Detection control method and system for vehicle emergency starting power supply

Abstract

The invention relates to a detection control method and system for a vehicle emergency starting power supply, and the method comprises the steps: obtaining multi-battery type interface data of the vehicle emergency starting power supply, and carrying out the electrical characteristic recognition processing, and obtaining the characteristic information of a battery interface; acquiring real-time operation data of the vehicle emergency starting power supply, and performing association processing on the real-time operation data and the battery interface feature information to obtain a battery operation state evaluation result; acquiring battery charging and discharging energy data of the vehicle emergency starting power supply, and performing battery state prediction according to the battery charging and discharging energy data to obtain a battery energy balance trend; comprehensively analyzing the battery operation state evaluation result and the battery energy balance trend to obtain an initial power management control scheme; and performing iterative optimization on the initial power management control scheme according to the fault early warning information to obtain a corresponding emergency start power protection strategy. According to the invention, the emergency starting power supply can be ensured to operate efficiently under different use conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle emergency starting power supplies, and in particular to a detection and control method and system for a vehicle emergency starting power supply. Background Art

[0002] As a key vehicle rescue equipment, the vehicle emergency starting power supply plays an important role when the vehicle battery is accidentally depleted or fails. With the continuous development of automobile technology and the acceleration of the electrification trend, the vehicle electrical system is becoming increasingly complex, and higher requirements are placed on the performance and reliability of the emergency starting power supply. How to effectively manage and optimize the operating status of the vehicle emergency starting power supply to ensure that it can reliably provide the required power in an emergency has become one of the research focuses. Existing vehicle emergency starting power supply management methods often only focus on basic parameters such as the output voltage and capacity of the power supply, while ignoring the internal state of the battery, the characteristic differences of different types of batteries, and the impact of long-term use on battery performance. This simplified management method may lead to inaccurate power supply performance evaluation and failure to detect potential failure risks in a timely manner, thereby affecting the reliability and service life of the emergency starting power supply. In addition, the lack of dynamic monitoring and prediction of the battery charging and discharging process also makes it difficult to formulate an effective protection strategy, increasing the risk of power failure or malfunction. Summary of the invention

[0003] The main purpose of the present invention is to provide a detection and control method and system for a vehicle emergency starting power supply, which can ensure that the emergency starting power supply can operate efficiently under different usage conditions and reduce unnecessary energy loss.

[0004] To achieve the above object, the present invention provides a detection and control method for a vehicle emergency starting power supply, comprising: Acquire multiple battery type interface data of the vehicle emergency starting power supply, and perform electrical characteristic recognition processing to obtain battery interface characteristic information; Acquire real-time operation data of the vehicle emergency starting power supply, associate the real-time operation data with the battery interface characteristic information, and obtain a battery operation status evaluation result; Acquiring battery charge and discharge energy data of the vehicle emergency starting power supply, performing battery status prediction based on the battery charge and discharge energy data, and obtaining a battery energy balance trend; Comprehensively analyzing the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control plan; Performing risk identification on the battery charging and discharging energy data to obtain potential fault information, and performing risk assessment on the potential fault information based on the energy balance trend to obtain fault warning information; The initial power management control scheme is iteratively optimized according to the fault warning information to obtain a corresponding emergency starting power protection strategy.

[0005] Furthermore, the acquisition of multiple battery type interface data of the vehicle emergency starting power supply and the electrical characteristic identification processing to obtain battery interface characteristic information includes: Performing high-frequency anti-interference sampling on the vehicle emergency starting power supply to obtain an original interface parameter data set including voltage fluctuation rate, current fluctuation rate, charge and discharge response time and impedance; Constructing a nonlinear mapping matrix for the original interface parameter data set to obtain an interface parameter feature mapping matrix; Performing vector weight calculation on the interface parameter feature mapping matrix to obtain a battery interface information entropy weight vector; Performing feature decoupling on the battery interface information entropy weight vector to obtain interface decoupling information; Constructing a probability evolution map of interface electrical characteristics based on the interface decoupling information to obtain an interface state transfer matrix; An information game association is constructed based on the interface state transfer matrix and the interface decoupling information to obtain a battery interface feature association network; Topological structure analysis and node identification are performed on the battery interface feature association network to obtain the battery interface feature information.

[0006] Furthermore, the acquiring of real-time operating data of the vehicle emergency starting power supply, associating the real-time operating data with the battery interface characteristic information to obtain a battery operating status evaluation result, includes: Extracting multi-dimensional voltage parameters from the real-time operation data, and obtaining a voltage feature vector through discrete wavelet transform and statistical feature analysis; Performing current spectrum mapping decomposition on the real-time operation data to obtain a current characteristic mapping; Performing feature association according to the voltage feature vector and the current feature map to obtain a battery operating state feature association matrix; Performing coding matrix conversion on the battery interface characteristic information to obtain a battery interface characteristic coding matrix; An association evaluation is performed on the battery interface feature coding matrix and the battery operation status feature association matrix to obtain the battery operation status evaluation result.

[0007] Further, the acquiring of battery charge and discharge energy data of the vehicle emergency starting power supply, and performing battery state prediction based on the battery charge and discharge energy data to obtain a battery energy balance trend include: Collect the battery voltage, battery current and temperature data of the vehicle emergency starting power supply, and integrate them synchronously to obtain the battery charging and discharging energy data Performing multi-dimensional feature construction on the battery charge and discharge energy data to obtain a corresponding battery energy feature set; Perform multi-scale state analysis based on the battery energy feature set to obtain a battery energy change prediction sequence; Performing trend analysis on the battery energy change prediction sequence to obtain a battery energy decay prediction result; The energy distribution inside the battery pack is dynamically calibrated and evaluated according to the battery energy decay prediction result to obtain the battery energy balance trend.

[0008] Furthermore, the comprehensive analysis of the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control scheme includes: Calculate the health of the battery to obtain a battery health index. Calculate the battery energy efficiency according to the battery energy balance trend to obtain an energy utilization efficiency index; Comprehensively evaluating the battery health index and the energy utilization efficiency index to obtain a comprehensive evaluation value of battery performance; Perform battery charge and discharge threshold and power limit parameter setting analysis based on the comprehensive battery performance evaluation value to obtain a basic power management strategy; Dynamically adjusting the power management basic strategy to obtain adaptive control parameters; Based on the adaptive control parameters, corresponding battery charge and discharge control instructions and power output adjustment instructions are generated and integrated to obtain the initial power management control solution.

[0009] Furthermore, the battery energy efficiency is calculated according to the battery energy balance trend to obtain an energy utilization efficiency index, including: Performing battery charge and discharge cycle identification on the battery energy balance trend to obtain the number of charge and discharge cycles; Counting the number of charge and discharge cycles to obtain a battery usage frequency index; Calculate the attenuation rate according to the battery usage frequency index to obtain the capacity attenuation coefficient; Integrating the number of charge and discharge cycles to obtain a cumulative input and output energy value of the battery; Calculating the conversion efficiency according to the accumulated input and output energy values ​​of the battery and the capacity attenuation coefficient to obtain an energy conversion efficiency coefficient; Performing performance rating according to the energy conversion efficiency coefficient to obtain a battery energy utilization level; The battery energy utilization level is converted into a numerical index to obtain an energy utilization efficiency index.

[0010] Further, the risk identification is performed on the battery charging and discharging energy data to obtain potential fault information, and the risk assessment is performed on the potential fault information based on the energy balance trend to obtain fault warning information, including: Performing energy fluctuation analysis on the battery charging and discharging energy data to obtain battery fluctuation data; Classify the battery charging and discharging mode according to the battery fluctuation data to obtain the battery usage mode type; Performing abnormal pattern recognition on the battery usage pattern type to obtain abnormal usage pattern information; Performing deviation analysis on the abnormal usage mode information according to preset battery performance standard parameters to obtain battery performance degradation information; Performing trend evaluation on the energy balance trend of the battery performance degradation information to obtain a fault development trend; Perform fault warning according to the fault development trend to obtain the fault warning time point; A comprehensive analysis is performed on the fault development trend and the fault warning time point to obtain fault warning information.

[0011] Furthermore, the initial power management control scheme is iteratively optimized according to the fault warning information to obtain a corresponding emergency startup power protection strategy, including: Classifying the fault warning information into risk levels to obtain multiple levels of fault risk levels; hierarchically optimizing the initial power management control scheme according to the multi-level fault risk levels to obtain a plurality of alternative protection strategies; Performing safety evaluation on the plurality of candidate protection strategies to obtain a safety factor for each of the candidate protection strategies; Sorting the plurality of candidate protection strategies according to the safety factor to obtain a priority sorting result; Performing strategy screening on the plurality of candidate protection strategies according to the priority sorting result to obtain a corresponding initial power supply protection strategy; Performing simulation verification on the initial power supply protection strategy to obtain simulation results; The initial power supply protection strategy is parameter-adjusted according to the simulation result, and the emergency power supply protection strategy is started.

[0012] The present invention also provides a detection control device for a vehicle emergency starting power supply, which is applied to any of the above detection control methods for a vehicle emergency starting power supply, comprising: A collection module, the collection module is used to obtain multiple battery type interface data of the vehicle emergency starting power supply, and perform electrical characteristic recognition processing to obtain battery interface characteristic information; An analysis module, the analysis module is used to obtain real-time operation data of the vehicle emergency starting power supply, associate the real-time operation data with the battery interface characteristic information, and obtain a battery operation status evaluation result; A correlation module, the correlation module is used to obtain battery charge and discharge energy data of the vehicle emergency starting power supply, perform battery state prediction based on the battery charge and discharge energy data, and obtain a battery energy balance trend; A processing module, the processing module is used to comprehensively analyze the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control plan; A control module, the control module is used to perform risk identification on the battery charging and discharging energy data to obtain potential fault information, perform risk assessment on the potential fault information based on the energy balance trend, and obtain fault warning information; An execution module is used to iteratively optimize the initial power management control scheme according to the fault warning information to obtain a corresponding emergency starting power protection strategy.

[0013] The present invention provides a detection and control method and system for a vehicle emergency starting power supply, which has the following beneficial effects: By acquiring the interface data of multiple battery types and performing electrical characteristic identification processing, the performance characteristics of different types of batteries can be evaluated more accurately, thereby improving the accuracy of power management and providing a more reliable basis for system operation. The real-time operation data is associated with the battery interface characteristic information to achieve a refined evaluation of the battery operation status, which helps to timely discover potential problems and avoid power performance degradation or failure. Battery status prediction based on battery charge and discharge energy data can ensure that the emergency starting power supply can operate efficiently under different usage conditions, reduce unnecessary energy loss, and improve the overall operation efficiency of the system. By comprehensively analyzing the battery operation status evaluation results and energy balance trends, a more reasonable power management control plan is formulated, and iterative optimization is performed through risk identification and evaluation, thereby effectively improving the reliability and service life of the emergency starting power supply. Risk identification and evaluation of battery charge and discharge energy data can flexibly adjust the protection strategy according to the characteristics of different battery types and usage conditions, making the system more adaptable to diverse emergency starting demand scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention provides a flow chart of a detection and control method for a vehicle emergency starting power supply; Figure 2The present invention provides a structural diagram of a detection and control system for a vehicle emergency starting power supply.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a detection and control method for a vehicle emergency starting power supply, comprising: Step S1: acquiring multiple battery type interface data of the vehicle emergency starting power supply, and performing electrical characteristic recognition processing to obtain battery interface characteristic information; Step S2: acquiring real-time operation data of the vehicle emergency starting power supply, correlating the real-time operation data with the battery interface characteristic information, and obtaining a battery operation status evaluation result; Step S3: acquiring battery charge and discharge energy data of the vehicle emergency starting power supply, performing battery status prediction based on the battery charge and discharge energy data, and obtaining a battery energy balance trend; Step S4: Comprehensively analyzing the battery operation status evaluation results and the battery energy balance trend to obtain an initial power management control plan; Step S5: performing risk identification on the battery charging and discharging energy data to obtain potential fault information, and performing risk assessment on the potential fault information based on the energy balance trend to obtain fault warning information; Step S6: Iteratively optimize the initial power management control scheme according to the fault warning information to obtain a corresponding emergency starting power protection strategy.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Scan and identify different types of battery interfaces through dedicated interface equipment, collect basic electrical parameters including voltage, current, impedance, etc., and establish a complete battery interface database. Based on data acquisition, use signal processing algorithms to reduce noise and filter the interface data, and compare it with standard battery characteristics to identify the unique electrical characteristics of the battery interface. At the same time, establish a machine learning model to achieve automatic identification and classification of battery interfaces. In the feature information extraction stage, focus on analyzing the voltage response characteristics of the battery interface, extract the impedance spectrum of the battery interface, and finally construct a complete battery interface feature vector to provide basic data support for subsequent status assessment.

[0020] Step S2: Continuously collect key operating parameters such as battery temperature, voltage, and current, and record the dynamic changes of the battery during the charging and discharging process. Map the collected real-time operating data with the battery interface characteristic information, build data associations, and deeply analyze the parameter change patterns. By designing a multi-dimensional evaluation indicator system, an intelligent evaluation of the battery operating status is performed to obtain the status evaluation results.

[0021] Step S3: Detect the energy flow of the battery during charging and discharging, and collect energy change data under different working conditions. Based on the collected data, use the time series analysis method to build a battery aging and performance degradation prediction algorithm, establish an energy balance dynamic model, evaluate the energy conversion efficiency of the battery charging and discharging process, and predict the long-term energy balance trend of the battery.

[0022] Step S4: Deeply integrate the previously obtained battery operation status evaluation results and energy balance trends. Through multi-dimensional cross-analysis, comprehensively evaluate the current performance and future development trend of the battery. The comprehensive analysis process focuses on key indicators such as battery health, energy conversion efficiency, and performance degradation trends. Based on these data, the system will build a preliminary power management control solution to lay the foundation for subsequent power optimization and fault prevention. The control solution will include battery charging and discharging strategies, temperature control recommendations, performance compensation measures, etc. to ensure the stability and reliability of the emergency starting power supply.

[0023] Step S5: Through careful detection of energy data, identify key signals that may cause battery failure. The risk identification process will comprehensively scan various abnormal indicators during battery operation, including voltage fluctuations, current mutations, temperature anomalies, etc. Based on the energy balance trend obtained in the early stage, these potential fault information are sequentially subjected to quantitative risk assessment, qualitative risk assessment and risk level classification to form fault warning information including detailed risk levels and warning information.

[0024] Step S6: By repeatedly iterating and adjusting the initial power management control scheme based on the fault warning information, the protection strategy of the emergency starting power supply is continuously improved. The optimization process will fully consider the real-time status, potential risks and performance characteristics of the battery to formulate more accurate and reliable protection measures. The formed protection strategy will cover multiple dimensions such as battery charge and discharge control, abnormal situation handling, performance compensation, etc., to ensure that the emergency starting power supply can maintain efficient and safe operation under various complex working conditions.

[0025] The present invention provides a detection and control method for a vehicle emergency starting power supply. By acquiring multiple battery type interface data and performing electrical characteristic identification processing, the performance characteristics of different types of batteries can be more accurately evaluated, thereby improving the accuracy of power management and providing a more reliable basis for system operation. Real-time operation data is associated with battery interface characteristic information to achieve a refined evaluation of the battery operation status, which helps to timely discover potential problems and avoid power performance degradation or failure. Battery status prediction based on battery charge and discharge energy data can ensure that the emergency starting power supply can operate efficiently under different usage conditions, reduce unnecessary energy loss, and improve the overall operation efficiency of the system. By comprehensively analyzing the battery operation status evaluation results and energy balance trends, a more reasonable power management control plan is formulated, and iterative optimization is performed through risk identification and evaluation, thereby effectively improving the reliability and service life of the emergency starting power supply. Risk identification and evaluation of battery charge and discharge energy data can flexibly adjust the protection strategy according to the characteristics of different battery types and usage conditions, so that the system is more adaptable to diverse emergency starting demand scenarios.

[0026] In one embodiment, multiple battery type interface data of a vehicle emergency starting power supply is obtained, and electrical characteristic recognition processing is performed to obtain battery interface characteristic information, including: In the high-frequency anti-interference sampling stage, a dedicated sampling device is used to collect electrical parameters of the vehicle emergency starting power supply with millisecond accuracy to obtain the original interface parameters such as voltage fluctuation rate, current fluctuation rate, charge and discharge response time and system impedance. The sampling frequency is set to 1-10kHz to ensure the accuracy and real-time performance of data capture. The original data is filtered by a digital filtering algorithm to remove electromagnetic interference and high-frequency noise and retain effective electrical characteristic information.

[0027] The construction of nonlinear mapping matrix is ​​based on a complex mathematical transformation model. Through principal component analysis and kernel function mapping, the original parameter data is converted into a high-dimensional feature space. The mapping process introduces a weight calculation method based on entropy weight theory to normalize different parameters, eliminate dimensional differences, and extract the essential characteristics of the data.

[0028] The information entropy weight vector calculation uses an entropy model to assign weights to each feature dimension in the mapping matrix. The weight calculation takes into account the discreteness and information richness of the parameters, and evaluates the importance of each feature dimension through the maximum entropy principle. The weight vector reflects the information content and distinguishing ability of the battery interface parameters.

[0029] The feature decoupling process uses the independent component analysis algorithm to decompose the complex interface parameter information into mutually orthogonal independent components. The decoupling algorithm is based on the statistical independence criterion, eliminates the redundancy and correlation between parameters, and extracts the most representative feature vectors. The decoupling results reveal the potential physical characteristics and change patterns of the battery interface.

[0030] The interface state transfer matrix is ​​constructed based on Markov chain theory to describe the probability transition of the battery interface between different working states. The state transfer matrix quantifies the transition probability between different states by statistically analyzing the change pattern of battery interface parameters. The matrix elements reflect the dynamic evolution characteristics of battery interface performance.

[0031] The information game association network construction introduces game theory methods, and establishes a multi-dimensional feature association model based on the interface state transfer matrix and decoupling information. The network nodes represent key electrical characteristics, and the edge weights reflect the interaction strength between nodes. Through topological structure analysis, the key feature nodes and potential risk points of the battery interface are identified.

[0032] The battery interface feature information is obtained through precise analysis of the feature association network.

[0033] This embodiment significantly improves the accuracy and reliability of battery interface parameters through high-frequency anti-interference sampling and nonlinear mapping technology, overcoming the technical limitations of signal noise and data distortion in traditional detection methods. Based on information entropy weights and feature decoupling algorithms, deep mining and effective dimensionality reduction of complex battery interface parameters are achieved, which can accurately identify key performance characteristics of battery systems and improve the level of intelligent diagnosis. Markov chain and game theory methods are introduced to construct state transfer matrices and association networks, dynamically capture the evolution of battery interfaces, and establish a multi-dimensional, high-precision electrical characteristics evaluation model, which enhances the ability to predict potential risks of battery systems.

[0034] In one embodiment, real-time operation data of the vehicle emergency starting power supply is obtained, and the real-time operation data is associated with the battery interface characteristic information to obtain a battery operation status evaluation result, including: Through multi-dimensional data analysis and feature association technology, the precise operating status of the vehicle emergency starting power supply is evaluated. The data processing process starts with the multi-angle extraction of real-time operating data. First, the discrete wavelet transform is used to extract multi-dimensional features of voltage parameters. In the voltage feature extraction stage, an appropriate wavelet basis function (such as Haar wavelet or Daubesi wavelet) is selected to decompose the collected voltage data, extract statistical features including energy, variance, kurtosis, etc., and construct a multi-dimensional voltage feature vector.

[0035] The current feature mapping link uses spectrum decomposition technology to convert real-time operating data into frequency domain representation. Through fast Fourier transform (FFT) or short-time Fourier transform (STFT), the current signal is mapped to the frequency space and its spectrum distribution characteristics are analyzed. In the spectrum mapping process, the amplitude, frequency component and phase information of the current signal are focused on to construct a current feature map that can reflect the dynamic characteristics of the battery.

[0036] In the feature association stage, the voltage feature vector and current feature map are integrated into a feature association matrix. The construction of the association matrix follows the principle of information complementarity and correlation analysis, and the voltage and current features are comprehensively evaluated through weight allocation and feature fusion algorithm. Each element in the matrix represents the strength of association between different feature dimensions, providing a comprehensive data basis for subsequent battery status evaluation.

[0037] The encoding of battery interface feature information is processed through standardized and digital methods. First, the interface features (such as connection type, material parameters, and geometric structure) are numerically quantified, and then these features are converted into a standardized encoding matrix using matrix encoding technology. The encoding process ensures that interface features of different types and sources can be uniformly characterized and compared.

[0038] The battery operation status evaluation is completed by the correlation evaluation of the battery interface feature coding matrix and the operation status feature correlation matrix to obtain the battery operation status evaluation result. The evaluation method adopts multi-criteria decision-making to calculate the similarity and matching degree of the two matrices. The evaluation results will quantify the battery's health status, performance indicators and potential risks, and provide accurate diagnostic basis for the management and maintenance of vehicle emergency starting power supplies.

[0039] This embodiment uses multi-dimensional data analysis technology to achieve accurate assessment of the operating status of the vehicle's emergency starting power supply, breaking through the limitations of traditional single-dimensional monitoring. The multi-angle feature extraction method significantly improves the accuracy and comprehensiveness of the power system status diagnosis, and can capture subtle changes in battery operation more deeply. The discrete wavelet transform and spectrum mapping technology are innovatively introduced into battery status assessment, and a set of intelligent feature correlation analysis models are constructed, which effectively overcomes the problem of insufficient data correlation in traditional detection methods. By establishing a battery interface feature coding matrix and an operating status feature correlation matrix, accurate quantification and risk prediction of battery performance are achieved.

[0040] In one embodiment, the battery charge and discharge energy data of the vehicle emergency starting power supply is obtained, and the battery state is predicted based on the battery charge and discharge energy data to obtain the battery energy balance trend, including: During the battery charge and discharge energy data acquisition phase, high-precision sensors are used to collect battery parameters of the vehicle emergency starting power supply. Voltage sensors, current sensors, and temperature sensors are key devices for data acquisition, and their sampling frequency and accuracy directly affect the accuracy of subsequent analysis. The data synchronization and integration process follows the signal processing standard, and through timestamp alignment and data standardization, occasional errors in the acquisition process are eliminated to build a complete battery charge and discharge energy data set.

[0041] The battery energy feature construction phase uses a multi-dimensional feature extraction algorithm to parse key performance indicators from the charge and discharge energy data. Feature construction follows the principle of maximizing information entropy and selects feature dimensions with the highest amount of information. Feature selection considers multiple indicators such as charge and discharge rate, energy conversion efficiency, temperature gradient, and internal resistance change. The algorithm introduces dimensionality reduction technology to map high-dimensional data to low-dimensional feature space, significantly improving the accuracy and discrimination of feature expression.

[0042] Multi-scale state analysis is based on the constructed battery energy feature set and uses a time series prediction model. The prediction model comprehensively considers short-term, medium-term and long-term scales, and captures the nonlinear characteristics of battery energy changes through deep learning neural networks. The effectiveness of the prediction algorithm depends on the representativeness of the training data and the precise tuning of the model parameters. The algorithm considers multiple factors such as battery aging, ambient temperature, and charge and discharge cycles to generate a battery energy change prediction sequence.

[0043] In the trend analysis phase, statistical and probability analysis methods are used to quantitatively evaluate the battery energy change prediction sequence. The analysis process is based on a strict mathematical model, and the quantitative prediction results of battery energy decay are obtained through techniques such as fitting decay curves and calculating confidence intervals. The decay model comprehensively considers complex factors such as internal chemical reactions of the battery and material degradation to ensure the scientificity and reliability of the prediction results.

[0044] According to the battery energy decay prediction results, the internal energy distribution of the battery pack is dynamically calibrated to obtain the battery energy balance trend. The evaluation process includes single cell consistency analysis, energy balance detection and potential faulty single cell identification. By accurately locating the abnormal points of energy distribution inside the battery pack, the battery energy balance trend can be accurately predicted and actively adjusted.

[0045] This embodiment achieves accurate evaluation of the performance of the vehicle emergency starting power battery through multi-dimensional feature construction and multi-scale state analysis. Innovatively integrate multi-source sensor data, establish a dynamic prediction model for battery energy balance, and significantly improve the accuracy and reliability of battery status monitoring. This technology breaks through the limitations of traditional battery management systems, can accurately capture battery energy decay trends, and effectively prevent battery failure risks. By dynamically calibrating the internal energy distribution of the battery pack, the battery life is extended and the maintenance cost of the vehicle emergency starting system is reduced.

[0046] In one embodiment, a comprehensive analysis is performed on the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control scheme, including: The battery operation status evaluation results are read to obtain the battery voltage evaluation results, current evaluation results, temperature evaluation results and other operation evaluation result parameters, and the battery health index is calculated based on the evaluation result parameters. The health index is obtained by analyzing the voltage evaluation results, current evaluation results, and temperature evaluation results to obtain the battery's charge and discharge cycle number, charging efficiency, discharge depth and other parameters, and the battery health index is calculated by weighted average based on the charge and discharge cycle number, charging efficiency, and discharge depth. The health index reflects the overall health of the battery, and the higher the value, the better the battery condition.

[0047] After obtaining the battery health index, the battery energy efficiency is calculated. The battery energy utilization efficiency index is calculated based on the battery energy balance trend, that is, the difference between the energy input and output of the battery under different working conditions. The energy utilization efficiency index is obtained by statistically analyzing the energy conversion efficiency of the battery within a certain period of time. The higher the value, the higher the energy utilization efficiency of the battery.

[0048] The battery health index and energy utilization efficiency index are comprehensively evaluated to obtain the comprehensive evaluation value of battery performance. The comprehensive evaluation value of battery performance is calculated by weighted average of the health index and energy utilization efficiency index, and the weight is adjusted according to the specific use environment and requirements of the battery. This comprehensive evaluation value reflects the overall performance of the battery.

[0049] According to the comprehensive evaluation value of battery performance, the battery's charge and discharge thresholds and power limit parameters are set. The specific setting method is: when the comprehensive evaluation value of battery performance is high, the charge and discharge thresholds and power limit parameters are set more loosely. For example, the upper limit voltage of charging can be increased from the standard 4.2V to 4.25V, and the lower limit voltage of discharging can be reduced from the standard 3.0V to 2.8V, increasing the available capacity range of the battery. In terms of power limit, the maximum charging power is increased by 10-15%, and the maximum discharging power is increased by 15-20%. These settings allow the battery to operate in a wider operating range and give full play to its performance.

[0050] On the contrary, when the comprehensive evaluation value of battery performance is low, the charge and discharge thresholds and power limit parameters are set more strictly. The upper limit voltage of charging may be reduced to 4.1V or lower, and the lower limit voltage of discharging may be increased to 3.2V or higher to reduce the deep charge and discharge of the battery and reduce the stress of the battery. In terms of power limit, the maximum charge power may be reduced by 20-30%, and the maximum discharge power may be reduced by 25-35%. These more conservative settings are designed to reduce the load on the battery and extend its service life.

[0051] The charge and discharge current limits will also be adjusted accordingly. When the evaluation value is higher, the maximum allowed charge and discharge current will be increased; when the evaluation value is lower, the maximum charge and discharge current will be limited to a lower level. These adjustments ensure that the battery can be properly protected and utilized in different states.

[0052] Based on the basic power management strategy, it is dynamically adjusted. The dynamic adjustment method is to generate adaptive control parameters based on the real-time monitored battery status parameters, combined with historical data and prediction models. The adaptive control parameters can reflect the battery status changes and the impact of the external environment in real time, and manage the battery charging and discharging process in a fine manner.

[0053] After obtaining the adaptive control parameters, the corresponding battery charge and discharge control instructions and power output adjustment instructions are generated. The charge and discharge control instructions include parameters such as charging current, charging voltage, and discharge current, and the power output adjustment instructions include parameters such as output power and output voltage. By integrating these instructions, an overall initial power management control solution is formed.

[0054] This embodiment achieves a comprehensive evaluation of battery performance by conducting a comprehensive analysis of the battery operating status and energy balance trend, providing a reliable basis for formulating power management strategies. By dynamically adjusting the charge and discharge thresholds and power limit parameters, the battery management is refined and intelligent, which can not only give full play to the battery performance, but also effectively protect the battery and extend its service life. The introduction of adaptive control parameters enables the system to respond to battery status and environmental changes in real time, improving the flexibility and adaptability of power management. Overall, this method significantly improves the reliability, efficiency and life of the vehicle emergency starting power supply, while optimizing energy utilization, reducing maintenance costs, and providing more stable and lasting power support for vehicle emergency starting.

[0055] In one embodiment, the battery energy efficiency is calculated according to the battery energy balance trend to obtain an energy utilization efficiency index, including: The battery energy balance trend is identified by charge and discharge cycles. During the identification process, the changes in battery voltage and current are monitored. When the voltage changes from low to high and the current is positive, the charging process is determined to have started; when the voltage changes from high to low and the current is negative, the discharging process is determined to have started. A complete charging and discharging process is recorded as a cycle. These cycles are continuously monitored and cumulatively recorded to obtain the number of charge and discharge cycles.

[0056] After obtaining the number of charge and discharge cycles, statistical analysis is performed on them. The number of cycles in a certain period of time (such as a week or a month) is divided by the time to obtain the average usage frequency, which is used as the battery usage frequency index. This index reflects the intensity of battery usage.

[0057] The battery capacity decay rate is calculated based on the battery usage frequency index. Generally speaking, the higher the usage frequency, the faster the capacity decays. The current capacity decay coefficient is calculated based on the preset decay model (such as a linear model or an exponential model). This coefficient represents the ratio of the current battery capacity to the initial capacity.

[0058] The energy of each charge and discharge cycle is integrated and calculated. The input energy is obtained by integrating the charging process, and the output energy is obtained by integrating the discharging process. The input and output energies of all cycles are accumulated to obtain the cumulative input and output energy value of the battery.

[0059] The energy conversion efficiency is calculated based on the accumulated energy value and the capacity attenuation coefficient. The specific calculation method is: output energy divided by input energy, and then multiplied by the inverse of the capacity attenuation coefficient to obtain the energy conversion efficiency coefficient. This coefficient reflects the actual energy utilization efficiency after considering capacity attenuation.

[0060] Based on the calculated energy conversion efficiency coefficient, the battery performance is rated. The rating standard can be pre-set according to the specific application scenario, such as 90% or more is excellent, 80-90% is good, 70-80% is fair, and below 70% is poor. According to this standard, the efficiency coefficient is converted into the corresponding battery energy utilization level.

[0061] Convert the battery energy utilization level into a numerical index. For example, excellent, good, fair, and poor can be mapped to score intervals of 90-100, 80-89, 70-79, and 60-69, respectively. According to the specific energy conversion efficiency coefficient, interpolation calculation is performed within the corresponding interval to obtain the final energy utilization efficiency index.

[0062] This embodiment can accurately grasp the intensity and frequency of battery usage by identifying and statistically analyzing the battery energy balance trend through charge and discharge cycles, and provide reliable basic data for evaluating battery performance. The capacity attenuation coefficient is calculated based on the usage frequency index, taking into account the impact of the actual use of the battery on its performance, so that the evaluation results are closer to reality. Through the comprehensive calculation of the cumulative input and output energy values ​​and the capacity attenuation coefficient, the energy conversion efficiency coefficient is obtained, which fully reflects the actual energy utilization efficiency of the battery. The efficiency coefficient is converted into an energy utilization level and a numerical index, making the evaluation results more intuitive and easy to understand. The entire evaluation process takes into account multiple key factors, realizes a comprehensive and accurate evaluation of the battery energy efficiency, provides an important basis for battery management and usage decisions, and helps to improve the efficiency and reliability of the vehicle emergency starting power supply.

[0063] In one embodiment, risk identification is performed on battery charge and discharge energy data to obtain potential fault information, and risk assessment is performed on the potential fault information based on the energy balance trend to obtain fault warning information, including: Energy fluctuation analysis is performed on the battery charge and discharge energy data to obtain battery fluctuation data. Energy fluctuation analysis includes calculating the charge and discharge energy change rate of the battery within a certain time interval and statistically analyzing the distribution of the energy change rate. Battery fluctuation data includes statistical characteristics such as the mean, variance, and peak value of the energy change rate.

[0064] Based on the obtained battery fluctuation data, the battery charge and discharge mode is classified to obtain the battery usage mode type. The battery fluctuation data is used as the input feature and the battery usage mode type is output. The usage mode type includes normal use, frequent charge and discharge, long-term shelving, etc.

[0065] The obtained battery usage pattern type is subjected to abnormal pattern recognition to identify abnormal usage pattern information. Abnormal pattern recognition is performed based on preset normal usage rules. For example, the number of charge and discharge times exceeding a threshold, the single charge and discharge time being too long, etc. are all considered abnormal patterns. The recognition result includes the type and duration of the abnormal pattern.

[0066] The abnormal usage mode information is analyzed for deviations based on the preset battery performance standard parameters to obtain battery performance degradation information. Standard parameters include capacity attenuation rate and internal resistance growth rate under normal usage conditions. The degree of battery performance degradation is quantified by comparing the difference between the performance parameters in the actual usage mode and the standard parameters.

[0067] The energy balance trend of the battery performance degradation information is evaluated to obtain the fault development trend. The evaluation process takes into account factors such as battery capacity and charge and discharge efficiency, and predicts the energy balance status in the future. The fault development trend is represented by the slope and inflection point of the energy balance curve.

[0068] Fault warning is performed according to the fault development trend to obtain the fault warning time point. The warning is based on the preset fault threshold. When the predicted energy balance curve reaches or exceeds the threshold, the time point is recorded as the fault warning time point. The setting of the threshold takes into account factors such as battery type and usage environment.

[0069] Comprehensively analyze the fault development trend and fault warning time point to obtain fault warning information. Comprehensive analysis includes evaluating the severity of the fault, predicting the remaining service life, and recommending maintenance measures. Fault warning information includes detailed data analysis results and preliminary treatment suggestions.

[0070] This embodiment can accurately identify abnormal usage patterns and effectively prevent battery damage caused by improper use by performing comprehensive fluctuation analysis and pattern classification on battery charge and discharge energy data. Deviation analysis is performed based on preset battery performance standard parameters to achieve accurate quantification of battery performance degradation, providing a reliable basis for timely maintenance and replacement. By evaluating the development trend of faults, potential faults can be predicted in advance to avoid problems such as vehicle failure to start caused by sudden faults. Combined with the comprehensive analysis of fault development trends and warning time points, it can not only provide accurate fault warning information, but also provide targeted maintenance suggestions, greatly improving the reliability and service life of the vehicle's emergency starting power supply. The entire detection and control process has a high degree of automation, which reduces human judgment errors and improves detection efficiency and accuracy.

[0071] In one embodiment, the initial power management control scheme is iteratively optimized according to the fault warning information to obtain a corresponding emergency startup power protection strategy, including: When classifying the risk level of fault warning information, various types of fault warning information are analyzed and evaluated according to the preset risk assessment criteria. The assessment criteria may include factors such as the severity of the fault, the probability of occurrence, and the scope of impact. By comprehensively considering these factors, the fault warning information is divided into multiple risk levels, such as low risk, medium risk, high risk, and extremely high risk.

[0072] According to the multi-level fault risk levels, the initial power management control scheme is optimized in a hierarchical manner. For faults of different risk levels, corresponding optimization strategies are adopted. For example, for low-risk faults, only the initial scheme may need to be fine-tuned; for high-risk faults, more aggressive protection measures may need to be taken. Through this hierarchical optimization, multiple alternative protection strategies are generated, each corresponding to a different risk level and protection strength.

[0073] The safety of the generated multiple alternative protection strategies is then evaluated. Multiple safety indicators are considered during the evaluation process, such as power supply stability, overload protection capability, short circuit protection, etc. The safety factor of each alternative strategy is calculated by simulating the system response under different fault scenarios. The safety factor reflects the strategy's ability to protect the system safety under various fault conditions.

[0074] Based on the calculated safety factor, the system ranks multiple alternative protection strategies. The ranking criteria mainly consider the safety factor, and may also comprehensively consider factors such as the difficulty of implementing the strategy and resource consumption. The ranking results reflect the priority of each strategy, and the strategy with a higher safety factor is ranked higher.

[0075] According to the priority sorting results, the strategy is screened. During the screening process, the top several strategies are selected as candidates, or the strategy with a safety factor exceeding the preset safety threshold is selected. Through this screening step, the initial power protection strategy is obtained.

[0076] The initial power protection strategy obtained by screening is simulated and verified. During the simulation process, various possible fault scenarios and working conditions are simulated to test the response effect and protection capability of the strategy. The simulation results include the performance data of the strategy under different conditions, such as response time, protection effect, system stability and other indicators.

[0077] According to the simulation results, the parameters of the initial power protection strategy are adjusted. During the adjustment process, the simulation data is analyzed to find out the shortcomings of the strategy, such as slow response and insufficient protection. In response to these problems, the relevant parameters of the strategy are fine-tuned, such as adjusting the protection threshold and optimizing the control algorithm. After multiple rounds of adjustment and verification, the optimized emergency startup power protection strategy is finally obtained.

[0078] This embodiment achieves accurate identification and classification of faults of different severity by dividing the fault warning information into risk levels, providing a scientific basis for the subsequent formulation of protection strategies. The initial power management control scheme is hierarchically optimized based on the multi-level fault risk level, and multiple alternative protection strategies are generated, which greatly improves the flexibility and pertinence of dealing with different fault conditions. Through safety assessment and priority sorting, the selection of the best protection strategy is ensured, and the safety and reliability of the emergency starting power supply are effectively improved. The process of simulation verification and parameter adjustment makes the final protection strategy more complete and practical, and can better adapt to actual application scenarios. This iterative optimization method not only improves the effectiveness of power supply protection, but also enhances the adaptability and robustness of the system, providing a strong guarantee for the safe and stable operation of the vehicle emergency starting power supply.

[0079] Reference Figure 2 As shown, the present invention provides a detection control device for a vehicle emergency starting power supply, which is applied to any of the above-mentioned detection control methods for a vehicle emergency starting power supply, comprising: The acquisition module is used to obtain the data of multiple battery type interfaces of the vehicle emergency starting power supply, and perform electrical characteristic recognition processing to obtain battery interface characteristic information; An analysis module, which is used to obtain real-time operation data of the vehicle emergency starting power supply, associate the real-time operation data with the battery interface characteristic information, and obtain a battery operation status evaluation result; The correlation module is used to obtain the battery charge and discharge energy data of the vehicle emergency starting power supply, and to predict the battery state based on the battery charge and discharge energy data to obtain the battery energy balance trend; A processing module, the processing module is used to comprehensively analyze the battery operation status evaluation results and the battery energy balance trend to obtain an initial power management control plan; A control module is used to identify risks of battery charging and discharging energy data, obtain potential fault information, conduct risk assessment on the potential fault information based on energy balance trends, and obtain fault warning information; The execution module is used to iteratively optimize the initial power management control plan according to the fault warning information to obtain the corresponding emergency starting power protection strategy.

[0080] The present invention provides a detection and control system for a vehicle emergency starting power supply. By acquiring multiple battery type interface data and performing electrical characteristic identification processing, the performance characteristics of different types of batteries can be more accurately evaluated, thereby improving the accuracy of power management and providing a more reliable basis for system operation. Real-time operation data is associated with battery interface characteristic information to achieve a refined evaluation of the battery operation status, which helps to timely discover potential problems and avoid power performance degradation or failure. Battery status prediction based on battery charge and discharge energy data can ensure that the emergency starting power supply can operate efficiently under different usage conditions, reduce unnecessary energy loss, and improve the overall operation efficiency of the system. By comprehensively analyzing the battery operation status evaluation results and energy balance trends, a more reasonable power management control plan is formulated, and iterative optimization is performed through risk identification and evaluation, thereby effectively improving the reliability and service life of the emergency starting power supply. Risk identification and evaluation of battery charge and discharge energy data can flexibly adjust the protection strategy according to the characteristics of different battery types and usage conditions, so that the system is more adaptable to diverse emergency starting demand scenarios.

[0081] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0082] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A detection and control method for a vehicle emergency starting power supply, characterized in that: include: Acquire multiple battery type interface data of the vehicle emergency starting power supply, and perform electrical characteristic recognition processing to obtain battery interface characteristic information; Acquire real-time operation data of the vehicle emergency starting power supply, associate the real-time operation data with the battery interface characteristic information, and obtain a battery operation status evaluation result; Acquiring battery charge and discharge energy data of the vehicle emergency starting power supply, performing battery status prediction based on the battery charge and discharge energy data, and obtaining a battery energy balance trend; Comprehensively analyzing the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control plan; Performing risk identification on the battery charging and discharging energy data to obtain potential fault information, and performing risk assessment on the potential fault information based on the energy balance trend to obtain fault warning information; The initial power management control scheme is iteratively optimized according to the fault warning information to obtain a corresponding emergency starting power protection strategy.

2. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The method of acquiring the multi-battery type interface data of the vehicle emergency starting power supply and performing electrical characteristic recognition processing to obtain the battery interface characteristic information includes: Performing high-frequency anti-interference sampling on the vehicle emergency starting power supply to obtain an original interface parameter data set including voltage fluctuation rate, current fluctuation rate, charge and discharge response time and impedance; Constructing a nonlinear mapping matrix for the original interface parameter data set to obtain an interface parameter feature mapping matrix; Performing vector weight calculation on the interface parameter feature mapping matrix to obtain a battery interface information entropy weight vector; Performing feature decoupling on the battery interface information entropy weight vector to obtain interface decoupling information; Constructing a probability evolution map of interface electrical characteristics based on the interface decoupling information to obtain an interface state transfer matrix; An information game association is constructed based on the interface state transfer matrix and the interface decoupling information to obtain a battery interface feature association network; Topological structure analysis and node identification are performed on the battery interface feature association network to obtain the battery interface feature information.

3. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The acquiring of real-time operation data of the vehicle emergency starting power supply, associating the real-time operation data with the battery interface characteristic information to obtain a battery operation status evaluation result, includes: Extracting multi-dimensional voltage parameters from the real-time operation data, and obtaining a voltage feature vector through discrete wavelet transform and statistical feature analysis; Performing current spectrum mapping decomposition on the real-time operation data to obtain a current characteristic mapping; Performing feature association according to the voltage feature vector and the current feature map to obtain a battery operating state feature association matrix; Performing coding matrix conversion on the battery interface characteristic information to obtain a battery interface characteristic coding matrix; An association evaluation is performed on the battery interface feature coding matrix and the battery operation status feature association matrix to obtain the battery operation status evaluation result.

4. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The acquiring of battery charge and discharge energy data of the vehicle emergency starting power supply, and performing battery state prediction based on the battery charge and discharge energy data to obtain a battery energy balance trend includes: Collect the battery voltage, battery current and temperature data of the vehicle emergency starting power supply, and integrate them synchronously to obtain the battery charging and discharging energy data Performing multi-dimensional feature construction on the battery charge and discharge energy data to obtain a corresponding battery energy feature set; Perform multi-scale state analysis based on the battery energy feature set to obtain a battery energy change prediction sequence; Performing trend analysis on the battery energy change prediction sequence to obtain a battery energy decay prediction result; The energy distribution inside the battery pack is dynamically calibrated and evaluated according to the battery energy decay prediction result to obtain the battery energy balance trend.

5. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The comprehensive analysis of the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control scheme includes: Calculate the health of the battery to obtain a battery health index. Calculate the battery energy efficiency according to the battery energy balance trend to obtain an energy utilization efficiency index; Comprehensively evaluating the battery health index and the energy utilization efficiency index to obtain a comprehensive evaluation value of battery performance; Perform battery charge and discharge threshold and power limit parameter setting analysis based on the comprehensive battery performance evaluation value to obtain a basic power management strategy; Dynamically adjusting the power management basic strategy to obtain adaptive control parameters; Based on the adaptive control parameters, corresponding battery charge and discharge control instructions and power output adjustment instructions are generated and integrated to obtain the initial power management control solution.

6. The detection and control method of the vehicle emergency starting power supply according to claim 5, characterized in that: The battery energy efficiency is calculated according to the battery energy balance trend to obtain an energy utilization efficiency index, including: Performing battery charge and discharge cycle identification on the battery energy balance trend to obtain the number of charge and discharge cycles; Counting the number of charge and discharge cycles to obtain a battery usage frequency index; Calculate the attenuation rate according to the battery usage frequency index to obtain the capacity attenuation coefficient; Integrating the number of charge and discharge cycles to obtain a cumulative input and output energy value of the battery; Calculating the conversion efficiency according to the accumulated input and output energy values ​​of the battery and the capacity attenuation coefficient to obtain an energy conversion efficiency coefficient; Performing performance rating according to the energy conversion efficiency coefficient to obtain a battery energy utilization level; The battery energy utilization level is converted into a numerical index to obtain an energy utilization efficiency index.

7. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The performing risk identification on the battery charging and discharging energy data to obtain potential fault information, and performing risk assessment on the potential fault information based on the energy balance trend to obtain fault warning information includes: Performing energy fluctuation analysis on the battery charging and discharging energy data to obtain battery fluctuation data; Classify the battery charging and discharging mode according to the battery fluctuation data to obtain the battery usage mode type; Performing abnormal pattern recognition on the battery usage pattern type to obtain abnormal usage pattern information; Performing deviation analysis on the abnormal usage mode information according to preset battery performance standard parameters to obtain battery performance degradation information; Performing trend evaluation on the energy balance trend of the battery performance degradation information to obtain a fault development trend; Perform fault warning according to the fault development trend to obtain the fault warning time point; A comprehensive analysis is performed on the fault development trend and the fault warning time point to obtain fault warning information.

8. The detection and control method of the vehicle emergency starting power supply according to claim 1, characterized in that: The iterative optimization of the initial power management control scheme according to the fault warning information to obtain a corresponding emergency startup power protection strategy includes: Classifying the fault warning information into risk levels to obtain multiple levels of fault risk levels; hierarchically optimizing the initial power management control scheme according to the multi-level fault risk levels to obtain a plurality of alternative protection strategies; Performing safety evaluation on the plurality of candidate protection strategies to obtain a safety factor for each of the candidate protection strategies; Sorting the plurality of candidate protection strategies according to the safety factor to obtain a priority sorting result; Performing strategy screening on the plurality of candidate protection strategies according to the priority sorting result to obtain a corresponding initial power supply protection strategy; Performing simulation verification on the initial power supply protection strategy to obtain simulation results; The initial power supply protection strategy is parameter-adjusted according to the simulation result, and the emergency power supply protection strategy is started.

9. A detection and control device for a vehicle emergency starting power supply, characterized in that: The detection and control method of the vehicle emergency starting power supply applied to any one of claims 1 to 8 above comprises: A collection module, the collection module is used to obtain multiple battery type interface data of the vehicle emergency starting power supply, and perform electrical characteristic recognition processing to obtain battery interface characteristic information; An analysis module, the analysis module is used to obtain real-time operation data of the vehicle emergency starting power supply, associate the real-time operation data with the battery interface characteristic information, and obtain a battery operation status evaluation result; A correlation module, the correlation module is used to obtain battery charge and discharge energy data of the vehicle emergency starting power supply, perform battery state prediction based on the battery charge and discharge energy data, and obtain a battery energy balance trend; A processing module, the processing module is used to comprehensively analyze the battery operation status evaluation result and the battery energy balance trend to obtain an initial power management control plan; A control module, the control module is used to perform risk identification on the battery charging and discharging energy data to obtain potential fault information, perform risk assessment on the potential fault information based on the energy balance trend, and obtain fault warning information; An execution module is used to iteratively optimize the initial power management control scheme according to the fault warning information to obtain a corresponding emergency starting power protection strategy.

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