A Vehicle Redundant Power Supply Method, Device, Equipment and Storage Medium

Abstract

The present application provides a vehicle redundant power supply method, device, equipment and storage medium. The device consists of a sensor, an energy converter, a control unit, a backup capacitor and a collision detection and energy distribution module. The sensor is responsible for monitoring driving parameters such as vehicle speed, acceleration and braking force. The energy converter converts kinetic energy into electrical energy when the vehicle brakes. The control unit determines the vehicle driving state based on the data provided by the sensor and determines the energy recovery ratio according to the preset energy recovery rate mapping relationship. The backup capacitor serves as a temporary energy storage device, receives and stores the electrical energy converted from kinetic energy. When the collision detection module identifies a collision event, it triggers the energy distribution module to work, and distributes the energy stored in the backup capacitor to the key functional units of the vehicle according to the preset priority. Through energy recovery and release, the device ensures the basic power supply requirements of the vehicle in an emergency state, reduces energy loss and effectively improves the vehicle safety performance.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and particularly to a vehicle redundant power supply method, device, equipment and storage medium. Background Art

[0002] With the rapid development of the automotive industry and the enhancement of environmental awareness, how to effectively utilize the energy generated during vehicle driving, especially the kinetic energy wasted during braking, has become the focus of research. When traditional cars brake, most of the kinetic energy is dissipated in the form of heat, which not only causes energy waste but also increases the burden on the environment. In recent years, although some new energy vehicles have adopted regenerative braking systems to recover part of the braking energy, their recovery efficiency and application scope are still limited.

[0003] The existing vehicle power supply system mainly relies on on-vehicle batteries or generators to provide power, which meets the vehicle electrification requirements to a certain extent. However, in case of emergencies such as collision accidents, the conventional power supply may fail, resulting in the inability of safety assistance systems to work properly and increasing the accident risk. In addition, the existing system lacks a flexible response mechanism to the changes in energy requirements under different vehicle driving states, and it is difficult to achieve effective management and distribution of energy. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, this application provides a vehicle redundant power supply method, device, equipment and storage medium to solve the above technical problems.

[0005] A vehicle redundant power supply device provided by this application, the device includes: a sensor for monitoring vehicle driving parameters, the vehicle driving parameters including vehicle speed, acceleration and braking force; a control unit connected to the sensor for determining the vehicle driving state based on the vehicle driving parameters and determining the current energy recovery rate corresponding to the current vehicle driving state according to a preset energy recovery rate mapping relationship, the energy recovery rate mapping relationship being used to characterize the relationship between the vehicle driving state and the energy recovery rate; an energy converter for converting the kinetic energy generated when the vehicle brakes into electric energy according to the current energy recovery rate; a spare capacitor connected to the energy converter and the control unit for storing the electric energy converted from the kinetic energy into the spare capacitor; a collision detection module for monitoring the occurrence state of a collision event and transmitting the collision signal generated when the collision event occurs to an energy distribution module; an energy distribution module connected to the spare capacitor and the collision detection module for, when detecting a collision signal, distributing the energy in the spare capacitor to each functional unit of the vehicle according to a preset priority order.

[0006] In one embodiment of the present application, the energy distribution module includes a neural network, which is used to predict the working duration of combinations of various functional units of the vehicle powered by the backup capacitor under different stored powers of the backup capacitor and different vehicle driving states.

[0007] In one embodiment of the present application, the neural network includes: a data collection module, which is used to collect the working duration data of different functional units under different remaining energy conditions; a data preprocessing module, which is used for data cleaning, feature selection, data normalization and data segmentation; a multi-layer perceptron model, including an input layer, a hidden layer and an output layer; a training module, which is used to train the initial model using a preset loss function and optimizer to obtain a working duration prediction model; an evaluation module, which is used to evaluate the prediction accuracy of the working duration prediction model using a test set. If the prediction accuracy is lower than a preset accuracy threshold, the working duration prediction model is optimized until its prediction accuracy is greater than or equal to the preset accuracy threshold.

[0008] In one embodiment of the present application, the energy distribution module further includes: a priority sorting module, which is used to sort the power supply priorities of functional units according to the prediction results and preset functional priorities; an energy distribution calculation module, which is used to preferentially allocate energy to functional units with higher power supply priorities; a real-time adjustment module, which is used to dynamically adjust energy distribution according to the real-time monitored energy consumption and remaining energy.

[0009] The present application provides a vehicle redundant power supply method, and the method includes: monitoring vehicle driving parameters, where the vehicle driving parameters at least include vehicle speed, acceleration and braking force; comparing the vehicle driving parameters with a preset vehicle driving parameter threshold to determine the driving state of the vehicle, and determining a current energy recovery rate matching the current vehicle driving state according to a preset energy recovery rate mapping relationship, where the energy recovery rate mapping relationship is used to characterize the relationship between the vehicle driving state and the energy recovery rate; converting the kinetic energy generated when the vehicle brakes into electric energy according to the current energy recovery rate, and storing the electric energy in a backup capacitor; monitoring the occurrence status of a collision event. If a collision event is detected, a collision signal is generated and the collision signal is transmitted to the energy distribution module; when the energy distribution module receives the collision signal, energy is allocated from the backup capacitor to each functional unit of the vehicle according to a preset priority order.

[0010] In an embodiment of the present application, before distributing energy from the backup capacitor to each functional unit of the vehicle according to a preset priority order, the method further includes: obtaining the current stored power of the backup capacitor, the current driving state of the vehicle, and the target power supply duration; based on the trained working duration prediction model, predicting the working duration of each functional unit combination powered by the backup capacitor under the current stored power and the current driving state, so as to obtain the available working duration of various functional unit combinations; determining the functional unit combination with any available working duration greater than the target power supply duration as the target functional unit combination, and determining each functional unit in the target functional unit combination as the target functional unit.

[0011] In an embodiment of the present application, distributing energy from the backup capacitor to each functional unit of the vehicle according to a preset priority order includes: obtaining the importance score and urgency score of each target functional unit, as well as the weight ratio of importance and urgency; calculating the importance score and urgency score of each target functional unit based on the weight ratio to obtain the comprehensive score of each target functional unit; sorting each target functional unit of the vehicle according to the comprehensive score to obtain a power supply priority list; obtaining the basic energy requirement of each target functional unit, where the basic energy requirement is the minimum energy requirement for maintaining the startup of the functional unit; assigning a weighting coefficient to each target functional unit based on the power supply priority list, and distributing the current stored power of the backup capacitor to each target functional unit of the vehicle based on the weighting coefficient.

[0012] In an embodiment of the present application, after distributing energy from the backup capacitor to each functional unit of the vehicle, the method further includes: obtaining user feedback, where the user feedback includes user satisfaction and user-suggested power supply priority; when the user satisfaction is lower than the preset satisfaction threshold, using the user-suggested power supply priority as an input item to optimize the training of the working duration prediction model.

[0013] The present application provides an electronic device, including a processor, a memory, and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to implement the vehicle redundant power supply method as described above.

[0014] The present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used to make a computer execute the vehicle redundant power supply method as described above.

[0015] Advantages of this application: In the vehicle redundant power supply device of this application, the kinetic energy generated when the vehicle brakes is efficiently converted into electrical energy through an energy converter and stored in a backup capacitor, reducing the waste of energy during braking and providing an additional power source for the vehicle. The control unit can accurately judge the driving state of the vehicle based on the vehicle driving parameters monitored by sensors, and automatically adjust the storage ratio of electrical energy according to the preset energy recovery rate mapping relationship, making the collection and storage of energy more efficient and reasonable, and adapting to the changes in energy requirements under different driving conditions. The combined use of the collision detection module and the energy distribution module can quickly respond and distribute the energy in the backup capacitor to the key functional units of the vehicle according to the preset priority in case of emergencies such as collisions, ensuring that these systems can operate normally even when the main power supply fails, greatly improving the safety performance of the vehicle in emergency situations. In summary, by integrating advanced sensing technology, energy conversion technology and intelligent control strategies, this application not only solves the deficiencies of traditional vehicles in energy recovery and management, but also provides an effective solution for improving the overall performance and safety of vehicles.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Brief Description of the Drawings

[0017] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments in line with this application, and are used together with the specification to explain the principles of this application. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0018] Figure 1 is a schematic diagram of the implementation environment of the vehicle redundant power supply method shown in an exemplary embodiment of this application;

[0019] Figure 2 is a schematic diagram of the structure of the vehicle redundant power supply device shown in an exemplary embodiment of this application;

[0020] Figure 3 is a flowchart of the implementation steps of the vehicle redundant power supply method shown in an exemplary embodiment of this application;

[0021] Figure 4 shows a schematic diagram of the structure of a computer system of an electronic device suitable for implementing the embodiments of this application. Detailed Embodiments

[0022] The embodiments of the present application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application and not for limiting the protection scope of the present application.

[0023] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0024] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0025] Figure 1 It is a schematic diagram of the implementation environment of the vehicle redundant power supply method shown in an exemplary embodiment of the present application.

[0026] As Figure 1 shown, the implementation environment of the vehicle redundant power supply method includes a sensor 101, an energy converter 102, a control unit 103, a backup capacitor 104, a collision detection module 105, and an energy distribution module 106. The functions of each component are described as follows:

[0027] Sensor 101: Responsible for monitoring key parameters during vehicle driving. These parameters at least include vehicle speed, acceleration, and braking force. These data are crucial for judging the current driving state of the vehicle and provide a necessary information basis for subsequent energy conversion and storage.

[0028] Energy converter 102: During vehicle braking, it can effectively convert the kinetic energy generated when the vehicle decelerates into electrical energy. This process not only helps to recover part of the energy that is usually wasted in the form of heat but also provides an additional power source for the vehicle, improving energy utilization efficiency.

[0029] Control Unit 103: Receives data from Sensor 101 and compares the vehicle driving parameters with preset thresholds to identify the vehicle's driving state. According to the preset energy recovery rate mapping relationship, the control unit can determine the optimal energy recovery rate that matches the current driving state. This mapping relationship defines the optimal electric energy storage strategy under specific driving conditions, ensuring the effective management and utilization of energy.

[0030] Backup Capacitor 104: As a temporary electric energy storage device, Backup Capacitor 104 can receive and store the electric energy converted by Energy Converter 102 according to the energy recovery rate determined by Control Unit 103. In emergency situations, such as when a collision occurs, the electric energy in the backup capacitor can be quickly released to provide necessary power support for the vehicle's critical systems.

[0031] Collision Detection Module 105: Continuously monitors the vehicle's surrounding environment, especially being highly sensitive to possible collision events. Once a collision event is detected, it immediately generates a collision signal and sends it to Energy Distribution Module 106. The design of this module aims to improve the vehicle's safety performance and ensure a quick response in case of an accident.

[0032] Energy Distribution Module 106: After receiving the collision signal, it retrieves energy from Backup Capacitor 104 according to the preset priority order and distributes it to different functional units of the vehicle. This process ensures that even under extreme conditions, the vehicle's critical systems can continue to operate, such as the activation of the airbag and the activation of the emergency communication system, thus maximizing the protection of the occupants' safety.

[0033] In summary, the implementation environment design of the vehicle redundant power supply method fully considers the electric energy requirements of the vehicle during normal driving and emergencies. Through efficient energy conversion, precise control logic, and a fast energy distribution mechanism, the reliability and safety of the vehicle power supply system are improved.

[0034] Figure 2 It is a schematic structural diagram of the vehicle redundant power supply device shown in an exemplary embodiment of the present application.

[0035] As Figure 2As shown in the figure, the vehicle redundant power supply device consists of a sensor, an energy converter, a control unit, a backup capacitor, and a collision detection and energy distribution module. Among them, the sensor is used to monitor the vehicle driving parameters, and the energy recovery rate vehicle driving parameters include vehicle speed, acceleration, and braking force; the control unit is connected to the energy recovery rate sensor and is used to determine the vehicle driving state based on the vehicle driving parameters, and determine the current energy recovery rate corresponding to the current vehicle driving state according to the preset energy recovery rate mapping relationship. The energy recovery rate mapping relationship is used to characterize the relationship between the vehicle driving state and the energy recovery rate; the energy converter is used to convert the kinetic energy generated when the vehicle brakes into electrical energy according to the current energy recovery rate; the backup capacitor is connected to the energy recovery rate energy converter and the energy recovery rate control unit and is used to store the electrical energy converted from kinetic energy into the backup capacitor; the collision detection module is used to monitor the occurrence state of a collision event and transmit the collision signal generated when the collision event occurs to the energy distribution module; the energy distribution module is connected to the energy recovery rate backup capacitor and the energy recovery rate collision detection module and is used to distribute the energy in the backup capacitor to each functional unit of the vehicle in accordance with a preset priority order when a collision signal is detected.

[0036] In an embodiment of the present application, the energy recovery rate energy distribution module includes a neural network for predicting the working duration of combinations of each functional unit of the vehicle powered by the backup capacitor under different stored powers of the backup capacitor and different vehicle driving states. Specifically, the neural network includes: a data collection module for collecting the working duration data of different functional units under different remaining energy conditions; a data preprocessing module for data cleaning, feature selection, data normalization, and data segmentation; a multi-layer perceptron model including an input layer, a hidden layer, and an output layer; a training module for training the initial model using a preset loss function and optimizer to obtain a working duration prediction model; an evaluation module for evaluating the prediction accuracy of the working duration prediction model using a test set. If the prediction accuracy is lower than a preset accuracy threshold, the working duration prediction model is optimized until its prediction accuracy is greater than or equal to the preset accuracy threshold of the energy recovery rate.

[0037] In a specific embodiment of the present application, constructing the working duration prediction model includes the following steps:

[0038] First, define the types of data to be collected, including the energy consumption characteristics (such as power demand) of each safety function, the remaining energy level (such as the current stored charge of the backup capacitor), and the actual working duration under these conditions. Use the vehicle's sensors and data recording system to collect data under actual driving conditions, ensuring that the dataset covers various different driving situations and remaining energy levels, such as urban driving, highway driving, hard braking, etc. Organize the collected data into a format suitable for neural network processing, usually a structured table form, where each row in the table represents a record, and the columns include the type of safety function, current energy consumption, remaining energy level, and actual working duration.

[0039] Secondly, preprocess the collected data, including data cleaning, feature selection, data normalization, and data splitting. During the data cleaning process, remove outliers and noise, handle missing data, and ensure the integrity and accuracy of the data. Select the features most relevant to the prediction target, such as remaining energy, current energy consumption, type of safety function, etc. Normalize the input data so that its mean is 0 and the standard deviation is 1 to improve the training efficiency of the neural network, and split the dataset into a training set, a validation set, and a test set, usually in the ratio of 70% training set, 15% validation set, and 15% test set.

[0040] Then, select the Multi-Layer Perceptron (MLP) model as the neural network architecture. Define the number of nodes in the input layer to match the number of features, for example, 3 nodes (remaining energy, current energy consumption, type of safety function). Add a hidden layer and select the Sigmoid activation function. Define that the output layer usually has only one node and use the linear activation function f(x) = x, which represents the predicted working duration. For a regression problem, select the Root Mean Square Error (RMSE) as the loss function and use the Adam optimizer.

[0041] Then, initialize the weights ω and bias terms b of the neural network, usually using the random initialization method. Calculate the result of the input data passing through the network at the output layer, and use the loss function to calculate the difference between the predicted result and the true value. According to the result of the loss function, backpropagate the error through the network to calculate the gradient of each parameter. Use the Adam optimizer to update the weights and bias terms of the model according to the gradient. Repeat the above steps until the performance of the model on the validation set no longer improves. And use the test set to evaluate the prediction accuracy of the model, and calculate the error between the predicted value and the true value. Use the cross-validation method to evaluate the generalization ability of the model to ensure the stability of the model's performance on different datasets. Evaluate the performance of the model on the final test set to ensure the prediction accuracy and stability of the model.

[0042] Finally, deploy the trained model to an actual vehicle system to ensure that the model can operate in the actual environment. Use the model to predict the remaining energy and energy consumption data monitored in real time, and calculate the working duration of each functional unit. According to the prediction results of the model, adjust the energy distribution strategy to ensure that key functional units obtain sufficient energy support in emergency situations. Collect the feedback data after model deployment, including user satisfaction and the power supply priorities suggested by users, and further optimize the model based on the feedback data to improve the prediction accuracy of the model and user satisfaction.

[0043] In addition, in an embodiment of the present application, the energy distribution module further includes: a priority sorting module for sorting the power supply priorities of functional units according to the prediction results and preset functional priorities; an energy distribution calculation module for preferentially allocating energy to functional units with higher power supply priorities; and a real-time adjustment module for dynamically adjusting energy distribution according to the energy consumption and remaining energy monitored in real time.

[0044] As Figure 3 shown, in an exemplary embodiment, the vehicle redundant power supply method at least includes steps S310 to S350, which are introduced in detail as follows:

[0045] Step S310, monitor vehicle driving parameters through sensors. The vehicle driving parameters for energy recovery rate at least include vehicle speed, acceleration, and braking force.

[0046] In an embodiment of the present application, in order to achieve accurate monitoring of vehicle driving parameters, the following several sensors are used to collect the vehicle driving parameters, specifically as follows:

[0047] Vehicle speed sensor: Installed on the vehicle axle to monitor the vehicle speed in real time. The vehicle speed sensor usually adopts a Hall effect sensor or a magnetoresistive sensor, which can provide high-precision vehicle speed data.

[0048] Acceleration sensor: Installed at key positions on the vehicle chassis or body to monitor the longitudinal acceleration and lateral acceleration of the vehicle. The acceleration sensor usually adopts MEMS (Micro-Electro-Mechanical System) technology and has the characteristics of high sensitivity and fast response.

[0049] Braking force sensor: Installed in the vehicle braking system to monitor the braking force applied by the driver. The braking force sensor can be a pressure sensor or a strain gauge sensor, which can accurately measure the pressure change of the brake pedal.

[0050] It should be noted that the acquisition of sensor data requires high time resolution and accuracy to ensure that the system can respond to changes in the vehicle driving state in a timely and accurate manner. The specific implementation steps are as follows:

[0051] Data sampling frequency: Set the sampling frequency of the sensor, for example, sampling 100 times per second, to ensure the real-time and accuracy of the data.

[0052] Data transmission: The sensor transmits data to the control unit via the CAN (Controller Area Network) bus or other high-speed communication protocols. The CAN bus features high reliability and strong anti-interference ability, making it suitable for data transmission in the vehicle environment.

[0053] Data verification: Verify the received data in the control unit to ensure the integrity and accuracy of the data. Common verification methods include parity check, CRC (Cyclic Redundancy Check), etc.

[0054] Step S320, the control unit compares the energy recovery rate vehicle driving parameters with the preset vehicle driving parameter thresholds to determine the vehicle's driving state, and determines the current energy recovery rate matching the current vehicle driving state according to the preset energy recovery rate mapping relationship, where the energy recovery rate mapping relationship defines the corresponding relationship between the vehicle driving state and the energy recovery rate.

[0055] In an embodiment of the present application, when the control unit receives the sensor data, it immediately performs real-time processing and status monitoring on these data to evaluate the vehicle's driving condition (i.e., the vehicle driving state). The specific implementation process includes first performing data parsing, that is, the control unit extracts key parameters such as vehicle speed, acceleration, and braking force from the received raw data. Subsequently, in the state judgment stage, based on the data obtained from the above parsing, the control unit can judge the current driving state of the vehicle. For example, if the acceleration shows a positive value and exceeds the set threshold, it is considered that the vehicle is in an accelerating state; if the acceleration is negative and lower than a certain threshold, it indicates that the vehicle is decelerating; when the acceleration is close to zero, it can be judged that the vehicle is traveling at a constant speed; and once the braking force exceeds the preset threshold, it means that the vehicle is performing a braking operation. Through this series of steps, the dynamic behavior of the vehicle can be effectively monitored and analyzed.

[0056] Step S330, use an energy converter to convert the kinetic energy generated when the vehicle brakes into electrical energy according to the current energy recovery rate of the energy recovery rate, and store the electrical energy converted from the kinetic energy in the backup capacitor.

[0057] In an embodiment of the present application, according to the vehicle driving state, dynamically adjust the working state of the energy converter, that is, the kinetic energy recovery rate, to optimize the energy recovery efficiency. Specifically as follows:

[0058] First, define a mapping function f to convert the braking force into the recovery power of the energy converter:

[0059] Prec = k1·F brake + k2 Equation (1)

[0060] Where, P rec is the kinetic energy recovery rate, F brake is the braking force, k1 is the slope coefficient, and k2 is the intercept.

[0061] It should be noted that the slope coefficient and intercept in Equation (1) can be calibrated according to experimental data, and this application does not impose any restrictions on their calibration methods and specific values.

[0062] In addition, in order to avoid imposing too much load on the vehicle's drive system, the maximum recovery power is restricted as follows:

[0063] P rec = min(P max , f(F brake )) Equation (2)

[0064] Where, P rec is the kinetic energy recovery rate, P max is the maximum kinetic energy recovery rate, and F brake is the braking force.

[0065] Then, a PID controller is used to adjust the operating state of the energy converter to ensure the optimal energy recovery efficiency, as follows:

[0066]

[0067] Where, K p , K i , K d are the proportional, integral, and derivative gains respectively, and E(t) is the error of the braking force.

[0068] Step S340, the collision detection module monitors the occurrence status of collision events in real time. If a collision event is detected, a collision signal is generated and the energy recovery rate collision signal is transmitted to the energy distribution module.

[0069] In an embodiment of this application, in order to monitor the occurrence status of collision events in real time, a collision sensor, a signal processor, and a communication module are adopted. The collision sensors are installed at key positions such as the front and rear bumpers, sides, and underbody of the vehicle to detect the impact force on the vehicle. These sensors usually use acceleration sensors or piezoelectric sensors and can sensitively capture the acceleration change or pressure change during a collision. The signal processor is responsible for processing the data collected by the collision sensors and judging whether a collision event has occurred. The communication module is used to transmit the collision signal to the energy distribution module and can adopt methods such as CAN bus, LIN bus, or wireless communication.

[0070] The working process of the collision detection module is as follows: First, the collision sensors collect the acceleration or pressure data of various parts of the vehicle in real time at a high frequency (for example, 1000 times per second) to ensure the real-time and accuracy of the data. Then, the signal processor filters, amplifies, and digitizes the received raw data to eliminate noise and improve the signal-to-noise ratio. The signal processor determines whether a collision event has occurred based on the processed data. Common judgment methods include the acceleration threshold method, impact waveform analysis, and multi-sensor fusion. When the acceleration in a certain direction exceeds the preset threshold, or the impact waveform unique to a collision is detected, the signal processor considers that a collision has occurred. Once a collision event is detected, the signal processor immediately generates a collision signal and transmits the collision signal to the energy distribution module through a communication module (such as the CAN bus).

[0071] After receiving the collision signal, the energy distribution module immediately starts the emergency procedure. It quickly evaluates the current working state and remaining power of the supercapacitor to determine the available energy resources. According to the preset list of safety function priorities, the energy distribution module determines which functions need to be prioritized for energy allocation. The common priority order is: The highest priority is the door unlocking system and the emergency warning light; The highest priority is the vehicle positioning system and the interior lighting; The medium priority is the vehicle communication system; The low priority is the entertainment system. The energy distribution module first meets the basic energy requirements of each safety function, and then calculates the weighting coefficient of each function according to the importance and urgency of the safety function for energy allocation. At the same time, the energy distribution module dynamically adjusts the energy allocation according to the real-time monitored energy consumption and remaining energy to ensure that all key functions can obtain sufficient energy. Finally, the calculated energy distribution result is implemented into the system to ensure that each safety function can work properly. After implementation, continuously monitor the energy consumption and system status of each function to ensure the effectiveness of the energy allocation strategy. If the system state changes, or the actual energy consumption is significantly different from the prediction, dynamically adjust the energy allocation according to the real-time data to ensure that all key functions can operate normally in an emergency.

[0072] It can be understood that through the above embodiments, the vehicle redundant power supply method proposed in this application can monitor the occurrence status of collision events in real time, and when a collision event is detected, quickly generate a collision signal and transmit it to the energy distribution module. The energy distribution module allocates the energy in the supercapacitor to the key safety functions according to the preset priority order to ensure the normal operation of the important systems of the vehicle in an emergency, thereby improving the safety performance of the vehicle and providing more safety guarantees for passengers.

[0073] Step S350, when the energy distribution module receives the collision signal, allocate energy from the backup capacitor to each functional unit of the vehicle according to the preset priority order.

[0074] In one embodiment of the present application, before distributing energy from the backup capacitor to each functional unit of the vehicle according to a preset priority order, it further includes: obtaining the current stored power of the backup capacitor, the current driving state of the vehicle, and the target power supply duration; based on the trained working duration prediction model, predicting the working duration of each functional unit combination powered by the backup capacitor under the current stored power and the current driving state, so as to obtain the available working duration of various functional unit combinations; determining any functional unit combination with an available working duration greater than the target power supply duration as the target functional unit combination, and determining each functional unit in the target functional unit combination as the target functional unit.

[0075] In a specific embodiment of the present application, before distributing energy from the backup capacitor to each functional unit of the vehicle according to a preset priority order, the system first monitors the current stored power of the backup capacitor in real time through a built-in power detection circuit, which collects the voltage and current data of the backup capacitor with high precision and high frequency (for example, 100 times per second) and calculates the current stored power. At the same time, the system monitors the driving parameters of the vehicle in real time through sensors (such as a vehicle speed sensor, an acceleration sensor, and a brake force sensor), including the vehicle speed, acceleration, and brake force, and the control unit determines the current driving state of the vehicle based on these parameters, such as accelerating, decelerating, moving at a constant speed, or being stationary. The system also presets a target power supply duration, and according to the current driving state and safety requirements of the vehicle, for example, after a collision, the target power supply duration can be set to 30 seconds to ensure that key safety functions can operate normally in a short time.

[0076] Next, the system uses a pre-trained neural network model to predict the working duration of each functional unit combination powered by the backup capacitor based on the current stored power and the current driving state. The inputs of the model include the current stored power, the current driving state, and the energy consumption characteristics of each functional unit, and the output is the working duration of each functional unit combination. The model training data includes the actual working duration of different functional units under different remaining energy conditions, and is trained through a large amount of actual driving data to improve the prediction accuracy.

[0077] Based on the prediction results, the system filters out all combinations of functional units whose available working duration is greater than the target power supply duration. The specific steps are as follows: Input the current stored power and the current driving state into the working duration prediction model to calculate the working duration of each combination of functional units, and then filter out all combinations of functional units whose working duration is greater than the target power supply duration to form candidate target combinations of functional units. From the candidate target combinations of functional units, select one or more combinations as the final target combinations of functional units. The selection criteria can be to comprehensively consider the importance and urgency of the functional units, as well as the overall working duration of the combination. For example, assume that the two candidate target combinations of functional units are combination A (door unlocking system, emergency warning light) and combination B (door unlocking system, emergency warning light, interior lighting). The system can select combination A as the final target combination of functional units because its working duration is longer and it includes the functional units with the highest priority. In addition, when selecting one or more combinations from the candidate target combinations of functional units as the final target combinations of functional units, it can also be based on user preferences. This application does not make any restrictions on the selection method.

[0078] Finally, determine each functional unit in the target combination of functional units as a target functional unit. The energy distribution module preferentially distributes the energy in the backup capacitor to these target functional units according to the determined target combination of functional units to ensure their normal operation in an emergency. During the distribution process, the system continuously monitors the energy consumption of each functional unit and the remaining power of the backup capacitor, and dynamically adjusts the energy distribution strategy to ensure that all key functional units can obtain sufficient energy.

[0079] It can be understood that through the above embodiments, the present invention can intelligently predict and determine the best combination of functional units according to the current stored power of the backup capacitor, the current driving state of the vehicle, and the target power supply duration, so as to maximize the utilization of the energy in the backup capacitor in an emergency, ensure the normal operation of the key safety functions of the vehicle, and improve the safety performance of the vehicle.

[0080] In an embodiment of the present application, energy is allocated from the backup capacitor to each functional unit of the vehicle according to a preset priority order, including: obtaining the importance score and urgency score of each target functional unit, as well as the weight ratios of importance and urgency; calculating the importance score and urgency score of each target functional unit based on the weight ratio of energy recovery rate to obtain the comprehensive score of each target functional unit; sorting each target functional unit of the vehicle according to the comprehensive score of energy recovery rate to obtain a power supply priority list; obtaining the basic energy requirement of each target functional unit, and the basic energy requirement of energy recovery rate is the minimum energy requirement for maintaining the startup of this functional unit; allocating a weighting coefficient to each target functional unit based on the power supply priority list of energy recovery rate, and allocating the current stored power of the backup capacitor to each target functional unit of the vehicle based on the weighting coefficient of energy recovery rate.

[0081] In a specific embodiment of the present application, in an emergency situation, in order to maximize the use of the energy in the backup capacitor and ensure the safety of the vehicle and its occupants, the system will allocate energy from the backup capacitor to each functional unit of the vehicle according to a preset priority order. First, the system will obtain the importance score and urgency score of each target functional unit, and these two scores respectively reflect the importance of the functional unit to the vehicle safety and passenger life, and the necessity of immediate activation in an emergency situation. For example, the importance and urgency scores of the door unlocking system are 0.9 and 0.8 respectively, while the scores of the emergency alarm light are 0.85 and 0.9.

[0082] Next, the system will calculate the comprehensive score of each target functional unit according to the preset weight ratios of importance and urgency (such as the importance weight α is set to 0.6, and the urgency weight 1 - α is set to 0.4). This comprehensive score is calculated by the following formula:

[0083] S i =αW i +(1-α)·E i Equation (4)

[0084] Where, W i and E i are respectively the importance score and urgency score of the i-th functional unit, and S i is the comprehensive score.

[0085] In this embodiment, based on this method, the comprehensive score of the emergency alarm light is the highest, which is 0.87, followed by that of the door unlocking system, which is 0.86, etc.

[0086] Subsequently, the vehicle's target functional units are sorted according to the comprehensive score to form a power supply priority list. For example, the emergency warning light has the highest priority, followed by the door unlocking system, and so on. The system also obtains the basic energy requirement of each functional unit, that is, the minimum energy required to maintain the startup of the functional unit. For example, the basic energy requirement of the emergency warning light is 15 units.

[0087] Next, the system assigns a weighting factor to each target functional unit that is proportional to the comprehensive score. Assuming the total energy is 100 units, the weighting factor of the emergency warning light is set to 1, and the weighting factors of other functional units are allocated proportionally according to their comprehensive scores. Based on these weighting factors and basic energy requirements, the system calculates the energy allocation for each functional unit to ensure that critical functional units can obtain sufficient energy support in an emergency.

[0088] Finally, the system implements the energy allocation and continuously monitors the energy consumption of each functional unit and the remaining power of the backup capacitor to dynamically adjust the energy allocation strategy to ensure that all critical functional units can operate normally in an emergency. This method not only improves the safety performance of the vehicle in an emergency but also demonstrates how to improve the resource utilization efficiency through intelligent management.

[0089] It can be understood that through the above embodiments, the present invention can intelligently calculate and determine the comprehensive score of each functional unit according to the importance score and urgency score of each target functional unit, as well as the weight ratio of importance and urgency, and then generate a power supply priority list. Based on this priority list, the system can reasonably allocate the energy in the backup capacitor to ensure the normal operation of the key safety functions of the vehicle in an emergency and improve the safety performance of the vehicle.

[0090] In an embodiment of the present application, after allocating energy from the backup capacitor to each functional unit of the vehicle, it further includes: obtaining user feedback. The energy recovery rate user feedback includes user satisfaction and user suggestions for power supply priority; when the user satisfaction of the energy recovery rate is lower than the preset satisfaction threshold, the user suggestion for power supply priority of the energy recovery rate is used as an input item for optimizing the training working duration prediction model.

[0091] In a specific embodiment of the present application, after allocating energy from the backup capacitor to each functional unit of the vehicle, the system further includes the step of obtaining user feedback to further optimize the energy allocation strategy. The specific implementation steps are as follows:

[0092] The system collects feedback information from users through the in-vehicle infotainment system or mobile applications. User feedback includes user satisfaction and the power supply priorities suggested by users. User satisfaction can be obtained through questionnaires, rating systems, or direct input, and the power supply priorities suggested by users can be obtained by users manually adjusting the priority order of functional units. The system presets a satisfaction threshold, such as 70%. When the user's satisfaction is lower than this preset threshold, the system will consider that the current energy allocation strategy needs to be optimized. For example, if the user is not satisfied with the current energy allocation, it may be because some key functional units do not receive enough energy, or non-key functional units occupy too much energy.

[0093] When the user satisfaction is lower than the preset threshold, the system uses the power supply priorities suggested by users as new input items to optimize the training of the working duration prediction model. The specific steps are as follows: The system records the detailed information of user feedback, including the user satisfaction score and the power supply priorities suggested by users, and saves this data in the system's database for subsequent model optimization. Preprocess the collected user feedback data, including data cleaning, feature selection, and data normalization. Data cleaning is used to remove outliers and noise, feature selection is used to select the features most relevant to the prediction target, and data normalization is used to convert the input data into a format with a mean of 0 and a standard deviation of 1 to improve the efficiency of model training. Use the power supply priorities suggested by users as new input features to retrain the working duration prediction model. During the model training process, use user feedback data to adjust the model's parameters to improve the model's prediction accuracy and user satisfaction. Commonly used model training methods include gradient descent, random forest, and neural networks, etc. Use the validation set to evaluate the performance of the optimized model. The validation set contains a part of the data that has not participated in training and is used to evaluate the performance of the model on new data. By comparing the model performance before and after optimization, ensure that the optimized model has a significant improvement in predicting the working duration. Deploy the optimized model to the actual vehicle system to replace the original model. The system will use the new model to predict the working duration of each functional unit and dynamically adjust the energy allocation strategy according to the prediction results.

[0094] The system will continuously collect user feedback, regularly evaluate user satisfaction, and continuously optimize the working duration prediction model according to the power supply priorities suggested by users. Through continuous feedback and optimization, the system can gradually improve the accuracy of energy allocation and user satisfaction.

[0095] It is understandable that, through the above embodiments, after the present invention distributes energy from the backup capacitor to each functional unit of the vehicle, by obtaining user feedback, including user satisfaction and the power supply priority of user suggestions, the energy distribution strategy is further optimized. When the user satisfaction is lower than the preset threshold, the system uses the power supply priority suggested by the user as a new input item to optimize the training of the working duration prediction model. This process, through data collection, preprocessing, model optimization, verification and application, ensures that the system can continuously improve the energy distribution strategy according to the actual needs and feedback of users, and improve the safety performance and user experience of the vehicle in emergency situations.

[0096] Finally, it should be emphasized that the vehicle redundant power supply method, by integrating advanced sensor technology, energy conversion technology, intelligent control strategies and energy distribution algorithms, realizes the efficient recovery and intelligent management of the vehicle's kinetic energy, and has the effects of improving energy utilization efficiency and enhancing vehicle safety. Specifically:

[0097] In terms of improving energy utilization efficiency, the kinetic energy generated when the vehicle brakes is efficiently converted into electrical energy by an energy converter and stored in a supercapacitor. This not only reduces the waste of energy during braking, but also provides an additional power source for the vehicle, helping to reduce fuel consumption and emissions, and conforming to the development trend of green environmental protection. By using sensors to continuously monitor the vehicle's driving parameters (such as vehicle speed, acceleration and braking force), and dynamically adjusting the working state of the energy converter according to these parameters to optimize the energy recovery efficiency, this energy management method makes the collection and storage of energy more efficient and reasonable, adapting to the energy demand changes under different driving conditions.

[0098] In terms of enhancing vehicle safety, the system continuously monitors the occurrence status of collision events through a collision monitoring module. Once a collision event is detected, the system immediately activates an emergency program and preferentially distributes the energy in the supercapacitor to key safety functions, such as the door unlocking system and emergency warning lights, ensuring that these systems can still operate normally in the event of main power failure, greatly improving the safety performance of the vehicle in emergency situations. The system intelligently distributes the energy in the supercapacitor according to the preset priority order and user feedback. By calculating the comprehensive scores and basic energy requirements of each functional unit, the system can reasonably distribute energy to ensure that key functional units obtain sufficient energy support in an emergency.

[0099] The embodiments of the present application also provide an electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the electronic device to implement the vehicle redundant power supply method provided in each of the above embodiments.

[0100] Figure 4The figure shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. It should be noted that, Figure 4 The computer system 400 of the shown electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0101] As Figure 4 shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage section 408 into the random access memory (RAM) 403, such as executing the method described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0102] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including, such as, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that the computer program read from it can be installed into the storage section 408 as needed.

[0103] Particularly, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0104] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0106] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation on the units themselves in some cases.

[0107] On the other hand, this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer is made to execute the vehicle redundant power supply method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist alone without being assembled into the electronic device.

[0108] On the other hand, this application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the vehicle redundant power supply method provided in each of the above embodiments.

[0109] The above embodiments only exemplarily illustrate the principles and effects of this application, rather than limiting this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in this application should still be covered by the claims of this application.

Claims

1. A vehicle redundant power supply device, characterized in that: The device comprises: A sensor for monitoring vehicle driving parameters, including vehicle speed, acceleration, and braking force; a control unit connected to the sensor, configured to determine a vehicle driving state based on vehicle driving parameters, and to determine a current energy recovery rate corresponding to the current vehicle driving state according to a preset energy recovery rate mapping relationship, wherein the energy recovery rate mapping relationship is configured to represent a relationship between the vehicle driving state and the energy recovery rate; an energy converter, configured to convert kinetic energy generated by braking the vehicle into electrical energy according to the current energy recovery rate; a backup capacitor connected to the energy converter and the control unit, and configured to store electrical energy obtained based on kinetic energy conversion into the backup capacitor; A collision detection module is used to monitor the occurrence of a collision event and transmit the collision signal generated when the collision event occurs to the energy distribution module; an energy distribution module, connected to the backup capacitor and the collision detection module, for distributing the energy in the backup capacitor to various functional units of the vehicle according to a preset priority order when a collision signal is detected; The energy distribution module includes a neural network for predicting the operating time of various functional unit combinations of the vehicle powered by the backup capacitor under different backup capacitor storage capacities and different vehicle driving conditions.

2. The vehicle redundant power supply device according to claim 1, characterized in that: The neural network comprises: A data collection module is used to collect working time data of different functional units under different residual energy conditions; Data preprocessing module, used for data cleaning, feature selection, data standardization and data segmentation; Multilayer perceptron model, including input layer, hidden layer and output layer; The training module is used to train the initial model using a preset loss function and optimizer to obtain a working time prediction model; An evaluation module is used to evaluate the prediction accuracy of the working time prediction model using a test set. If the prediction accuracy is lower than a preset accuracy threshold, the working time prediction model is optimized until its prediction accuracy is greater than or equal to the preset accuracy threshold.

3. The vehicle redundant power supply device according to claim 1, characterized in that: The energy distribution module further comprises: A priority sorting module is used to sort the power supply priorities of the functional units according to the prediction results and the preset functional priorities; An energy allocation calculation module is used to allocate energy to functional units with higher power supply priorities; The real-time adjustment module is used to dynamically adjust energy distribution according to the real-time monitored energy consumption and remaining energy.

4. A vehicle redundant power supply method, characterized in that: The method comprises: Monitoring vehicle driving parameters, wherein the vehicle driving parameters include at least vehicle speed, acceleration, and braking force; comparing the vehicle driving parameter with a preset vehicle driving parameter threshold to determine a vehicle driving state, and determining a current energy recovery rate that matches the current vehicle driving state based on a preset energy recovery rate mapping relationship, wherein the energy recovery rate mapping relationship is used to characterize a relationship between the vehicle driving state and the energy recovery rate; converting kinetic energy generated by vehicle braking into electrical energy according to the current energy recovery rate, and storing the electrical energy in a backup capacitor; monitoring the occurrence of a collision event, generating a collision signal if a collision event is detected, and transmitting the collision signal to an energy distribution module; wherein the energy distribution module includes a neural network for predicting the operating time of various vehicle functional units powered by the backup capacitor under different backup capacitor storage capacities and different vehicle driving conditions; When the energy distribution module receives a collision signal, it distributes energy from the backup capacitor to various functional units of the vehicle according to a preset priority order.

5. The vehicle redundant power supply method according to claim 4, characterized in that: Before distributing energy from the backup capacitor to the various functional units of the vehicle according to a preset priority order, it also includes: Obtain the current storage capacity of the backup capacitor, the current driving status of the vehicle, and the target power supply duration; Based on the trained working time prediction model, the working time of each functional unit combination powered by the backup capacitor is predicted under the current stored power and the current driving state, so as to obtain the available working time of each functional unit combination; Any functional unit combination whose available working time is longer than the target power supply time is determined as a target functional unit combination, and each functional unit in the target functional unit combination is determined as a target functional unit.

6. The vehicle redundant power supply method according to claim 5, characterized in that: Energy is distributed from the backup capacitor to various functional units of the vehicle according to a preset priority order, including: Obtain the importance score and urgency score of each target functional unit, as well as the weight ratio of importance and urgency; Based on the weight ratio, the importance score and the urgency score of each target functional unit, a comprehensive score of each target functional unit is obtained; Sorting each target functional unit of the vehicle according to the comprehensive score to obtain a power supply priority list; Obtaining a basic energy requirement of each target functional unit, where the basic energy requirement is the minimum energy requirement for maintaining the activation of the functional unit; A weighting coefficient is assigned to each target table functional unit based on the power supply priority list, and the current stored power of the backup capacitor is distributed to each target functional unit of the vehicle based on the weighting coefficient.

7. The vehicle redundant power supply method according to claim 5, characterized in that: After distributing energy from the backup capacitor to various functional units in the vehicle, it also includes: Obtaining user feedback, including user satisfaction and user-suggested power supply priority; When the user satisfaction is lower than a preset satisfaction threshold, the user-suggested power supply priority is used as an input item to optimize the training working time prediction model.

8. An electronic device, characterized in that: It comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to implement the vehicle redundant power supply method as described in any one of claims 4 to 7.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program is used to enable a computer to execute the vehicle redundant power supply method according to any one of claims 4 to 7.

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