Battery management and control method and system for new energy automobile

Abstract

The invention relates to the technical field of battery management, and discloses a battery management and control method and system for a new energy automobile, and the method comprises the steps: collecting the information of the new energy automobile, generating a battery monitoring data stream, recognizing the monitoring data stream, extracting the real-time and regular uploading information, uploading the information to a cloud platform, and analyzing and adjusting a battery health model in the cloud platform. The method comprises the steps of sending a task request to a new energy automobile of the same model, obtaining and analyzing key parameters to generate battery health features, sending the battery health features to the automobile, and adjusting a battery management and control model, so that real-time monitoring and intelligent management are achieved, battery health is optimized, the service life is prolonged, and the safety and performance of the new energy automobile are improved. The problem that in the prior art, the potential risk of an automobile battery cannot be handled in time is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, and particularly relates to a battery management method and system for new energy vehicles. BACKGROUND

[0002] The battery of a new energy vehicle is a core component thereof, and directly affects the endurance, performance and safety of the vehicle. With the popularization of new energy vehicles, the role of a battery management system (BMS) becomes increasingly important. The battery management system not only monitors the state of the battery, but also optimizes and protects the battery to prevent overcharging, overdischarging, overheating and the like. In the prior art, a fixed management algorithm is usually used to manage and control the battery of a vehicle. However, this management method cannot timely apply to potential risks of the battery of the vehicle. SUMMARY

[0003] The present application relates to the technical field of battery management, and particularly relates to a battery management method and system for new energy vehicles.

[0004] The present application is implemented in the following manner. In a first aspect, the present application provides a battery management method for new energy vehicles, comprising the following steps. S1: collecting information of a new energy vehicle to obtain a monitoring data stream of a battery of the vehicle; S2: identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals of a predetermined time; S3: uploading the real-time upload information and the timing upload information to a cloud platform based on a preset upload mechanism; S4: analyzing and adjusting a battery health model of a specified vehicle model in the cloud platform according to the real-time upload information and the timing upload information; S5: sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain key parameters of the monitoring data stream of each new energy vehicle, and analyzing the key parameters to generate battery health characteristics; S6: sending the battery health characteristics to the corresponding new energy vehicle to adjust a battery management model deployed in the new energy vehicle accordingly.

[0005] In a second aspect, the present application provides a battery management system for new energy vehicles, which is used to implement the battery management method for new energy vehicles according to any one of the first aspect, and comprises the following components. A data monitoring module is configured to collect information of a new energy vehicle to obtain a monitoring data stream of a battery of the vehicle; An information extraction module is configured to identify the monitoring data stream to extract real-time upload information and generate timing upload information at a predetermined interval; An information upload module is configured to upload the real-time upload information and the timing upload information to a cloud platform based on a preset upload mechanism; A health analysis module is configured to analyze and adjust a battery health model of a specified vehicle model in the cloud platform based on the real-time upload information and the timing upload information; A health positioning module is configured to send a task request to each new energy vehicle belonging to the specified vehicle model based on the adjusted battery health model to obtain key parameters of the monitoring data stream of each new energy vehicle and analyze the key parameters to generate battery health characteristics; An algorithm adjustment module is configured to send the battery health characteristics to the corresponding new energy vehicle to adjust a battery management model deployed in the new energy vehicle.

[0006] The present application provides a battery management method for new energy vehicles, which has the following advantages: The present application collects new energy vehicle information, generates battery monitoring data stream, identifies the monitoring data stream, extracts real-time and timing upload information, uploads the information to the cloud platform, analyzes and adjusts the battery health model in the cloud platform, sends a task request to the same type of new energy vehicle, obtains and analyzes the key parameters to generate battery health characteristics, sends the battery health characteristics to the vehicle, adjusts the battery management model, realizes real-time monitoring and intelligent management, optimizes battery health, prolongs the service life, improves the safety and performance of new energy vehicles, and solves the problem of not being able to respond to potential risks of vehicle batteries in a timely manner in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a step schematic diagram of a battery management method for new energy vehicles provided by an embodiment of the present application; Figure 2 is a structural schematic diagram of a battery management system for new energy vehicles provided by an embodiment of the present application. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0009] The implementation of the present application is described in detail below in combination with specific embodiments.

[0010] Referring to Figure 1 , Figure 2 , a preferred embodiment of the present application is provided.

[0011] In a first aspect, the present application provides a battery management method for new energy vehicles, comprising: S1: collecting information of a new energy vehicle to obtain a monitoring data stream of a battery of the vehicle; S2: identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals of a predetermined time; S3: uploading the real-time upload information and the timing upload information to a cloud platform based on a preset upload mechanism; S4: analyzing and adjusting a battery health model of a specified vehicle model in the cloud platform according to the real-time upload information and the timing upload information; S5: sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain key parameters of the monitoring data stream of each new energy vehicle and analyze the key parameters to generate battery health characteristics; S6: sending the battery health characteristics to the corresponding new energy vehicle to make corresponding adjustments to a battery management model deployed in the new energy vehicle.

[0012] Specifically, in step S1 of the embodiments provided by the present application, various types of sensors are installed in the battery system of the new energy vehicle. Temperature sensors are used to monitor the temperature of battery monomers or battery packs, current sensors are used to record charging and discharging currents, voltage sensors are used to measure the voltage of battery monomers or battery packs, and other sensors such as pressure sensors, humidity sensors, etc. are used to monitor other environmental parameters as needed.

[0013] More specifically, through the data acquisition module, real-time sensor data is acquired, the collected data is transmitted to the central control unit (ECU) through the vehicle-mounted network (such as CAN bus), noise and invalid data are removed to ensure the accuracy of the data, the data of different sensors are time-synchronized to ensure the consistency and synchronization of the data, the preprocessed data is temporarily stored in the vehicle-mounted storage device, a data cache area is set up to deal with sudden high data volume, important data is uploaded to the cloud platform in real time through the wireless communication module (such as 4G / 5G), the aggregated data is uploaded at a preset time interval to reduce the communication load, various parameters of the battery are comprehensively monitored by installing various sensors to provide complete battery status information, the accuracy and consistency of the data are ensured through data filtering and synchronization to improve the credibility of the monitoring data, the real-time nature of important data and the timeliness of global data are ensured through real-time and timing upload mechanisms to effectively support subsequent analysis and decision-making, the local storage and cache processing mechanism ensures that data loss will not occur due to instantaneous communication problems during data upload, improving the reliability and security of data acquisition, and the complete, accurate and real-time monitoring data stream provides basic data support for subsequent battery health model analysis, task request sending, key parameter generation and other links, improving the effectiveness and accuracy of the entire battery management method.

[0014] It can be understood that through detailed battery monitoring data flow, the running state of the battery can be better understood, potential problems can be discovered in time, and corresponding measures can be taken to optimize the battery management strategy, prolong the battery life, and improve the running safety and reliability of the vehicle.

[0015] Specifically, in steps S2 and S3 of the embodiments provided in the application, the monitoring data flow is analyzed, different types of sensor data are processed separately, and the data is divided into real-time upload information and timing upload information according to the importance and real-time requirement of the data.

[0016] It should be noted that if the monitoring data flow of the vehicle is uploaded to the cloud platform at each moment to update the battery health model, it will cause a large burden on the network pressure, and also cause a large burden on the model processing capacity of the cloud platform, therefore, the way adopted by the technical solution of the application is to continuously identify the monitoring data flow, and generate real-time upload information when it is identified that the data part of the monitoring data flow at the current moment has upload value, so as to upload to the cloud platform in real time.

[0017] More specifically, the monitoring data flow is compressed at intervals to generate corresponding timing upload information uploaded to the cloud platform, and the real-time upload information with high value is uploaded in real time, and the timing upload information with low value is uploaded at intervals, and the preset upload mechanism is used to realize the balance of efficiency and value.

[0018] It should be noted that the timing upload of the timing upload information includes monitoring of network quality fluctuations to upload the timing upload information at a time when the network quality is suitable, and also includes monitoring of the vehicle condition, and when an abnormal condition of the vehicle is monitored, the current monitoring data stream is uploaded as real-time upload information.

[0019] It can be understood that through the identification and classification of the monitoring data stream, the key parameters can be monitored and uploaded in real time, the accuracy and timeliness of battery monitoring are improved, the data is divided into real-time and timing two categories for uploading, the efficiency of data transmission is optimized, unnecessary communication load is avoided, and timely transmission of important information is ensured, real-time extraction and uploading of key parameter data can quickly detect abnormal conditions of the battery, timely measures are taken to ensure the safety of the battery and the vehicle, the data amount is reduced by summarizing and compressing the timing upload information, the uploading efficiency is improved, and the communication cost is reduced.

[0020] Specifically, in step S4 of the embodiment provided by the application, the cloud platform receives the real-time upload information and the timing upload information from the vehicle, stores the received data in a database, classifies and manages the data according to time, vehicle ID, sensor type, etc., cleans the stored data to remove noise, repetition and error data, ensures data quality, and standardizes the data to make data of different times and different vehicles comparable.

[0021] More specifically, according to the specified automobile model, the corresponding battery health model is selected, and in the cloud platform, the corresponding battery health model is deployed for each specified automobile model, that is, the battery health model of one type of automobile is used to collect the upload information of each new energy vehicle of the same type of automobile, to analyze and adjust the battery health model, and to predict and display the battery health condition of each new energy vehicle of the same type of automobile.

[0022] More specifically, the battery health model predicts and expresses the change trend of the battery health condition of the battery of the new energy vehicle under various conditions, so that the uploaded information can be used for data training, detection and correction of the battery health model, and at the same time, the corrected battery health model can be used to locate the battery health condition of each vehicle of the same type of automobile, that is, to detect whether the battery has abnormal conditions (such as high temperature, abnormal voltage, etc.), to evaluate the health status of the current battery according to real-time data, to calculate the health index (SOH), to analyze the long-term trend of the battery using the timing upload information, to understand the attenuation law and use of the battery, to identify the use mode and load characteristics of the battery, and to analyze the performance of the battery under different working conditions.

[0023] It can be understood that by combining real-time and timing uploaded information, the health status of the battery can be comprehensively and accurately evaluated, the accuracy of the health evaluation is improved, real-time data analysis can quickly detect abnormal conditions of the battery, timely generate alarm information, ensure the safety of the vehicle and the user, adjust and optimize the model periodically, make the health model more accurately reflect the actual state and performance changes of the battery, improve the prediction ability of the model, use the timing uploaded information for long-term trend analysis, find the attenuation law and use characteristics of the battery, and provide a scientific basis for long-term maintenance and maintenance.

[0024] Specifically, in step S5 of the embodiment provided by the present application, based on the adjusted battery health model in the previous step, the data stream key parameters (such as voltage, current, temperature, SOC, etc.) that need to be monitored are determined, a task request is generated, the key parameter list that needs to be obtained and its sampling frequency and time range are specified, and the generated task request is sent to each new energy vehicle belonging to the specified automobile model in the cloud platform.

[0025] More specifically, after each new energy vehicle receives the task request, it confirms the reception and starts to perform the data collection task. Each new energy vehicle monitors and collects the data stream of the key parameters in real time according to the requirements of the task request, uploads the collected key parameter data stream to the cloud platform in real time, the cloud platform receives the monitoring data stream uploaded by each new energy vehicle, and stores the received data stream according to the vehicle ID, time stamp, etc. to ensure the integrity and traceability of the data.

[0026] More specifically, the received data stream is analyzed, the specific values of the key parameters are extracted, and the key parameters of each vehicle are analyzed according to the requirements of the battery health model to obtain the battery health characteristics of each vehicle, i.e. the battery health status of each vehicle at the current time and the expected health change trend of the battery in the future time.

[0027] It can be understood that by monitoring the real-time data stream and collecting the key parameters of each new energy vehicle, the real-time and accuracy of the data are ensured, the accurate battery health characteristics are extracted from the massive data by using the adjusted battery health model, the accuracy of the battery health evaluation is improved, the individualized battery health characteristic report is generated for each new energy vehicle, the detailed health status and trend analysis are provided, the adaptability adjustment of the battery control of the new energy vehicle is performed, potential problems are found in time and warning information is generated by analyzing the battery health characteristics, specific maintenance suggestions are provided, the battery life is prolonged, and the vehicle safety is ensured.

[0028] Specifically, in step S6 of the embodiment provided by the present application, the latest battery health characteristics of each new energy vehicle are extracted from the database, the extracted battery health characteristic data is packaged to form a standardized data packet, ensuring the integrity and security of the data, a task request containing the data packet of the battery health characteristics and the adjustment instruction is generated, and the task request is sent to the corresponding new energy vehicle through the network, ensuring that the task request can stably and quickly reach the target vehicle. After receiving the task request, the new energy vehicle receives and decodes the data packet, and the vehicle control system confirms that the received data packet is complete and correct, and sends confirmation feedback information to the cloud platform.

[0029] More specifically, the control system inside the new energy vehicle analyzes the received battery health characteristic data, and adjusts the battery management model deployed in the new energy vehicle according to the analyzed battery health characteristics. The adjustment content includes: charging and discharging strategy, optimizing the current, voltage and time parameters of charging and discharging, temperature management, adjusting the control parameters of the battery temperature control system, optimizing the cooling and heating strategy, health management, adjusting the maintenance and maintenance strategy of the battery based on the health characteristics, preventing potential failures, energy management, optimizing energy distribution and use strategy, improving battery use efficiency and life.

[0030] More specifically, within a certain period of time after adjustment, the running state of the battery is monitored in real time to verify the effectiveness of the adjustment result, and the battery management model is further optimized according to the monitoring result to ensure that the battery runs in the best state. The specific content, time and result of each adjustment are recorded to form a complete adjustment log, which is analyzed regularly to track the trend of battery health characteristics and provide data support for further optimization.

[0031] It can be understood that by sending the battery health characteristics in real time and adjusting the management and control algorithm, the individual management of each new energy vehicle battery is realized, the use efficiency and life of the battery are improved, the real-time sending and adjustment mechanism greatly improves the response speed of the system, which can quickly respond to changes in the health state of the battery and prevent potential problems from occurring. By adjusting the charging and discharging strategy and the energy management strategy, the utilization efficiency of battery energy is optimized, and energy waste is reduced. Based on the temperature management and maintenance strategy adjustment of the battery health characteristics, the battery aging speed is reduced, and the service life of the battery is prolonged. Through real-time monitoring and adjustment, potential safety hazards are discovered and handled in time, and the overall safety of the new energy vehicle is improved. The adjustment of the battery management and control model is driven by real-time data, which enhances the scientificity and accuracy of the decision-making process, forms a closed-loop control system from data collection, feature generation, task sending, algorithm adjustment to result verification, improves the intelligent level of the battery management system, and ensures the reliability and stability of the system through real-time monitoring and feedback mechanism, reducing the vehicle failure rate caused by battery problems.

[0032] The application provides a battery management method for a new energy vehicle. The application collects information of the new energy vehicle, generates a battery monitoring data stream, identifies the monitoring data stream, extracts real-time and timing upload information, uploads the information to a cloud platform, analyzes and adjusts a battery health model in the cloud platform, sends a task request to a new energy vehicle of the same type, obtains and analyzes key parameters to generate a battery health feature, sends the battery health feature to the vehicle, adjusts a battery management model, realizes real-time monitoring and intelligent management, optimizes battery health, prolongs the service life, improves the safety and performance of the new energy vehicle, and solves the problem that potential risks of the vehicle battery cannot be responded to in a timely manner in the prior art.

[0033] Preferably, the step of collecting information of the new energy vehicle to obtain the monitoring data stream of the vehicle battery comprises: S11: continuously collecting battery temperature parameters, battery electrical parameters and battery operation parameters of the new energy vehicle by a sensor module pre-installed in the new energy vehicle, to obtain the battery temperature parameters, the battery electrical parameters and the battery operation parameters of the new energy vehicle at each time point; S12: constructing three parallel and aligned data time axes, and filling the battery temperature parameters, the battery electrical parameters and the battery operation parameters at each time point into the corresponding positions of the data time axes, to generate the monitoring data stream of the vehicle battery.

[0034] Specifically, temperature sensors, electrical sensors and other necessary operation parameter sensors are installed at key positions of the new energy vehicle battery, the installed sensors are calibrated to ensure the accuracy of the data, and the sampling frequency and data transmission mode of the sensors are configured, the sensor module continuously collects three key parameters of the new energy vehicle battery: the battery temperature parameters include temperature distribution, maximum temperature, minimum temperature, etc., the battery electrical parameters include voltage, current, charging and discharging rate, remaining capacity, etc., and the battery operation parameters include health status, charging and discharging times, fault records, etc.

[0035] More specifically, the sensor module transmits the collected data to the central control unit (CCU) or the vehicle data processing system through wireless or wired means, adds a timestamp to each set of data during data collection to ensure time synchronization of the data, checks the received data to ensure its integrity and accuracy, filters out abnormal data points, creates three parallel and aligned time axes corresponding to the battery temperature parameters, battery electrical parameters, and battery operation parameters, fills the battery temperature parameters, battery electrical parameters, and battery operation parameters collected at each time into their respective time axis positions to ensure time consistency of the data, integrates the three filled time axis data together to generate a complete automobile battery monitoring data stream, stores the generated monitoring data stream in the vehicle data storage system, and regularly uploads it to the cloud server or remote monitoring platform.

[0036] It can be understood that real-time monitoring of the new energy automobile battery state is achieved, which can quickly respond to changes in the battery state, ensuring the safety and reliability of vehicle operation, ensuring the time synchronization of battery temperature parameters, battery electrical parameters, and battery operation parameters by constructing parallel and aligned data time axes, improving the accuracy of data analysis, providing detailed battery operation data, supporting precise battery management and optimization strategies, prolonging battery life and improving battery performance, based on the monitoring data stream, abnormal conditions and potential faults of the battery can be discovered in a timely manner, providing early warning information to prevent further expansion of faults, optimizing charging and discharging strategies through real-time monitoring of battery electrical parameters, improving energy utilization efficiency, extending the range, providing data support for battery management and maintenance of new energy vehicles, and improving the scientificity and accuracy of decision-making.

[0037] Preferably, the step of identifying the monitoring data stream to extract real-time upload information and generating timing upload information at intervals of a predetermined time comprises: S21: dividing the monitoring data stream into continuous time interval data parts based on the data time axis; S22: judging the information real-time upload demand of the current time interval data part based on the monitoring data stream; S23: when the judgment result is that the current time interval data part needs real-time upload, extracting key information of the monitoring data stream in the current time interval data part to generate real-time upload information; S24: when the judgment result is that the current time interval data part does not need real-time upload, temporarily storing the current time interval data part in the cache area, and converting the data in the cache area to generate timing upload information when the data contained in the cache area accumulates to a specified specification.

[0038] Specifically, by using the time axis of the battery temperature parameters, battery electrical parameters and battery operation parameters previously constructed, the monitoring data stream is divided into continuous, predetermined length time intervals (for example, every minute, every five minutes, etc.), the data part of the monitoring data stream in these time intervals is marked to form a plurality of continuous time interval data parts, and conditions for determining whether the data part of the current time interval needs real-time uploading are set based on specific business needs and battery management strategies, such as whether the battery temperature exceeds the safety threshold, whether the battery voltage or current appears abnormal fluctuation, and whether the battery operation state changes significantly.

[0039] More specifically, the above conditions are judged for each time interval data part to determine whether it needs real-time uploading, and when it is judged that the data part of the current time interval needs real-time uploading, key information such as the maximum temperature, the minimum temperature, the average temperature, the maximum value, the minimum value and the average value of the voltage and current, the change of the operation state and the abnormal record are extracted from the data part of the time interval, and the extracted key information is integrated into a structured data packet as real-time uploading information.

[0040] More specifically, when it is judged that the data part of the current time interval does not need real-time uploading, the data part of the time interval is temporarily stored in the cache area, the data accumulation conditions of the cache area are set, such as the data volume reaching a certain size or the time interval reaching a predetermined value, and when the data of the cache area accumulates to the specified specification, the data of the cache area is integrated and converted to generate timing uploading information. The real-time uploading information is transmitted to the remote monitoring platform or the cloud server through the network to ensure the timely delivery of key data, and the timing uploading information is uploaded from the cache area to the remote monitoring platform or the cloud server at regular intervals to ensure the batch transmission of complete data.

[0041] It can be understood that by real-time uploading of key data, the abnormal change of the battery state can be quickly responded to, ensuring the safety of vehicle operation, by judging the real-time uploading needs of the data, the frequency of data transmission is effectively reduced, the network bandwidth is saved, and the transmission efficiency is improved, by timing uploading, the integrity of the monitoring data is ensured, and the risk of data loss is reduced, the combination of real-time uploading and timing uploading effectively optimizes the utilization of system resources, reduces the burden of data processing and transmission, provides high-frequency key data and low-frequency complete data, supports more accurate battery performance analysis and fault diagnosis, and the judgment conditions and time intervals of real-time uploading and timing uploading can be adjusted according to specific needs, having high flexibility and scalability.

[0042] Preferably, the step of judging the information real-time uploading needs of the data part of the current time interval based on the monitoring data stream comprises: S221: performing feature analysis on the battery temperature parameters, battery electrical parameters and battery operation parameters of the monitoring data stream in each time interval to generate battery monitoring information of the new energy vehicle in each time interval; S222: performing abnormality degree evaluation on the change of the battery operation status in continuous time based on the battery monitoring information of adjacent time intervals, and extracting trend features of the change of the battery operation status of the whole monitoring data stream to perform trend consistency evaluation on the change of the battery operation status of the current time interval, to obtain the abnormality degree evaluation result and the trend consistency evaluation result; S223: performing battery out-of-control risk analysis of the new energy vehicle according to the abnormality degree evaluation result and the trend consistency evaluation result to obtain the out-of-control risk parameter of the new energy vehicle; S224: performing evaluation on the out-of-control risk parameter according to a preset standard to determine whether the data part of the monitoring data stream in the current time interval needs to perform information real-time uploading.

[0043] Specifically, the battery temperature parameters, battery electrical parameters (such as voltage, current, SOC, etc.) and battery operation parameters of the monitoring data stream in each time interval are subjected to data statistics, the mean value, maximum value, minimum value, standard deviation and other statistical features of each parameter are extracted, the correlation between the battery temperature parameters and the electrical parameters and the battery operation parameters is analyzed, the correlation coefficient or other correlation indicators are extracted, the data correlation features are formed, the statistical features and the correlation features are combined, and the battery monitoring information of the new energy vehicle in each time interval is generated.

[0044] More specifically, based on the battery monitoring information of adjacent time intervals, the change amplitude and change rate of the battery operation parameters are calculated, the change degree of the battery operation status in continuous time is evaluated, an abnormality degree calculation model (such as based on standard deviation, mean square deviation, etc.) is set, the abnormality degree evaluation of adjacent time interval change is performed, the abnormality degree evaluation result is obtained, the long-term trend features (such as upward trend, downward trend, periodic fluctuation, etc.) of the battery temperature, electrical parameters and operation mode are extracted on the whole monitoring data stream, the trend consistency evaluation of the change of the battery operation status of the current time interval is performed, whether it is consistent with the long-term trend is judged, and the trend consistency evaluation result is obtained.

[0045] More specifically, according to the abnormality degree evaluation result and the trend consistency evaluation result, in combination with a preset risk analysis model (such as based on fuzzy logic, neural network, etc.), the battery runaway risk of the new energy vehicle is analyzed, the runaway risk parameter is calculated, according to the preset runaway risk evaluation standard (such as risk threshold, risk level, etc.), the runaway risk parameter is evaluated, it is judged whether the monitoring data in the current time interval reaches the condition of real-time uploading, when the runaway risk parameter exceeds the preset standard, it is determined that the data part of the current time interval needs to be uploaded in real time; otherwise, the data is temporarily stored for timing uploading.

[0046] It can be understood that through detailed feature analysis and abnormality degree evaluation, accurate monitoring of the battery operating condition is realized, abnormal changes are identified in time, the accuracy of real-time uploading is improved, based on the extraction and evaluation of trend characteristics, the understanding of long-term operating condition changes of the battery is improved, the early warning ability of runaway risk is enhanced, through multi-dimensional abnormality degree evaluation and trend consistency evaluation, in combination with a preset risk analysis model, the reliability and accuracy of runaway risk analysis are improved, through evaluation and judgment of real-time uploading demand, unnecessary data transmission is reduced, data processing and transmission efficiency are optimized, and system resources are saved.

[0047] Preferably, the step of analyzing and adjusting the battery health model of the specified vehicle model in the cloud platform according to the real-time uploading information and the timing uploading information comprises: S41: receiving real-time uploading information and timing uploading information of a new energy vehicle uploaded in real time through a cloud platform, and analyzing the information attribution of the real-time uploading information or the timing uploading information to obtain the specified vehicle model and the specific vehicle code of the new energy vehicle to which the real-time uploading information or the timing uploading information belongs; S42: inducing the real-time uploading information or the timing uploading information into the information set of the specific vehicle code, combining the real-time uploading information or the timing uploading information with the historical uploading information already stored in the information set, to obtain the condition monitoring information of the new energy vehicle corresponding to the specific vehicle code; S43: aggregating the condition monitoring information of each specific vehicle code belonging to the same specified vehicle model to update the original health model reference data of the new energy vehicle of the specified vehicle model, and generating the latest health model reference data; S44: adaptively analyzing the battery health model of the new energy vehicle of the specified vehicle model according to the latest health model reference data to adjust the battery health model.

[0048] Specifically, real-time upload information and timing upload information of new energy vehicles are received from a cloud platform, the received real-time upload information and timing upload information are parsed, and specific models and specific vehicle codes of new energy vehicles to which the information belongs are determined.

[0049] More specifically, the parsed real-time upload information and timing upload information are summarized into information sets corresponding to the specific vehicle codes, new real-time upload information or timing upload information is combined with historical upload information of the specific vehicle codes, and complete condition monitoring information is formed.

[0050] More specifically, condition monitoring information of all specific vehicle codes belonging to the same specified vehicle model is summarized, and original health model reference data of new energy vehicles of the specified vehicle model is updated based on the summarized condition monitoring information to generate the latest health model reference data.

[0051] More specifically, the battery health model of new energy vehicles of the specified vehicle model is adaptively analyzed according to the latest health model reference data, and necessary model adjustment is performed according to the analysis result to ensure that the battery health model can accurately reflect the current health condition.

[0052] More specifically, real-time upload information and timing upload information of new energy vehicles are received from a cloud platform, the information is parsed, specific models and specific vehicle codes to which the information belongs are determined, the parsed information is summarized into information sets corresponding to the specific vehicle codes, new information is combined with historical information to form complete condition monitoring information, condition monitoring information of all specific vehicle codes of the same specified vehicle model is summarized, health model reference data of new energy vehicles of the specified vehicle model is updated to generate the latest reference data, the battery health model of new energy vehicles is adaptively analyzed according to the latest health model reference data, and the battery health model is adjusted according to the analysis result to ensure its accuracy and reliability.

[0053] It can be understood that the effective combination of real-time and timing upload information ensures the completeness and real-time nature of the data, the dynamic update of the health model reference data ensures that the model always reflects the latest operating condition, the adaptive analysis and model adjustment improve the prediction accuracy of the battery health model, and the timely update and adjustment of the battery health model improves the reliability and stability of the new energy vehicle battery management system.

[0054] Preferably, the step of summarizing condition monitoring information of each of the specific vehicle codes belonging to the same specified vehicle model and updating original health model reference data of new energy vehicles of the specified vehicle model to generate the latest health model reference data comprises: S431: Obtain the original health model reference data of the new energy vehicle of the specified vehicle model; wherein the health model reference data comprises a plurality of battery health development curves and a connection relationship between each of the battery health development curves; S432: Perform information mapping analysis on the condition monitoring information of each of the specific vehicle codes according to the health model reference data to obtain the information contribution degree of each of the specific vehicle codes to each of the battery health development curves and the connection relationship in the health model reference data; S433: Based on the information contribution degree of each of the specific vehicle codes to the health model reference data, obtain the confidence weight distribution of the health model reference data at the current time, and generate adjustment tendency labels of each of the battery health development curves and the connection relationship in the health model reference data according to the confidence weight distribution; S434: Continuously accumulate the adjustment tendency labels of each of the battery health development curves and the connection relationship in the health model reference data, and perform overall analysis of the adjustment tendency of each of the accumulated adjustment tendency labels according to a preset standard to determine whether adjustment is needed; S435: When the determination result is that adjustment is needed, analyze the specific adjustment mode in combination with all the accumulated adjustment tendency labels to obtain the specific adjustment mode and adjust the battery health development curves and the connection relationship of the health model reference data accordingly to generate the latest health model reference data.

[0055] Specifically, first, the original health model reference data of the new energy vehicle of the specified vehicle model is obtained, which includes a plurality of battery health development curves and their connection relationship. According to the health model reference data, information mapping analysis is performed on the condition monitoring information of each specific vehicle code. This step aims to determine the information contribution degree of each specific vehicle code to the battery health development curve and the connection relationship.

[0056] It should be noted that the health model reference data is data used by the battery health model for reference to generate a specific model, that is, reference data for model construction. The battery health development curve is used to describe the health development trend of the vehicle battery, which can be used to predict the real-time positioning and future changes of the battery health. The health development trend of the battery is diverse, so there are a plurality of battery health development curves. There is a connection relationship between each of the battery health development curves, that is, the health condition of the battery may change from one battery health development curve to another connected battery health development curve.

[0057] More specifically, based on the information contribution of each specific vehicle code to the health model reference data, the confidence weight distribution at the current time is calculated, which synchronously generates the adjustment tendency mark of each battery health development curve and its connection relationship. The adjustment tendency mark of each battery health development curve and its connection relationship in the health model reference data is continuously accumulated. According to the preset standard, the accumulated adjustment tendency mark is analyzed as a whole to determine whether the health model needs to be adjusted.

[0058] More specifically, when the judgment result is that adjustment is needed, the analysis of the specific adjustment mode is performed in combination with all the accumulated adjustment tendency marks. According to the specific adjustment mode, the battery health development curve and the connection relationship in the health model reference data are adjusted accordingly to generate the latest health model reference data.

[0059] It can be understood that by real-time acquisition and analysis of the condition monitoring information of the specific vehicle code, it is ensured that the health model reference data can dynamically reflect the actual use of the new energy vehicle. Through information mapping analysis and confidence weight distribution calculation, the accuracy of the health model in different use scenarios is improved, making it more reliable. Accumulating and analyzing the adjustment tendency mark can help to find and adjust possible problems in advance, thereby realizing proactive maintenance. In combination with the specific adjustment mode, the health model reference data can be self-adaptively optimized to adapt to different use environments and conditions, thereby improving the overall performance and service life of the new energy vehicle.

[0060] Preferably, the step of sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain the key parameters of the monitoring data stream of each new energy vehicle and analyzing the key parameters to generate the battery health characteristics comprises: S51: When the battery health model is adjusted, a task request is sent to each new energy vehicle belonging to the specified vehicle model managed by the battery health model to drive each new energy vehicle to receive the task request, divide the monitoring data stream into data segments, and analyze the information validity of each data segment to obtain the validity index of each data segment; S52: According to the validity index of each data segment, the monitoring data stream is combined into data segments, and the obtained data segment combination is compressed to generate the key parameters of the monitoring data stream; S53: Upload the key parameters to the cloud platform, and let the battery health model locate the health development of the battery to obtain the battery health characteristics corresponding to the key parameters.

[0061] Specifically, after the battery health model is adjusted, a task request is sent to each new energy vehicle belonging to a specified vehicle model managed by the model, the task request is intended to drive each new energy vehicle to perform subsequent data processing tasks, each new energy vehicle receives and responds to the task request, starts processing its monitoring data stream, and each new energy vehicle divides the monitoring data stream into data segments. Data segment division is to better analyze and process data, making it more conducive to subsequent effectiveness analysis and information processing. The effectiveness of each data segment is analyzed to obtain an effectiveness index of each data segment, which reflects the quality and reliability of each data segment.

[0062] More specifically, according to the effectiveness index of each data segment, the monitoring data stream is combined into data segments, and data segments with high effectiveness indexes will be combined first to ensure the overall quality of the data. The obtained data segment combination is compressed, the purpose of information compression is to reduce data transmission volume while retaining key information, making subsequent processing more efficient. Through information compression, key parameters of the monitoring data stream are generated, the key parameters are core data extracted from a large amount of data that have a clear indication on the battery health status, the generated key parameters are uploaded to the cloud platform to ensure that the data can be uniformly stored and managed, and to provide a data basis for further analysis of the battery health model. The battery health model analyzes the uploaded key parameters on the cloud platform to locate the health development status of the vehicle battery, and determines the health characteristics of the battery by comparing with the health model.

[0063] It can be understood that the adjusted battery health model sends a task request, which can obtain the monitoring data stream of the new energy vehicle in real time, ensuring the timeliness and accuracy of the data. The steps of data segment division and combination, information effectiveness analysis, and information compression realize an efficient data processing flow, greatly improving the efficiency and quality of data processing. The introduction of the effectiveness index makes the data processing and combination more scientific, ensuring the preferential processing and utilization of high-quality data, improving the effectiveness of the data. Through information compression, data transmission volume is reduced, data transmission efficiency is optimized, key information is retained, and the overall performance of the system is improved. By uploading the key parameters to the cloud platform and analyzing them, the battery health model can intelligently monitor and locate the battery health status, providing accurate battery health characteristics. The generated battery health characteristics provide accurate health status information for the battery management system, improving the accuracy and efficiency of battery management, and prolonging the service life of the battery.

[0064] Preferably, the step of sending the battery health characteristics to the corresponding new energy vehicle to make corresponding adjustments to the battery management model deployed in the new energy vehicle comprises: S61: sending the battery health characteristics to the corresponding new energy vehicle; S62: The battery health feature performs safety boundary analysis of the battery operation of the new energy vehicle to generate safety boundary conditions of the new energy vehicle under the current battery health feature; S63: Adjust the control parameters of the battery control model of the new energy vehicle based on the safety boundary conditions.

[0065] Specifically, the battery health features generated from the cloud platform are sent to the corresponding new energy vehicles, and these feature data are reliably transmitted to the control system of the vehicle through wireless communication (such as 4G / 5G network). After the new energy vehicle receives the battery health features, the internal system of the vehicle analyzes these battery health features. The goal of the analysis is to determine the safety boundary conditions under the current battery health state. The safety boundary conditions refer to the parameter range within which the battery can safely operate under the current battery health state, such as the charge and discharge current, voltage range, temperature limit, etc.

[0066] More specifically, based on the safety boundary conditions obtained by analysis, the battery control model of the new energy vehicle is adjusted, including modifying the battery charge and discharge strategy to ensure operation within the safety boundary, adjusting the battery temperature management strategy to avoid excessive high or low temperature, optimizing the battery balancing strategy to ensure consistency of each battery cell, and adjusting the control parameters to ensure that the battery can safely and efficiently operate under the new health feature.

[0067] It can be understood that by sending the battery health features to the new energy vehicle and adjusting the battery control model, the system achieves real-time response and high adaptability, enabling it to dynamically adjust according to changes in the battery health state. By analyzing the battery health features to generate safety boundary conditions, it ensures that the battery operation is always within a safe range, effectively preventing dangerous situations such as battery overheating, overcharging, and over-discharging, improving the safety of vehicle operation. By adjusting the battery control model, the battery charge and discharge strategy and temperature management are optimized, improving the overall performance and efficiency of the battery, extending the service life of the battery. The adjustment process of the battery control model reflects the intelligent level of the system, which can autonomously optimize parameters and adjust strategies, improving the self-adaptability and intelligence of the system.

[0068] Referring to Figure 2 The second aspect, the present application provides a battery control system for a new energy vehicle, which is used to implement the battery control method of any one of the first aspect, comprising: A data monitoring module for collecting information of the new energy vehicle to obtain monitoring data stream of the vehicle battery; An information extraction module for identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals of a predetermined time; an information uploading module, configured to upload the real-time uploading information and the timing uploading information to a cloud platform based on a preset uploading mechanism; a health analysis module, configured to perform corresponding analysis and adjustment on a battery health model of a specified automobile model in the cloud platform according to the real-time uploading information and the timing uploading information; a health positioning module, configured to send a task request to each new energy vehicle belonging to the specified automobile model through the adjusted battery health model, to obtain key parameters of a monitoring data stream of each new energy vehicle, and to analyze the key parameters to generate battery health features; an algorithm adjustment module, configured to send the battery health features to the corresponding new energy vehicle to perform corresponding adjustment on a battery management model deployed in the new energy vehicle.

[0069] In the embodiment, the specific implementation of each module in the system embodiment is described above in the method embodiment, and will not be described here.

[0070] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A battery management method for a new energy vehicle, characterized in that, The method comprises the following steps: S1: collecting information of a new energy vehicle to obtain a monitoring data stream of a battery of the vehicle; S2: identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals of a predetermined time; S3: uploading the real-time upload information and the timing upload information to a cloud platform based on a preset upload mechanism; S4: analyzing and adjusting a battery health model of a specified vehicle model in the cloud platform according to the real-time upload information and the timing upload information; S5: sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain key parameters of the monitoring data stream of each new energy vehicle, and analyzing the key parameters to generate battery health characteristics; S6: sending the battery health characteristics to the corresponding new energy vehicle to adjust a battery management model deployed in the new energy vehicle accordingly, so as to manage and control the battery through the adjusted battery management model.

2. The battery management method of claim 1, wherein, The step of collecting information of a new energy vehicle to obtain a monitoring data stream of a battery of the vehicle comprises: collecting battery temperature parameters, battery electrical parameters and battery operating parameters of the new energy vehicle at each time through a sensor module pre-installed in the new energy vehicle; constructing three parallel and aligned data timelines, and filling the battery temperature parameters, battery electrical parameters and battery operating parameters at each time into the corresponding positions of the data timelines to generate the monitoring data stream of the battery of the vehicle.

3. The battery management method of claim 2, wherein, The step of identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals of a predetermined time comprises: dividing the monitoring data stream into data portions of continuous time intervals based on the data timelines; judging the information real-time upload demand of the data portion of the current time interval based on the monitoring data stream; when the judgment result is that real-time upload is needed, extracting key information from the data portion of the current time interval of the monitoring data stream to generate real-time upload information; when the judgment result is that real-time upload is not needed, temporarily storing the data portion of the current time interval in a cache area, and converting the data in the cache area to generate timing upload information when the data contained in the cache area accumulates to a specified specification.

4. The battery management method of claim 3, wherein, The step of judging the information real-time upload demand of the data portion of the current time interval based on the monitoring data stream comprises: performing feature analysis on the data statistical features and data correlation features of the battery temperature parameters, battery electrical parameters and battery operating parameters in each time interval of the monitoring data stream to generate battery monitoring information of the new energy vehicle in each time interval; performing abnormality evaluation on the change of the operating condition of the vehicle battery in continuous time based on the battery monitoring information of adjacent time intervals, and extracting the trend feature of the change of the operating condition of the vehicle battery of the entire monitoring data stream to evaluate the trend consistency of the change of the operating condition of the vehicle battery in the current time interval, to obtain the abnormality evaluation result and the trend consistency evaluation result; According to the abnormality evaluation result and the trend consistency evaluation result, the battery runaway risk of the new energy vehicle is analyzed to obtain a runaway risk parameter of the new energy vehicle; According to the preset standard, the runaway risk parameter is evaluated to determine whether the data part of the monitoring data stream in the current time interval needs to perform information real-time uploading.

5. The battery management method of a new energy vehicle according to claim 1, wherein, According to the real-time uploading information and the timing uploading information, the battery health model of the specified vehicle model in the cloud platform is analyzed and adjusted, and the steps include: Through the cloud platform, the real-time uploading information and the timing uploading information of the new energy vehicle are received, and the real-time uploading information or the timing uploading information is analyzed to obtain the specified vehicle model and the specific vehicle code of the new energy vehicle to which the real-time uploading information or the timing uploading information belongs; The real-time uploading information or the timing uploading information is summarized into the information set of the specific vehicle code, and the real-time uploading information or the timing uploading information is combined with the historical uploading information stored in the information set to obtain the condition monitoring information of the new energy vehicle corresponding to the specific vehicle code; The condition monitoring information of each specific vehicle code belonging to the same specified vehicle model is summarized to update the original health model reference data of the new energy vehicle of the specified vehicle model, and the latest health model reference data is generated; According to the latest health model reference data, the battery health model of the new energy vehicle of the specified vehicle model is adaptively analyzed to adjust the battery health model.

6. The battery management method of a new energy vehicle according to claim 5, wherein, The condition monitoring information of each specific vehicle code belonging to the same specified vehicle model is summarized, and the original health model reference data of the new energy vehicle of the specified vehicle model is updated to generate the latest health model reference data, and the steps include: The original health model reference data of the new energy vehicle of the specified vehicle model is obtained; wherein the health model reference data includes a plurality of battery health development curves and a connection relationship between each battery health development curve; According to the health model reference data, the condition monitoring information of each specific vehicle code is analyzed to obtain the information contribution degree of each specific vehicle code corresponding to each battery health development curve and the connection relationship in the health model reference data; Based on the information contribution degree corresponding to each specific vehicle code, the confidence weight distribution of the health model reference data at the current time is obtained, and the adjustment tendency mark of each battery health development curve and the connection relationship in the health model reference data is generated according to the confidence weight distribution; The adjustment tendency mark in the health model reference data is continuously accumulated, and the overall analysis of the adjustment tendency of the accumulated adjustment tendency mark is performed according to the preset standard to determine whether adjustment is needed. When the judgment result is that adjustment is needed, the analysis of the specific adjustment mode is combined with all the accumulated adjustment tendency marks to obtain the specific adjustment mode and make corresponding adjustments to the battery health development curve and the connection relationship of the health model reference data to generate the latest health model reference data.

7. The battery management method of a new energy vehicle according to claim 1, wherein, The step of sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain the key parameters of the monitoring data stream of each new energy vehicle and analyzing the key parameters to generate the battery health characteristics includes: When the battery health model is adjusted, a task request is sent to each new energy vehicle belonging to the specified vehicle model managed by the battery health model to drive each new energy vehicle to divide the data segments of the monitoring data stream and analyze the information validity of each data segment to obtain the validity index of each data segment; According to the validity index of each data segment, the data segments of the monitoring data stream are combined, and the obtained data segment combination is information compressed to generate the key parameters of the monitoring data stream; The key parameters are uploaded to the cloud platform, and the battery health model is used to locate the health development of the battery to obtain the battery health characteristics corresponding to the key parameters.

8. The battery management method of a new energy vehicle according to claim 1, wherein, The step of sending the battery health characteristics to the corresponding new energy vehicle to make corresponding adjustments to the battery control model deployed in the new energy vehicle includes: The battery health characteristics are sent to the corresponding new energy vehicle; The battery health characteristics are analyzed to generate the safety boundary condition of the new energy vehicle under the current battery health characteristics; Based on the safety boundary condition, the battery control model of the new energy vehicle is adjusted.

9. A battery management system for a new energy vehicle, characterized in that, A battery control method for a new energy vehicle, as claimed in any one of claims 1-8, includes: A data monitoring module for collecting information of the new energy vehicle to obtain a monitoring data stream of the battery of the vehicle; An information extraction module for identifying the monitoring data stream to extract real-time upload information and generate timing upload information at intervals; An information upload module for uploading the real-time upload information and the timing upload information to the cloud platform based on a preset upload mechanism; A health analysis module for making corresponding analysis and adjustment of the battery health model of the specified vehicle model in the cloud platform based on the real-time upload information and the timing upload information; A health positioning module for sending a task request to each new energy vehicle belonging to the specified vehicle model through the adjusted battery health model to obtain the key parameters of the monitoring data stream of each new energy vehicle and analyze the key parameters to generate the battery health characteristics; An algorithm adjustment module for sending the battery health characteristics to the corresponding new energy vehicle to make corresponding adjustments to the battery control model deployed in the new energy vehicle.

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