CN117458678B - Active equalization battery management system for lead-acid battery pack - Google Patents
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Classifications
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention relates to the technical field of battery management, in particular to an active equalization battery management system of a lead-acid battery pack. In the invention, the PID control algorithm and the Kalman filter are adopted to accurately monitor and regulate the voltage and the current of the battery, the efficiency and the service life are improved, the intelligent prediction module accurately predicts the state of the battery by using the long-short-term memory network algorithm, the problem is identified in advance and preventive measures are taken, the data mining module deeply analyzes the use mode and the performance degradation by using the principal component analysis and the clustering algorithm, the use and maintenance strategy is optimized, the fault tree analysis and the fuzzy logic of the health monitoring module comprehensively evaluate the health of the battery, the load balance control and system integration module improves the system efficiency, and the user interaction module optimizes the user experience and convenience.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to an active equalization battery management system of a lead-acid battery pack.
Background
The technical field of battery management is focused on improving the performance and life of a battery pack while ensuring the safety and efficiency thereof in various applications, and in the fields of electric vehicles, renewable energy storage, emergency power supply systems, etc., lead-acid batteries are widely used because of their cost effectiveness and stability. However, there may be an imbalance in state of charge between individual cells in the battery pack, which may lead to reduced overall performance and even reduced battery life. Therefore, development and optimization of battery management systems are of paramount importance.
The core of the active equalization battery management system of the lead-acid battery pack is to actively balance the charge states of all battery units in the battery pack through an intelligent control technology, so that each individual battery can be operated in an optimal state, the efficiency and the service life of the whole battery pack are maximized, the reliability and the efficiency of the battery pack are improved, particularly in the application requiring long-term or continuous power supply, in order to achieve the aim, means such as a battery monitoring technology, an intelligent control algorithm and the like are generally used, the proper charge and discharge treatment of each battery in the battery pack is ensured, and the electric quantity is transferred from the battery with more charge to the battery with less charge, so that the overall balance and the efficiency of the battery pack are maintained.
The existing lead-acid battery pack active equalization battery management system lacks depth prediction capability on battery state, potential problems are difficult to identify and prevent in advance, the traditional system has limited capability in terms of data mining and analysis, the use and maintenance strategies of the battery cannot be effectively optimized, health monitoring is usually based, comprehensive and accurate assessment is lacking, and a user interaction interface is usually not intuitive and user-friendly, so that operation and maintenance become more complex and time-consuming.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an active equalization battery management system of a lead-acid battery pack.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the lead-acid battery pack active equalization battery management system comprises an active equalization management module, a Bluetooth communication module, an intelligent prediction module, a data mining module, a health monitoring module, a maintenance decision support module, a load balancing control module, a system integration module and a user interaction module;
the active equalization management module is used for adjusting voltage and current by adopting a PID control algorithm based on the lead-acid battery pack real-time monitoring data, and detecting the battery state by combining a Kalman filter to generate battery state data;
The Bluetooth communication module realizes data synchronization by using a Bluetooth low-energy communication protocol based on battery state data, and performs data encryption by applying AES encryption algorithm to generate encrypted communication data;
the intelligent prediction module adopts a long-term and short-term memory network algorithm to conduct deep learning prediction of the battery state based on the encrypted communication data, and conducts model optimization processing to generate a prediction report;
the data mining module performs data mining by using a principal component analysis and clustering algorithm based on the prediction report, and presents a result through a data visualization technology to generate a mining analysis report;
the health monitoring module is used for carrying out battery health assessment by adopting fault tree analysis and fuzzy logic based on the mining analysis report, and carrying out maintenance decision logic to generate a health condition report;
the maintenance decision support module is used for making a battery maintenance strategy based on the health status report and combining historical data with a decision tree algorithm to generate a maintenance scheme;
the load balancing control module is based on a maintenance scheme, applies a load balancing algorithm to optimally allocate the load of the battery pack, and generates a load optimization report through a performance monitoring adjustment strategy;
The system integration module integrates all parts of the integrated system by utilizing a middleware technology and an API based on the load optimization report, and performs system-level performance test to generate a system running state;
the user interaction module develops an operation interface based on the running state of the system by adopting a user experience design principle, and optimizes by combining user feedback to generate an optimized operation interface;
the battery state data specifically comprise voltage and charge and discharge states of battery cells, the encrypted communication data comprise battery state information and operation logs, the prediction report comprises battery performance and expected service life, the mining analysis report specifically comprises a battery use mode and a performance degradation mode, the health status report comprises a current state of the battery and maintenance advice, the maintenance advice comprises maintenance steps and precautions, the load optimization report comprises load distribution and optimization results, the system operation state specifically comprises operation efficiency and performance indexes of the system, and the optimization operation interface provides data display, control operation and user feedback channels.
As a further scheme of the invention, the active equalization management module comprises a voltage detection sub-module, a current regulation sub-module and a capacitance control sub-module;
The voltage detection submodule is used for detecting the voltage of the battery cell by adopting an analog-digital conversion technology based on the lead-acid battery pack real-time monitoring data, removing noise through a high-pass filter and generating voltage detection data;
the regulating submodule regulates voltage and current by adopting a proportional-integral-derivative control algorithm based on voltage detection data, and stably outputs the voltage and current by utilizing a closed-loop feedback mechanism to generate regulated voltage and current data;
the capacitance control submodule detects and analyzes the battery state based on the regulated voltage and current data by combining a Kalman filter technology, optimizes the detection by applying a multivariate data analysis technology and generates battery state data.
As a further scheme of the invention, the Bluetooth communication module comprises a data transmission sub-module, a signal encryption sub-module and an interface compatible sub-module;
the data transmission sub-module uses a Bluetooth low energy communication protocol to carry out data transmission based on battery state data, and applies a data packet and recombination technology to verify the integrity of transmission and generate data in transmission;
the signal encryption submodule encrypts data by applying an AES encryption algorithm based on the data in transmission and combines key management encryption security to generate encrypted data;
The interface compatible submodule carries out interface protocol conversion and data format standardization processing based on the encrypted data and is compatible with an external system to generate encrypted communication data.
As a further scheme of the invention, the intelligent prediction module comprises a data preprocessing sub-module, a characteristic engineering sub-module and a model optimizing sub-module;
the data preprocessing sub-module performs data normalization and outlier processing based on the encrypted communication data, and generates preprocessing data by using a characteristic scaling technology;
the feature engineering submodule performs data feature optimization by applying an automatic feature selection technology based on the preprocessing data to generate optimized feature data;
and the model optimization submodule adopts a long-short-term memory network algorithm to conduct deep learning prediction of the battery state based on the optimization feature data, and uses grid search and cross verification to conduct model parameter optimization to generate a prediction report.
As a further scheme of the invention, the data mining module comprises a data integration sub-module, an algorithm implementation sub-module and a visual display sub-module;
the data integration sub-module performs data integration by adopting an ETL flow based on a prediction report, and cleans, converts and aggregates data from multiple sources to generate a comprehensive data set;
The algorithm implementation submodule carries out pattern recognition and data classification by using a principal component analysis and K-means clustering algorithm based on the comprehensive data set, evaluates data distribution by a probability density function and generates mining result data;
the visual display submodule displays a data mode by adopting an interactive data visual technology comprising a dynamic scatter diagram and a hierarchical clustering tree diagram based on mining result data, and generates a mining analysis report.
As a further scheme of the invention, the health monitoring module comprises a real-time monitoring sub-module, a fault prediction sub-module and an evaluation report generation sub-module;
the real-time monitoring sub-module is used for monitoring the battery state based on the mining analysis report by adopting a sensor network and a real-time data stream processing technology, collecting and analyzing performance indexes and generating real-time monitoring data;
the fault prediction sub-module predicts and analyzes the battery fault by using a fault tree analysis and a fuzzy logic algorithm based on real-time monitoring data, and combines statistical risk assessment to generate a fault prediction result;
and the evaluation report generation sub-module comprehensively analyzes the health state of the battery based on the fault prediction result, and generates a health condition report by applying an intelligent decision support system.
As a further scheme of the invention, the maintenance decision support module comprises a knowledge base construction sub-module, a data analysis sub-module and a recommendation system sub-module;
the knowledge base construction sub-module constructs and updates an expert system knowledge base maintained by the battery based on the health status report, integrates historical maintenance data and pattern analysis, and generates a maintenance knowledge data set;
the data analysis sub-module analyzes a battery maintenance mode based on a maintenance knowledge data set by applying association rule mining and random forests to generate maintenance strategy suggestions;
the recommendation system submodule adopts a decision tree and a collaborative filtering recommendation algorithm to formulate a maintenance strategy based on maintenance strategy suggestion and combines battery performance data and historical maintenance cases to generate a maintenance scheme.
As a further scheme of the invention, the load balance control module comprises a strategy generation sub-module, a dynamic management sub-module and a performance optimization sub-module;
the strategy generation submodule adopts fuzzy logic control and self-adaptive control algorithm to design a load balancing strategy corresponding to multiple battery states based on a maintenance scheme, and generates a load control strategy;
the dynamic management submodule is used for adjusting the load of the battery pack in real time by applying a dynamic programming algorithm based on a load control strategy, and monitoring the battery state in real time by combining a state estimation algorithm to generate dynamic load management data;
And the performance optimization submodule optimizes the load balancing strategy by using a genetic algorithm and a simulated annealing algorithm based on dynamic load management data to generate a load optimization report.
As a further scheme of the invention, the system integration module comprises a function integration sub-module, a system test sub-module and an optimization adjustment sub-module;
the function integration submodule integrates system functions by adopting a service guide architecture and a middleware technology based on a load optimization report, and realizes inter-module communication by applying an interface definition language to generate function integration data;
the system test submodule is used for performing performance and stability tests by applying an automatic test framework and a load test tool based on the function integration data to generate a system test report;
and the optimization adjustment submodule performs optimization adjustment on the system by adopting a continuous integration and continuous deployment process based on the system test report, and generates a system running state.
As a further scheme of the invention, the user interaction module comprises an interface design sub-module, a user feedback sub-module and a system updating sub-module;
the interface design submodule designs an operation interface based on the system running state by adopting a man-machine interaction principle and a user experience design method, and applies a prototype design tool to perform interface prototype manufacture so as to generate an interface prototype design;
The user feedback submodule collects user feedback of an interface based on interface prototype design by using A/B test and user satisfaction investigation, and applies quantitative analysis technology to carry out feedback arrangement to generate a user feedback analysis report;
the system updating submodule carries out iterative optimization on the interface by adopting an agile development method and design thinking based on a user feedback analysis report to generate an optimized operation interface.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the invention, the voltage and the current of the battery can be monitored and regulated more accurately by adopting the PID control algorithm and the Kalman filter, the service efficiency and the service life of the battery are improved, the long-term and short-term memory network algorithm of the intelligent prediction module enables the prediction of the battery state to be more accurate, potential problems can be identified in advance and preventive measures can be taken, the main component analysis and clustering algorithm of the data mining module further deeply analyzes the battery use mode and the performance degradation mode, the battery use and maintenance strategy can be optimized, the fault tree analysis and the fuzzy logic of the health monitoring module enable the battery health assessment to be more comprehensive and accurate, the application of the load balance control module and the system integration module improves the running efficiency and the performance of the whole system, and the optimization of the user interaction module improves the user experience and the operation convenience.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of an active equalization management module according to the present invention;
fig. 4 is a flowchart of a bluetooth communication module according to the present invention;
FIG. 5 is a flow chart of an intelligent prediction module of the present invention;
FIG. 6 is a flow chart of a data mining module of the present invention;
FIG. 7 is a flow chart of a health monitoring module of the present invention;
FIG. 8 is a flow chart of a maintenance decision support module of the present invention;
FIG. 9 is a flow chart of a load balancing control module of the present invention;
FIG. 10 is a flow chart of a system integration module of the present invention;
FIG. 11 is a flowchart of a user interaction module according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 to 2, an active equalization battery management system for a lead-acid battery pack includes an active equalization management module, a bluetooth communication module, an intelligent prediction module, a data mining module, a health monitoring module, a maintenance decision support module, a load balance control module, a system integration module, and a user interaction module;
the active equalization management module is used for adjusting voltage and current by adopting a PID control algorithm based on the lead-acid battery pack real-time monitoring data, and detecting the battery state by combining a Kalman filter to generate battery state data;
the Bluetooth communication module realizes data synchronization by using a Bluetooth low-energy communication protocol based on battery state data, and performs data encryption by applying AES encryption algorithm to generate encrypted communication data;
the intelligent prediction module adopts a long-term and short-term memory network algorithm to conduct deep learning prediction of the battery state based on the encrypted communication data, and conducts model optimization processing to generate a prediction report;
the data mining module is used for carrying out data mining by applying a principal component analysis and clustering algorithm based on the prediction report, and presenting a result through a data visualization technology to generate a mining analysis report;
the health monitoring module is used for carrying out battery health assessment by adopting fault tree analysis and fuzzy logic based on the mining analysis report, and carrying out maintenance decision logic to generate a health condition report;
The maintenance decision support module is used for making a battery maintenance strategy based on the health status report and combining historical data with a decision tree algorithm to generate a maintenance scheme;
the load balance control module performs optimal allocation on the load of the battery pack by applying a load balance algorithm based on a maintenance scheme, and generates a load optimization report through a performance monitoring adjustment strategy;
the system integration module integrates all parts of the system based on the load optimization report by utilizing a middleware technology and an API (application program interface), and performs system-level performance test to generate a system running state;
the user interaction module develops an operation interface based on the running state of the system by adopting a user experience design principle, and optimizes by combining user feedback to generate an optimized operation interface;
the battery state data is specifically the voltage and charge and discharge states of the battery cells, the encrypted communication data comprises battery state information and operation logs, the prediction report comprises battery performance and life expectancy, the mining analysis report specifically comprises a battery use mode and a performance degradation mode, the health status report comprises a battery current state and maintenance advice, the maintenance proposal comprises maintenance steps and preventive measures, the load optimization report comprises load distribution and optimization results, the system running state specifically comprises running efficiency and performance indexes of the system, and the optimized operation interface provides data display, control operation and user feedback channels.
The implementation of the active equalization management module greatly improves the accuracy and efficiency of battery management by utilizing a PID control algorithm and a Kalman filter, and the advanced battery state monitoring and adjustment not only optimizes the charge and discharge process of the battery and prolongs the service life of the battery, but also is beneficial to preventing overcharge and overdischarge and ensures the safety of the system; the application of the Bluetooth communication module ensures the high efficiency and the safety of data transmission through a Bluetooth low-energy communication protocol and an AES encryption algorithm, and the encryption communication mechanism reduces the risk of data leakage and improves the data safety of the whole system, which is particularly important in a modern battery management system; the long-term and short-term memory network algorithm of the intelligent prediction module enables the prediction of the battery state to be more accurate, is beneficial to timely identifying potential battery problems, and therefore preventive maintenance is carried out, the service efficiency of the battery is improved, and potential risks and costs caused by battery faults are reduced; the principal component analysis and clustering algorithm of the data mining module provides deep analysis of battery use modes and performance degradation modes, and the deep data insight is helpful for optimizing battery use strategies, improving overall performance and service life of the battery; the health monitoring module provides comprehensive and accurate assessment for the health condition of the battery through fault tree analysis and fuzzy logic, and the comprehensive health assessment provides reliable data support for maintenance decision, so that the stable operation and long-term reliability of the battery are ensured; the maintenance decision support module combines historical data and a decision tree algorithm, provides a scientific battery maintenance strategy, effectively improves the predictability and accuracy of battery maintenance, and reduces maintenance cost and time; the application of the load balance control module ensures the uniform use of the battery pack, avoids the overload and the rapid degradation of individual batteries, improves the performance of the whole battery pack and prolongs the service life of the batteries; the system integration module ensures the high-efficiency cooperative work of each part of the system and improves the overall operation efficiency and stability of the system through middleware technology and API integrated application; the design of the user interaction module enables the system operation to be more humanized, and user experience is improved. The optimized operation interface and the user feedback channel enable the system to be more easily understood and operated, and improve the operation satisfaction degree of the user and the usability of the system.
Referring to fig. 3, the active equalization management module includes a voltage detection sub-module, a current adjustment sub-module, and a capacitance control sub-module;
the voltage detection sub-module is used for detecting the voltage of the battery cell by adopting an analog-digital conversion technology based on the lead-acid battery pack real-time monitoring data, removing noise through a high-pass filter and generating voltage detection data;
the regulating submodule regulates voltage and current by adopting a proportional-integral-derivative control algorithm based on the voltage detection data, and stably outputs the voltage and current by utilizing a closed-loop feedback mechanism to generate regulated voltage and current data;
the capacitance control sub-module is used for detecting and analyzing the battery state based on the regulated voltage and current data and combining a Kalman filter technology, and optimizing the detection by applying a multivariate data analysis technology to generate battery state data.
The voltage detection submodule monitors the voltage state of the lead-acid battery pack in real time, the process utilizes an analog-digital conversion technology to convert the analog voltage signal of the battery cell into a digital signal so as to facilitate subsequent processing and analysis, a high-pass filter is adopted to remove noise in the voltage signal conversion process, the accuracy of voltage data is ensured, after the step is finished, the module outputs clear and accurate voltage detection data, the basis is provided for subsequent voltage and current regulation, the regulation submodule regulates the voltage and the current of the battery pack according to the voltage detection data, the submodule adopts a proportional-integral-derivative control algorithm, which is an algorithm widely applied to an industrial control system, can dynamically regulate the voltage and the current output according to the real-time change of the voltage detection data, and in order to ensure the stability of the output, the sub-module also utilizes a closed loop feedback mechanism to continuously correct output, thereby avoiding fluctuation caused by external interference or system errors, generating regulated voltage and current data in the process, laying a foundation for accurate monitoring and regulation of battery states, the capacitance control sub-module takes the regulated voltage and current data as input, carries out deep battery state detection and analysis by combining a Kalman filter technology, the Kalman filter is an effective prediction and correction method, can estimate the state of a dynamic system under the condition of noise, also utilizes a multivariate data analysis technology to comprehensively analyze a plurality of state indexes of a battery, is helpful for comprehensively knowing the working conditions of the battery, and the generated battery state data not only comprises instant information of the voltage and the current, but also comprises key indexes such as the whole health state, the expected life and the like of the battery, reliable data support is provided for maintenance and optimization of the battery.
Referring to fig. 4, the bluetooth communication module includes a data transmission sub-module, a signal encryption sub-module, and an interface compatible sub-module;
the data transmission sub-module uses a Bluetooth low energy communication protocol to carry out data transmission based on battery state data, and applies a data packet and recombination technology to verify the integrity of transmission and generate data in transmission;
the signal encryption submodule encrypts data by applying an AES encryption algorithm based on the data in transmission and combines key management encryption security to generate encrypted data;
the interface compatible submodule carries out interface protocol conversion and data format standardization processing based on the encrypted data and is compatible with an external system to generate encrypted communication data.
The data transmission sub-module receives battery state data from the active equalization management module, the Bluetooth low energy communication protocol is adopted to conduct wireless transmission of the data, the protocol is particularly suitable for data exchange of low power consumption equipment, thereby guaranteeing energy efficiency of the battery management system, in the data transmission process, in addition, the sub-module applies a data packetization and reassembly technology to ensure data integrity and accuracy, meaning that original data can be divided into smaller data packets for transmission, the data packets can be reassembled at a receiving end to restore the original data, verification of transmission integrity can be conducted, all the data packets are guaranteed to be correctly received, the generated transmission data has high integrity and reliability, the signal encryption sub-module is used for conducting data encryption processing based on the data in transmission, the sub-module adopts an AES algorithm to encrypt the data, thereby guaranteeing safety and privacy of the data in the transmission process, in addition, in order to further strengthen data safety, the sub-module further, the sub-module can reassemble the data packets to restore the original data, verification of transmission integrity can be conducted, the data can be easily processed by the data transmission data has high integrity and reliability through the user interface, the data access protection system can be compatible with the data encryption sub-module, the data can be widely applied to the data access protection system, and the data has high-level and external data access security interface can be widely compatible with the encryption system through the data interface and decryption system, and has high data access standards, the data can be easily processed through the data access interface and has high-level and has high data access and has high-level and high-quality interface and can be easily processed through the data interface and is compatible with the data encryption system and has high data encryption and encryption system and has high security and high security, the generated encrypted communication data can thus be used safely and efficiently in a variety of different environments.
Referring to fig. 5, the intelligent prediction module includes a data preprocessing sub-module, a feature engineering sub-module, and a model optimization sub-module;
the data preprocessing sub-module performs data normalization and outlier processing based on the encrypted communication data, and generates preprocessing data by using a characteristic scaling technology;
the feature engineering submodule performs data feature optimization by applying an automatic feature selection technology based on the preprocessed data to generate optimized feature data;
the model optimization submodule adopts a long-term and short-term memory network algorithm to conduct deep learning prediction of the battery state based on the optimization feature data, and conducts model parameter optimization by grid search and cross verification to generate a prediction report.
The data preprocessing sub-module executes data normalization based on the encrypted communication data, the step eliminates the scale difference among different features by adjusting the numerical scale, is beneficial to improving the learning efficiency and accuracy of a subsequent model, the outlier processing is performed for identifying and processing outliers in the data, the feature scaling technology is applied to further optimize the data, the applicability of the data in model training is ensured, the function of the feature engineering sub-module cannot be ignored, the automatic feature selection technology such as principal component analysis or recursive feature elimination is applied to accurately identify and select the feature most influencing battery state prediction based on the preprocessed data, the optimization of the data feature not only reduces the complexity of the model, but also improves the accuracy and efficiency of prediction, generates optimized feature data, and provides more accurate and effective input for the model; the model optimization submodule adopts a long-short-term memory network algorithm, is a powerful deep learning model specially processing time series data, is very suitable for dynamic prediction of battery states, combines grid search and cross verification technology, fine adjustment and optimization are carried out on parameters of an LSTM model to achieve optimal prediction performance, the process not only involves optimization of a model structure, but also comprises adjustment of key parameters such as learning rate, layer number and the like, and then generates a detailed prediction report comprising key information such as battery performance, life expectancy and the like.
Referring to fig. 6, the data mining module includes a data integration sub-module, an algorithm implementation sub-module, and a visual display sub-module;
the data integration sub-module adopts an ETL flow to integrate data based on the prediction report, cleans, converts and aggregates the data from multiple sources, and generates a comprehensive data set;
the algorithm implementation submodule carries out pattern recognition and data classification by using a principal component analysis and K-means clustering algorithm based on the comprehensive data set, evaluates data distribution by using a probability density function and generates mining result data;
the visual display submodule displays a data mode by adopting an interactive data visual technology comprising a dynamic scatter diagram and a hierarchical clustering tree diagram based on mining result data, and generates a mining analysis report.
The data integration sub-module integrates data from a plurality of sources by adopting an ETL flow based on a prediction report, and in an extraction stage, relevant data is collected from various different data sources, a conversion stage relates to data cleaning and format standardization so as to eliminate inconsistencies and errors in the data, and in a loading stage, the processed data are integrated into a unified database, so that the generated comprehensive data set is ensured to reach high standards in terms of quality and consistency; the algorithm implementation submodule utilizes principal component analysis to reduce the dimensionality of data, simultaneously retains the most important information, adopts a K-means clustering algorithm to classify the data, identifies different data modes and groups, evaluates the data for more accurately understanding the data distribution by using a probability density function, generates mining result data with depth insight, and enables the modes and the relations of the data to be clear at a glance by adopting interactive data visualization technologies such as a dynamic scatter diagram, a hierarchical clustering tree diagram and the like, thereby improving the understanding and acceptance of users on the data analysis result.
Referring to fig. 7, the health monitoring module includes a real-time monitoring sub-module, a fault prediction sub-module, and an evaluation report generation sub-module;
the real-time monitoring sub-module is used for monitoring the battery state by adopting a sensor network and a real-time data stream processing technology based on the mining analysis report, collecting and analyzing performance indexes and generating real-time monitoring data;
the fault prediction sub-module predicts and analyzes the battery fault by using a fault tree analysis and fuzzy logic algorithm based on the real-time monitoring data, and combines statistical risk assessment to generate a fault prediction result;
the evaluation report generation sub-module comprehensively analyzes the health state of the battery based on the fault prediction result, and generates a health state report by applying an intelligent decision support system.
The real-time monitoring sub-module comprehensively monitors the battery state based on the mining analysis report by utilizing a sensor network and a real-time data stream processing technology, wherein the sensor network is responsible for collecting various performance indexes of the battery cell, such as voltage, current, temperature and the like, and the real-time data stream processing technology is used for analyzing the collected data in real time, so that the continuous monitoring and analysis ensures that any small change of the battery state can be captured in time, and the generated real-time monitoring data comprises detailed and real-time information of the battery performance; the fault prediction submodule predicts and analyzes the potential fault of the battery according to real-time monitoring data by adopting fault tree analysis and a fuzzy logic algorithm, wherein the fault tree analysis is a systematic fault diagnosis method which can identify and evaluate various factors possibly causing the system fault, the fuzzy logic algorithm is used for processing uncertainty and ambiguity, the prediction accuracy is improved, and the module can evaluate the probability and possibility of the occurrence of the fault of the battery by combining with a statistical risk evaluation method, so that a detailed fault prediction result is generated; the evaluation report generation sub-module comprehensively considers the whole health state of the battery based on the fault prediction result, and uses the intelligent decision support system to carry out deep analysis, and the intelligent decision support system utilizes advanced data analysis and machine learning technology to carry out comprehensive evaluation on the health state of the battery, so that a comprehensive health state report is generated, the report not only reflects the current health state of the battery in detail, but also provides specific suggestions for battery maintenance and optimization.
Referring to fig. 8, the maintenance decision support module includes a knowledge base construction sub-module, a data analysis sub-module, and a recommendation system sub-module;
the knowledge base construction sub-module constructs and updates an expert system knowledge base maintained by the battery based on the health status report, integrates historical maintenance data and pattern analysis, and generates a maintenance knowledge data set;
the data analysis sub-module analyzes a battery maintenance mode based on a maintenance knowledge data set by applying association rule mining and random forests to generate maintenance strategy suggestions;
the recommendation system submodule adopts a decision tree and a collaborative filtering recommendation algorithm to combine the battery performance data and the historical maintenance cases based on the maintenance strategy suggestion, and establishes a maintenance strategy to generate a maintenance scheme.
The knowledge base construction submodule collects and integrates historical maintenance data and mode analysis thereof based on health condition reports to form a comprehensive maintenance knowledge data set, the knowledge base not only comprises detailed records of historical maintenance activities, but also fuses the results of the mode analysis, a rich data basis is provided for identifying best maintenance practices, the data analysis submodule is responsible for deeply excavating the maintenance knowledge data set, the battery maintenance modes and trends are analyzed by applying association rule excavation and random forest algorithm, the association rule excavation reveals the association among various maintenance activities, the random forest provides deep analysis on the battery maintenance modes, the process not only enhances the insight on battery maintenance, but also promotes more effective maintenance strategy formulation, and finally maintenance strategy suggestions with practical guiding value are generated; the recommendation system sub-module adopts a decision tree and collaborative filtering recommendation algorithm to combine the battery performance data and the historical maintenance cases to formulate a personalized maintenance strategy according to the maintenance strategy suggestion, the decision tree algorithm helps to identify the most effective maintenance action scheme, the collaborative filtering uses the mode in the historical data to recommend the optimal maintenance strategy for the similar situation, a customized and data-driven maintenance scheme can be provided, and more scientific and accurate decision support is provided for battery maintenance management.
Referring to fig. 9, the load balancing control module includes a policy generation sub-module, a dynamic management sub-module, and a performance optimization sub-module;
the strategy generation submodule adopts fuzzy logic control and self-adaptive control algorithm to design a load balancing strategy corresponding to multiple battery states based on a maintenance scheme, and generates a load control strategy;
the dynamic management submodule adjusts the load of the battery pack in real time by applying a dynamic programming algorithm based on a load control strategy, and monitors the battery state in real time by combining a state estimation algorithm to generate dynamic load management data;
the performance optimization submodule optimizes the load balancing strategy by applying a genetic algorithm and a simulated annealing algorithm based on dynamic load management data to generate a load optimization report.
The strategy generation submodule adopts a fuzzy logic control and self-adaptive control algorithm, a flexible and efficient load balance control strategy is designed by combining the specific state and the characteristic of the battery pack, the fuzzy logic control algorithm optimizes the control decision by processing uncertain and fuzzy information, the self-adaptive control algorithm dynamically adjusts the control parameters according to the real-time state of the battery pack, the strategy design not only considers the current state of the battery pack, but also foresees possible change trend, and the load control strategy with high adaptability is generated; the dynamic management submodule is used for adjusting the load distribution of the battery pack in real time based on a load control strategy and applying a dynamic planning algorithm, which is an optimization algorithm used for searching an optimal solution under a given constraint condition, and can maximize the performance of the whole battery pack while guaranteeing the balance load of each battery unit, and can monitor the working state and the health condition of the battery in real time by combining with a state estimation algorithm so as to generate dynamic and accurate load management data; the performance optimization sub-module is responsible for further improving the efficiency and effectiveness of a load balancing strategy, and based on dynamic load management data, a genetic algorithm and a simulated annealing algorithm are used, and are both well known optimization algorithms, a global optimal solution can be found in a complex search space, the genetic algorithm optimizes a solution of a problem by simulating natural selection and a genetic mechanism, the simulated annealing algorithm avoids local optimization by simulating a cooling process in a metal annealing process, the application of the optimization algorithms enables the load balancing strategy to be finer and more effective, and a finally generated load optimization report provides an optimal load balancing scheme for the battery pack.
Referring to fig. 10, the system integration module includes a function integration sub-module, a system test sub-module, and an optimization adjustment sub-module;
the function integration submodule integrates system functions by adopting a service guide architecture and a middleware technology based on a load optimization report, and realizes inter-module communication by applying an interface definition language to generate function integration data;
the system test submodule applies an automatic test framework and a load test tool to test performance and stability based on the function integration data, and generates a system test report;
and the optimization adjustment submodule performs optimization adjustment on the system by adopting a continuous integration and continuous deployment flow based on the system test report, and generates a system running state.
The function integration submodule is based on a load optimization report, the module adopts a service guide architecture and a middleware technology to realize high-efficiency integration of each function module of the system, the service guide architecture provides a flexible and extensible method for integrating scattered system functions, the middleware technology is used as a communication bridge between different modules to ensure smooth transmission of data and instructions, the modules also apply interface definition languages to standardize communication protocols between the modules so as to ensure seamless collaborative work of the different modules, and the series of integration steps generate function integration data to provide a solid foundation for unified operation of the system; the system testing submodule comprehensively tests the performance and stability of the system based on the functional integration data by using an automatic testing framework and a load testing tool, wherein the automatic testing framework can rapidly and efficiently execute a series of complex test cases, the load testing tool is used for simulating the running state of the system under high load so as to evaluate the performance of the system under extreme conditions, the tests ensure the reliability and stability of the system under various running conditions, and finally, the generated system testing report provides detailed performance evaluation and potential problem identification; the optimization adjustment sub-module performs further optimization and adjustment of the system according to the system test report, adopts a continuous integration and continuous deployment process, and continuously integrates new improvement and update into the system, and simultaneously keeps the stable operation of the system.
Referring to fig. 11, the user interaction module includes an interface design sub-module, a user feedback sub-module, and a system update sub-module;
the interface design submodule designs an operation interface based on the system running state by adopting a man-machine interaction principle and a user experience design method, and applies a prototype design tool to perform interface prototype manufacture so as to generate interface prototype design;
the user feedback sub-module collects user feedback on an interface based on interface prototype design by using A/B test and user satisfaction survey, and applies quantitative analysis technology to carry out feedback arrangement to generate a user feedback analysis report;
the system updating submodule adopts an agile development method and design thinking to carry out iterative optimization on the interface based on the user feedback analysis report, and generates an optimized operation interface.
The interface design sub-module adopts a man-machine interaction principle and a user experience design method to design an operation interface. In this process, the sub-module focuses on creating an interface that is intuitive, easy to operate, and meets user requirements, using modern prototyping tools, such as sktech or AdobeXD, to quickly build and iterate an interface prototype. In the design process, the intuition and usability of a user are emphasized, each element of the interface is ensured to be carefully designed for improving the user efficiency and satisfaction, the generated interface prototype design not only intuitively shows the operation flow, but also gives consideration to the beauty and the practicability, the user feedback sub-module is based on the interface prototype design, the feedback and opinion of the user on the interface are collected by using methods such as A/B test and user satisfaction investigation, the A/B test allows the sub-module to compare the effectiveness of different interface designs, the user satisfaction investigation directly obtains the subjective evaluation and suggestion of the user, the collected feedback is processed and analyzed through a quantitative analysis technology, the main trend and the user requirement of the feedback are identified by using a statistical method, and the generated user feedback analysis report reveals the user preference and the potential improvement point of the operation interface in detail through the process; the system updating submodule carries out continuous iterative optimization on the operation interface based on the user feedback analysis report by adopting an agile development method and design thinking, and the agile development method enables the interface design process to be more flexible and quick in response, allows the improvement to be realized quickly and meets the change of the user demand. Design thinking ensures that innovative and user-centric thinking approaches run through the whole optimization process. By integrating the methods, the submodules can effectively realize continuous improvement and updating of the interface, and the finally generated optimized operation interface not only improves the user experience, but also improves the overall usability and satisfaction of the system.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (10)
1. An active equalization battery management system of a lead-acid battery pack, which is characterized in that: the system comprises an active balance management module, a Bluetooth communication module, an intelligent prediction module, a data mining module, a health monitoring module, a maintenance decision support module, a load balance control module, a system integration module and a user interaction module;
the active equalization management module is used for adjusting voltage and current by adopting a PID control algorithm based on the lead-acid battery pack real-time monitoring data, and detecting the battery state by combining a Kalman filter to generate battery state data;
the Bluetooth communication module realizes data synchronization by using a Bluetooth low-energy communication protocol based on battery state data, and performs data encryption by applying AES encryption algorithm to generate encrypted communication data;
The intelligent prediction module adopts a long-term and short-term memory network algorithm to conduct deep learning prediction of the battery state based on the encrypted communication data, and conducts model optimization processing to generate a prediction report;
the data mining module performs data mining by using a principal component analysis and clustering algorithm based on the prediction report, and presents a result through a data visualization technology to generate a mining analysis report;
the health monitoring module is used for carrying out battery health assessment by adopting fault tree analysis and fuzzy logic based on the mining analysis report, and carrying out maintenance decision logic to generate a health condition report;
the maintenance decision support module is used for making a battery maintenance strategy based on the health status report and combining historical data with a decision tree algorithm to generate a maintenance scheme;
the load balancing control module is based on a maintenance scheme, applies a load balancing algorithm to optimally allocate the load of the battery pack, and generates a load optimization report through a performance monitoring adjustment strategy;
the system integration module integrates all parts of the integrated system by utilizing a middleware technology and an API based on the load optimization report, and performs system-level performance test to generate a system running state;
the user interaction module develops an operation interface based on the running state of the system by adopting a user experience design principle, and optimizes by combining user feedback to generate an optimized operation interface;
The battery state data specifically comprise voltage and charge and discharge states of battery cells, the encrypted communication data comprise battery state information and operation logs, the prediction report comprises battery performance and expected service life, the mining analysis report specifically comprises a battery use mode and a performance degradation mode, the health status report comprises a current state of the battery and maintenance advice, the maintenance advice comprises maintenance steps and precautions, the load optimization report comprises load distribution and optimization results, the system operation state specifically comprises operation efficiency and performance indexes of the system, and the optimization operation interface provides data display, control operation and user feedback channels.
2. The lead acid battery active equalization battery management system of claim 1, wherein: the active equalization management module comprises a voltage detection sub-module, a current regulation sub-module and a capacitance control sub-module;
the voltage detection submodule is used for detecting the voltage of the battery cell by adopting an analog-digital conversion technology based on the lead-acid battery pack real-time monitoring data, removing noise through a high-pass filter and generating voltage detection data;
the regulating submodule regulates voltage and current by adopting a proportional-integral-derivative control algorithm based on voltage detection data, and stably outputs the voltage and current by utilizing a closed-loop feedback mechanism to generate regulated voltage and current data;
The capacitance control submodule detects and analyzes the battery state based on the regulated voltage and current data by combining a Kalman filter technology, optimizes the detection by applying a multivariate data analysis technology and generates battery state data.
3. The lead acid battery active equalization battery management system of claim 1, wherein: the Bluetooth communication module comprises a data transmission sub-module, a signal encryption sub-module and an interface compatible sub-module;
the data transmission sub-module uses a Bluetooth low energy communication protocol to carry out data transmission based on battery state data, and applies a data packet and recombination technology to verify the integrity of transmission and generate data in transmission;
the signal encryption submodule encrypts data by applying an AES encryption algorithm based on the data in transmission and combines key management encryption security to generate encrypted data;
the interface compatible submodule carries out interface protocol conversion and data format standardization processing based on the encrypted data and is compatible with an external system to generate encrypted communication data.
4. The lead acid battery active equalization battery management system of claim 1, wherein: the intelligent prediction module comprises a data preprocessing sub-module, a characteristic engineering sub-module and a model optimization sub-module;
The data preprocessing sub-module performs data normalization and outlier processing based on the encrypted communication data, and generates preprocessing data by using a characteristic scaling technology;
the feature engineering submodule performs data feature optimization by applying an automatic feature selection technology based on the preprocessing data to generate optimized feature data;
and the model optimization submodule adopts a long-short-term memory network algorithm to conduct deep learning prediction of the battery state based on the optimization feature data, and uses grid search and cross verification to conduct model parameter optimization to generate a prediction report.
5. The lead acid battery active equalization battery management system of claim 1, wherein: the data mining module comprises a data integration sub-module, an algorithm implementation sub-module and a visual display sub-module;
the data integration sub-module performs data integration by adopting an ETL flow based on a prediction report, and cleans, converts and aggregates data from multiple sources to generate a comprehensive data set;
the algorithm implementation submodule carries out pattern recognition and data classification by using a principal component analysis and K-means clustering algorithm based on the comprehensive data set, evaluates data distribution by a probability density function and generates mining result data;
The visual display submodule displays a data mode by adopting an interactive data visual technology comprising a dynamic scatter diagram and a hierarchical clustering tree diagram based on mining result data, and generates a mining analysis report.
6. The lead acid battery active equalization battery management system of claim 1, wherein: the health monitoring module comprises a real-time monitoring sub-module, a fault prediction sub-module and an evaluation report generation sub-module;
the real-time monitoring sub-module is used for monitoring the battery state based on the mining analysis report by adopting a sensor network and a real-time data stream processing technology, collecting and analyzing performance indexes and generating real-time monitoring data;
the fault prediction sub-module predicts and analyzes the battery fault by using a fault tree analysis and a fuzzy logic algorithm based on real-time monitoring data, and combines statistical risk assessment to generate a fault prediction result;
and the evaluation report generation sub-module comprehensively analyzes the health state of the battery based on the fault prediction result, and generates a health condition report by applying an intelligent decision support system.
7. The lead acid battery active equalization battery management system of claim 1, wherein: the maintenance decision support module comprises a knowledge base construction sub-module, a data analysis sub-module and a recommendation system sub-module;
The knowledge base construction sub-module constructs and updates an expert system knowledge base maintained by the battery based on the health status report, integrates historical maintenance data and pattern analysis, and generates a maintenance knowledge data set;
the data analysis sub-module analyzes a battery maintenance mode based on a maintenance knowledge data set by applying association rule mining and random forests to generate maintenance strategy suggestions;
the recommendation system submodule adopts a decision tree and a collaborative filtering recommendation algorithm to formulate a maintenance strategy based on maintenance strategy suggestion and combines battery performance data and historical maintenance cases to generate a maintenance scheme.
8. The lead acid battery active equalization battery management system of claim 1, wherein: the load balance control module comprises a strategy generation sub-module, a dynamic management sub-module and a performance optimization sub-module;
the strategy generation submodule adopts fuzzy logic control and self-adaptive control algorithm to design a load balancing strategy corresponding to multiple battery states based on a maintenance scheme, and generates a load control strategy;
the dynamic management submodule is used for adjusting the load of the battery pack in real time by applying a dynamic programming algorithm based on a load control strategy, and monitoring the battery state in real time by combining a state estimation algorithm to generate dynamic load management data;
And the performance optimization submodule optimizes the load balancing strategy by using a genetic algorithm and a simulated annealing algorithm based on dynamic load management data to generate a load optimization report.
9. The lead acid battery active equalization battery management system of claim 1, wherein: the system integration module comprises a function integration sub-module, a system test sub-module and an optimization adjustment sub-module;
the function integration submodule integrates system functions by adopting a service guide architecture and a middleware technology based on a load optimization report, and realizes inter-module communication by applying an interface definition language to generate function integration data;
the system test submodule is used for performing performance and stability tests by applying an automatic test framework and a load test tool based on the function integration data to generate a system test report;
and the optimization adjustment submodule performs optimization adjustment on the system by adopting a continuous integration and continuous deployment process based on the system test report, and generates a system running state.
10. The lead acid battery active equalization battery management system of claim 1, wherein: the user interaction module comprises an interface design sub-module, a user feedback sub-module and a system updating sub-module;
The interface design submodule designs an operation interface based on the system running state by adopting a man-machine interaction principle and a user experience design method, and applies a prototype design tool to perform interface prototype manufacture so as to generate an interface prototype design;
the user feedback submodule collects user feedback of an interface based on interface prototype design by using A/B test and user satisfaction investigation, and applies quantitative analysis technology to carry out feedback arrangement to generate a user feedback analysis report;
the system updating submodule carries out iterative optimization on the interface by adopting an agile development method and design thinking based on a user feedback analysis report to generate an optimized operation interface.
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