping force changes smoothly within a certain range to adapt to different road conditions and vehicle load changes, absorb high-frequency micro-vibrations of the road surface, and ensure the comfort and stability of vehicle driving. The first-level damping mainly acts on the front wheels. Under normal driving conditions, the dynamic characteristics of the front wheels have a greater impact on the stability and controllability of the vehicle. Therefore, the first-level damping acts on the front wheels preferentially to provide the basic damping force and maintain the stability of vehicle driving. Through the intelligent adjustment of the magnetorheological damper of the front wheels, the suspension damping force changes smoothly within a certain range to adapt to different road conditions and vehicle load changes, absorb high-frequency micro-vibrations of the road surface, and ensure the comfort and stability of vehicle driving.

[0075] The three - stage damping mode executes the enhanced damping mode: When the vehicle is in emergency situations such as emergency braking, speeding, or dynamic instability of the vehicle, the secondary damping module rapidly increases the damping force on the basis of the primary damping. By opening the hydraulic auxiliary circuit through a high - speed solenoid valve, the pressure of the suspension damping fluid rapidly rises. At the same time, the opening and closing angle of the damping valve plate is precisely adjusted by using an embedded piezoelectric stack, realizing a rapid gradient enhancement of the suspension damping force, assisting the braking system, shortening the braking distance, and working in coordination with the ESP system to stabilize the vehicle attitude. The secondary damping acts rapidly on the front and rear wheels in emergency situations such as emergency braking or speeding. When the system detects an emergency braking signal or the predicted risk value exceeds the secondary threshold, the enhanced damping mode is immediately activated. By opening the hydraulic auxiliary circuit through a high - speed solenoid valve, the pressure of the damping fluid on the front and rear wheels rapidly rises. At the same time, the opening and closing angle of the damping valve plate is precisely adjusted by using an embedded piezoelectric stack, realizing a rapid gradient enhancement of the suspension damping force. At this stage, the system establishes data intercommunication with the Electronic Stability Program (ESP), dynamically distributes the damping force of each wheel according to the rotational speed difference of the four wheels. Generally, the distribution ratio is 1:1.2 - 1.5 for the front wheel: rear wheel, to optimize the braking force distribution of the vehicle, assist the braking system, shorten the braking distance, and stabilize the vehicle attitude.

[0076] The three - stage damping mode executes the extreme damping mode: In the event of brake failure or when the vehicle is in an extremely dangerous situation, the tertiary damping module serves as the last means of safety protection, providing the maximum damping force for suspension damping and brake damping. Trigger the piezoelectric ceramic locking mechanism to provide a pre - tightening force on the vehicle brake disc, lock the piston rod of the suspension damper with a mechanical limit claw made of shape - memory alloy. At the same time, start the tertiary dedicated power module to drive the electromagnetic clutch device to press the spare friction plate group against the brake disc, generating a directional braking torque, forcibly reducing the vehicle speed, preventing the vehicle speed from getting out of control, and maximizing the safety of the vehicle and its occupants. The extreme damping acts comprehensively on the front and rear wheels in the event of brake failure or extreme danger. When the system determines that there is a risk of out - of - control or receives a signal of brake system failure, the extreme damping mode performs multi - dimensional hard intervention. Trigger the piezoelectric ceramic locking mechanism, instantaneously lock the piston rods of the front and rear wheel dampers with mechanical limit claws made of shape - memory alloy. At the same time, start the tertiary dedicated power module to drive the electromagnetic clutch device to press the spare friction plate group against the front and rear wheel brake discs, generating a directional braking torque, forcibly reducing the vehicle speed, preventing the vehicle speed from getting out of control, and maximizing the safety of the vehicle and its occupants.

[0077] The damping modes in three different vehicle driving states can not only provide precise damping force control according to different driving conditions, but also achieve rapid response and seamless switching of the damping force, ensuring the driving safety and stability of the vehicle under various complex working conditions. Through the three - stage control of the same damping device, problems such as increased system complexity, rising costs, and space occupation caused by using multiple independent damping devices are avoided, improving the integration and reliability of the system.

[0078] Energy Management Module: Responsible for managing the recovered electric energy, mainly enhancing the damping mode and the extreme damping mode. During vehicle braking, the electric energy recovery system converts the kinetic energy generated during braking into electric energy and feeds it back to the 48V system, and provides power supply support in case of emergency. This system integrates a regenerative braking energy recovery device, which converts the kinetic energy of the wheel deceleration into 48V DC electric energy through a three-phase inverter (conversion efficiency ≥ 92%), and stores it in the lithium battery pack through a bidirectional DC-DC converter. The energy management unit monitors the system load status in real time and uses a dynamic power distribution algorithm to prioritize the power supply requirements of the damping control system. When the main power supply is detected to be abnormal, the standby supercapacitor bank can complete seamless switching within 5ms and provide at least 30 seconds of emergency power support for the key control unit. The system is equipped with an intelligent insulation monitoring device, which can detect the insulation impedance of the high-voltage circuit in real time (detection accuracy ±0.1MΩ) to ensure electrical safety.

[0079] Low-Voltage Battery System and Emergency Power Supply Module: Provide stable power support for the three-stage damping module and the control system, and switch to the standby power supply when necessary.

[0080] System Working Principle:

[0081] Signal Acquisition and Processing Module: Real-time monitors the vehicle status through speed sensors, acceleration sensors and brake sensors, and performs local processing through an edge computing chip to reduce the load on the central controller. The processed data is sent to the control logic module through the CAN bus and the FlexRay protocol.

[0082] Control Logic Module: Adopts a prediction model based on the LSTM neural network, inputs the historical speed and acceleration curves, and outputs the risk probability for the next 30 seconds. When the risk level reaches the preset threshold, it triggers the basic, enhanced, and extreme damping modes.

[0083] Basic Damping Mode: Provides basic damping force during normal driving to ensure vehicle driving stability.

[0084] Enhanced Damping Mode: Rapidly increases the damping force during emergency braking or speeding, assisting the braking system and shortening the braking distance.

[0085] Extreme Damping Mode: Provides the maximum damping force in case of brake failure or extreme situations to prevent the vehicle speed from getting out of control.

[0086] Energy Management Module: Converts the kinetic energy generated when the vehicle decelerates or brakes into electric energy through the energy recovery system and stores it in the 48V battery. In case of emergency, it provides power supply support for the key systems of the vehicle through a bidirectional DC-DC converter.

[0087] Low-voltage battery system and emergency power supply module: Provide stable power support for the damping device and control system, and automatically switch to the backup power supply when the 48V battery power is lower than the preset threshold.

[0088] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A vehicle wheel damping control strategy safety system based on a low-voltage battery system, characterized in that, including a signal acquisition and processing module, configured to obtain vehicle driving data and perform denoising by an edge computing node; a control logic module, configured to input the acquired vehicle driving data into a data neural network model for risk assessment; a three-stage damping module, configured to cooperate with a multi-stage trigger logic to control a damping combination structure to execute different damping modes according to the obtained risk assessment result; an energy management module, configured to recover electric energy when the three-stage damping module performs braking; a low-voltage battery system and an emergency power supply module, configured to supply power to the three-stage damping module emergently; wherein, the three-stage damping module is capable of executing a basic damping mode, an enhanced damping mode or an extreme damping mode.

2. The vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 1, characterized in that The signal acquisition and processing module includes sensors for collecting different vehicle driving information arranged at different positions on the vehicle body and an edge computing node. The edge computing node is equipped with a vehicle data processing chip, and a data preprocessing algorithm is provided in the vehicle data processing chip. The data preprocessing algorithm includes a Kalman filtering algorithm and a dynamic sliding window analysis algorithm.

3. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 1, characterized in that, The neural network model is composed of an input layer, a hidden layer and an output layer. The input layer includes 12 neurons, corresponding to 12-dimensional time series data of vehicle driving information. The hidden layer adopts a three-layer gated recurrent unit structure. The output layer contains one neuron, and outputs a value between 0.1 and 1.

4. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 3, characterized in that, For the risk value between 0.1 and 1 output by the neural network model, 0.4 is used as the bottom line value of the first-level threshold, 0.6 is used as the bottom line value of the second-level threshold, and 0.8 is used as the bottom line value of the third-level threshold. When the risk value output by the neural network model is between 0.4 and 0.6, it is determined that the first-level threshold is breached. When the risk value is between 0.6 and 0.8, it is determined that the second-level threshold is breached. When the risk value is greater than 0.8, it is determined that the third-level threshold is breached.

5. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 4, characterized in that, The multi-stage trigger logic controls the three-stage damping module to enter different damping modes. When the output value of the neural network model breaches the first-level threshold, the multi-stage trigger logic controls the three-stage damping module to start the basic damping mode. When the output value of the neural network model breaches the second-level threshold, the multi-stage trigger logic controls the three-stage damping module to activate the enhanced damping mode. When the output value of the neural network model breaches the third-level threshold, the multi-stage trigger logic controls the three-stage damping module to trigger the extreme damping mode.

6. The safety system for vehicle wheel damping control strategy based on a low-voltage battery system according to claim 5, characterized in that, The damping combination structure includes a suspension damping part and a brake damping part. The basic damping mode and the enhanced damping mode act directly on the suspension damping part. The extreme damping mode controls both the suspension damping part and the brake damping part simultaneously.

7. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 6, characterized in that, The suspension damping part is a magnetorheological damper. The brake damper includes a piezoelectric ceramic locking mechanism and a backup friction plate.

8. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 7, characterized in that, The basic damping mode: The control module of the magnetorheological damper dynamically adjusts the current applied to the damper coil through pulse width modulation technology. By maintaining the current on the damper coil in the range of 0.5 - 1.2 A, the damping force of the suspension damper smoothly changes in the range of 500 - 1500 N, and in this stage, the piston of the suspension damper adopts a progressive slotted design; The enhanced damping mode: The high-speed solenoid valve opens the hydraulic auxiliary circuit, causing the suspension damping fluid pressure to jump from the base value to 3000 N within 50 ms. The secondary control circuit activates the embedded piezoelectric stack to precisely adjust the opening and closing angle of the damping valve plate, realizing the gradient enhancement of the suspension damping force. The distribution ratio of the suspension damping force between the front and rear wheels of the vehicle is 1:1.2 to 1:1.5; The extreme damping mode: The brake damping triggers the piezoelectric ceramic locking mechanism to apply a pre-tightening force of 2000 N to the wheel brake disc. The mechanical limit claw made of shape memory alloy instantaneously locks the piston rod of the suspension damper, and drives the electromagnetic clutch device to press the spare friction plate group against the brake disc within 10 ms to generate a directional braking torque.

9. A safety system for a vehicle wheel damping control strategy based on a low-voltage battery system according to claim 1, characterized in that, The training mode of the neural network model is as follows: The recorded vehicle driving information that has occurred is segmented at intervals of 30. The vehicle information in the first second interval of each 30-second interval is used as input data for the neural network model to identify the change in the vehicle driving state within 1 second through the change in the received data, and calculate the change value of the vehicle driving data through the data. The change in the vehicle driving state in the 30-second interval is converted into a value of 0.1 - 1 in the form of risk data as the output end of the neural network model. The data change value in the first second interval of the interval obtained by the neural network model is fitted with the output value of the vehicle driving state in the subsequent 30 seconds, so as to obtain the judgment strategy between the change in the vehicle driving data in the first second interval of the 30-second interval and the vehicle driving risk value in the 30-second interval, and continuously verify and repeat to complete the training of the neural network model.

10. A vehicle wheel damping control strategy safety system based on a low-voltage battery system according to claim 1, characterized in that, The energy management module integrates a regenerative braking energy recovery device, which converts the kinetic energy of the wheel deceleration into low-voltage DC electrical energy through a three-phase inverter.

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