4872 results about "Battery management systems" patented technology

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention discloses a BMS-based multifunctional integrated monitoring and fire control method and system, and the method comprises the steps: synchronously collecting the temperature, combustible gas concentration and dynamic environment monitoring data in a container through a BMU battery box management unit of a BMS, and generating a space-time correlated container environment state matrix; based on the environment state matrix, extracting temperature anomaly features, gas concentration gradient features and dynamic environment fluctuation features through a multi-modal feature fusion model, and constructing a multi-dimensional risk feature vector; inputting the multi-dimensional risk feature vector into a fire-fighting early warning optimization algorithm to generate a graded fire-fighting early warning signal; and according to the graded fire-fighting early warning signal, an active fire-fighting intervention strategy is triggered through a BMU unit of the BMS, a fire extinguishing device, a ventilation system and a power cut-off module are dynamically controlled, and a fire-fighting response path is synchronously optimized to be matched with the current risk grade. According to the embodiment of the invention, graded fire-fighting early warning can be realized, and the accuracy and timeliness of fire early warning are improved.

The invention relates to the technical field of battery management systems, in particular to a distributed battery state estimation and compensation method based on voltage and current sampling, which comprises the following steps: constructing an original characteristic matrix by synchronously acquiring voltage and current waveforms and extracting ripple characteristics and slope abrupt change points, and generating a characteristic tensor with environment correction through a coupling compensator; then constructing a state parameter space based on adaptive particle injection and an electrochemical-thermodynamic coupling model; adopting a multi-source data fusion engine to realize distributed collaborative estimation of a health state index and a charge state confidence interval, and completing SOC, SOH and temperature trend prediction and residual compensation correction through a multi-state dynamic prediction model; and based on the prediction result and the uncertainty boundary, constructing an energy compensation optimization model, and searching a Pareto optimal compensation strategy. The method can be widely applied to intelligent management and control in the fields of battery energy storage systems and electric power equipment.

The invention discloses a multi-level feature fusion and time sequence modeling combined lithium battery life prediction method, and relates to the technical field of lithium ion battery health management and life prediction. The method comprises the steps of receiving time sequence observation data in a battery operation process, inputting the time sequence observation data into a pre-constructed local feature extraction model, and introducing a one-dimensional convolutional neural network into the local feature extraction model to perform feature extraction on the time sequence observation data to obtain local feature representation. According to the method, three structures of local feature extraction, global context modeling and bidirectional time sequence modeling are fused, and the battery degradation modeling capability and prediction precision are effectively improved. The TFN adopts an end-to-end architecture design, has good feature perception capability and time-dependent modeling capability, and can adapt to various degradation modes and complex time sequence environments. The method is suitable for life evaluation and health state monitoring in an intelligent battery management system, and has relatively high practical value and popularization prospect.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

The invention relates to the technical field of power equipment inspection, and discloses a transmission, transformation and distribution equipment inspection system and method based on an unmanned aerial vehicle, and the system comprises the following specific components: an unmanned aerial vehicle state monitoring module which is connected with a battery management system, is provided with a pressure sensor, and is connected with a flight control system and various sensors; the unmanned aerial vehicle power, load and flight speed parameters are monitored in real time, tasks are dynamically and intelligently distributed and scheduled through deep fusion of multi-dimensional information such as unmanned aerial vehicle states, task attributes and environmental conditions, it is ensured that each unmanned aerial vehicle can execute the best matching task at the best opportunity and the most appropriate place, resource waste and potential risks are avoided, and the unmanned aerial vehicle task scheduling method and system are suitable for large-scale popularization and application. And meanwhile, task allocation is performed according to the states of the unmanned aerial vehicles and task requirements, the resources of each unmanned aerial vehicle are fully utilized, the service life of each unmanned aerial vehicle is prolonged, the inspection cost is reduced, and the resource utilization efficiency is improved.

The invention discloses a ternary internet-of-things battery management system, particularly relates to the technical field of lithium battery management, realizes lithium battery thermal safety closed-loop management through multi-sensor fusion, and comprises the following steps: deploying a digital sensor array to obtain time-space associated data; constructing a spatio-temporal feature vector including fusion of a local temperature gradient, a neighborhood temperature gradient and a stress coupling ratio; a density clustering algorithm is adopted to dynamically divide spontaneous heating, radiant heat and stress coupling clusters, and cluster boundaries are updated in real time; calculating a dual thermal coupling coefficient based on a clustering result, and eliminating the interference of temperature reading through a dual temperature compensation formula; a three-level alarm mechanism of a radiant heat proportion, a stress coupling degree and a compensation error is established, a side gateway is linked to realize parameter adaptive optimization, and the thermal runaway early warning precision and the structural failure detection rate are improved; strain monitoring resource optimization is realized through physically-driven key point screening and dynamic graph neural network prediction.

The invention provides a marine high-power container type charging station, which belongs to the technical field of marine charging stations, and realizes a nine-step charging control process by integrating an electric energy stability regulation module through a control chip: firstly, receiving power grid monitoring data, calculating an evaluation index, and constructing a prediction model to generate an early warning signal; adjusting the power output of the electric pile assembly according to the early warning signal; receiving data of a ship battery management system and calculating an optimal charging curve; adjusting charging parameters based on the temperature data and the thermal diffusion equation; the harmonic content is calculated in real time, and harmonic waves are suppressed through a fluctuation compensation model; monitoring a power factor and executing reactive compensation; calculating multi-path charging power distribution based on the battery optimization model; and the charging power change rate is adjusted when power grid frequency fluctuation is detected. According to the system, a multilayer convolutional neural network and a deep reinforcement learning technology are fused, and stable and efficient charging in a complex power grid environment is realized through adaptive weight adjustment.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention relates to the technical field of battery management systems, and particularly discloses a multi-dimensional parameter fused multi-battery-pack parallel dynamic balance control method and system, and the system comprises a multi-dimensional space-time parameter deep fusion module, an aging driving parameter weight self-adaption unit, a twin closed-loop strategy iteration system and a battery pack dynamic balance execution layer. Utilizing the state data output by multi-dimensional parameter fusion to drive aging weight adjustment; a weight strategy is verified and optimized through a twin system, and then feedback parameter fusion and weight adjustment are performed, so that the system continuously adapts to the battery state change. Multi-dimensional parameters such as voltage, current, temperature, internal resistance, SOC, SOH and the like are collected, time-space correlation characteristics are constructed in combination with timestamps and spatial position information, electrochemical, thermal and life states and time-space scene correlation of the battery are comprehensively covered through preprocessing and time-space attention mechanism neural network fusion, the one-sidedness of traditional single parameter evaluation is solved, and the evaluation accuracy is improved. Potential imbalance risks are identified in advance.

The invention discloses a lithium ion battery health prediction system and method based on microscopic image multi-scale and region adaptive fusion. The prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a multi-scale feature fusion module, a health state prediction module and a data enhancement and optimization module. The method overcomes the limitation based on traditional electric signal analysis, is suitable for various microscopic imaging technologies, enables the battery health state prediction to be directly evaluated based on the change of the internal microstructure of the electrode, and improves the prediction precision, applicability and physical interpretation. The method can be widely applied to battery life prediction, battery management system optimization and intelligent monitoring systems, and provides reliable technical support for battery safety management, performance optimization and industrial intelligence.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.

The invention discloses an intelligent charging and discharging management method and system for a lithium battery, and the method comprises the steps: collecting multi-dimensional parameter data of the lithium battery, and generating a data set with a timestamp; and analyzing the data features based on the battery type classification model, and determining a battery type identifier. And utilizing the recurrent neural network to model the relevance between the capacity attenuation and the health state, and predicting the residual capacity and the health score. If the capacity or health score is lower than the threshold value, extracting the environment temperature and the load demand to generate a temperature-load feature vector; and matching the candidate strategy set from the pre-established index database through the hash table. And distributing weights according to health scores and load demands, sorting strategy efficiency and life influences by adopting a linear regression model, and screening an optimal strategy. And if the strategy calculation complexity exceeds the equipment capability, iteratively optimizing parameters by utilizing a genetic algorithm, simplifying a strategy instruction, generating a charging current, a voltage curve and a discharging rate control instruction, and executing the charging current, the voltage curve and the discharging rate control instruction in real time by a battery management system. The method prolongs the service life of the battery and improves the charge-discharge efficiency.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The embodiment of the invention provides a battery system discharge control method, a battery system, a power utilization device and a battery management system. The possibility of over-discharge of a battery in the discharge process can be reduced. The battery system comprises a first battery and a second battery, the battery system comprises independently arranged energy areas, the first battery and the second battery are arranged in different energy areas, and the method comprises the following steps: acquiring a first state parameter and a second state parameter; determining first allowable discharge power of the first battery according to the first state parameter, and determining second allowable discharge power of the second battery according to the second state parameter; and determining target allowable discharge power of the battery system according to the first allowable discharge power and the second allowable discharge power, and controlling the battery system to discharge according to the target allowable discharge power.

The invention discloses a real vehicle power battery capacity attenuation trajectory prediction method based on a de-noising diffusion model. According to the method, a diffusion generation model is applied to the field of battery capacity prediction for the first time, multi-modal features are constructed for charging and discharging data under a real vehicle working condition, feature expressions are extracted through Transform and CNN, and the features are integrated into high-dimensional embedded vectors. And then, a DDPM model based on a ContextUnet architecture is utilized to learn a dynamic change rule of capacity attenuation in a noise environment through a noise diffusion and reverse denoising process under the guidance of multi-modal features, so that high-precision capacity prediction is realized. The training process is further combined with an implicit denoising mechanism of DDIM, and the reasoning speed is increased while the prediction quality is improved. Finally, a capacity attenuation track confidence interval is obtained by performing multiple times of prediction sampling calculation on a predicted capacity attenuation result, and the capacity attenuation track confidence interval can be used for deducing key indexes such as the state of health (SOH) and the remaining service life (RUL) of the battery. The method has the characteristics of high prediction precision and strong adaptability, obviously enhances the intelligent level of the battery management system, and has important practical value for health assessment and life management of the new energy automobile battery.

The invention discloses a wireless communication battery management system with a signal adaptive adjustment function, and relates to the field of wireless communication networks. The signal monitoring unit is used for monitoring parameter data of wireless signals in real time and transmitting the parameter data to the signal processing unit, and the signal processing unit comprises a multi-band antenna and a signal acquisition circuit; and the signal processing unit is connected to the signal monitoring unit and is used for receiving the parameter data of the wireless signal transmitted by the signal monitoring unit and carrying out filtering, noise reduction and enhancement processing on the signal through a preset signal processing algorithm. The distributed multi-band antenna design is combined with the automatic gain control technology, the signal receiving range is expanded, blind areas are reduced, the stability of signal collection in a complex environment is improved, signal multi-scale noise reduction is achieved through the wavelet transform algorithm, the signal quality trend is pre-judged in advance in combination with machine learning prediction, and the signal quality is improved. And the control unit is supported to pre-adjust the transmitting power and switch to a high-quality frequency band, so that the probability of communication interruption is reduced.

Online electrochemical impedance spectroscopy (EIS) circuitry and methods. For mobile installations, such as in a hybrid or electric vehicle, battery management units or battery management systems having EIS circuits can provide additional data regarding the state, health and potential failure of batteries. New topologies for the EIS circuitry are proposed, allowing voltage controlled and current controlled EIS to be performed.

The invention provides a marine box type power supply battery management system, which belongs to the technical field of marine power supplies, and comprises a control chip, a battery pack management unit (BMU), a battery cluster management unit (BCMU), a battery stack management unit (BSMU) and a related monitoring device, multi-dimensional battery state data is acquired by adopting a time window segmented acquisition method, a battery working state feature vector is extracted by utilizing a battery state evaluation function, battery parameters are calculated based on a depth time sequence analysis model, error compensation parameters are generated, and battery output errors are corrected in real time through a voltage and temperature combined compensation mechanism. And calling an equalization strategy optimization function to determine a multi-stage equalization control scheme, and predicting and compensating an error generated in an equalization operation instruction sequence execution process by using a pre-trained battery parameter error model, thereby solving the core technical problem that the marine box-type power supply battery management system cannot adapt to battery parameter changes in a complex water area environment.

The invention relates to the technical field of artificial intelligence and new energy management, in particular to a battery pack intelligent health management method, device and equipment based on a dynamic space-time diagram neural network and a computer storage medium. According to the battery pack intelligent health management method based on the dynamic time-space diagram neural network, multi-source and multi-mode data in the operation process of the battery pack are fully fused, a dynamically evolved diagram structure is constructed to describe a complex coupling relation and a degradation propagation path between battery cells, a GRU mechanism is introduced to model state evolution characteristics in a time dimension, and the battery pack intelligent health management method based on the dynamic time-space diagram neural network is obtained. The method adapts to different battery working conditions through a'pre-training-fine tuning 'migration strategy, and is finally deployed in a battery management system (BMS) to realize online health monitoring and intelligent fault early warning. The method effectively improves the precision, stability and generalization ability of health assessment of the battery pack, and overcomes the technical bottlenecks that a traditional method cannot dynamically sense the state evolution of the battery cell and is difficult to adapt to a complex operation environment.

The invention discloses a power domain controller system, which relates to the technical field of vehicle design and research and development and comprises a domain control board, a power module and a high-voltage power distribution part, the domain control board comprises a vehicle control unit, a heat management controller, a battery management system, a gear shifting controller and a power distribution control unit, wherein the heat management controller, the battery management system, the gear shifting controller and the power distribution control unit are electrically connected with the vehicle control unit. The heat management controller is electrically connected with the power distribution control unit. The power module comprises a slow charging motor, a DCDC converter and a relay, the slow charging motor is electrically connected with the battery management system, and the DCDC converter is electrically connected with the gear shifting controller; and the high-voltage power distribution part comprises a copper bar, a relay and a fuse. Through multi-module integration, the number of physical controllers and wiring harness complexity are reduced, the system development and maintenance cost is remarkably reduced, and meanwhile platform expansion is facilitated.

The present application relates to a digital twin device and a digital twin-based battery temperature monitoring method. The digital twin-based battery temperature monitoring method, according to one embodiment of the present invention, may comprise the steps of: receiving real-time state information of a battery unit from a battery management system (BMS); carrying out a temperature distribution analysis of the inside of the battery unit by applying the real-time state information to a digital twin corresponding to the battery unit; and transmitting, to the BMS, a virtual temperature value of a virtual point of measurement, which has been requested for by the BMS.

A battery management system includes a battery pack and a controller in communication with the battery pack and a packing actuator of the refuse vehicle. The controller is configured to receive a state of charge of the battery pack, determine if the state of charge satisfies a lower threshold, in response to determining that the state of charge satisfies the lower threshold, prevent operation of the packing actuator, determine if the state of charge satisfies an upper threshold, and in response to determining that the state of charge satisfies the upper threshold, initiate operation of the packing actuator.

Techniques are described for using machine learning to extract physics-based cell model parameters from battery charge data provided by the battery module of the battery management system. This data may be processed via machine learning techniques in order to periodically update or adapt a physics-based model that may capture cell electrical dynamics and degradation mechanisms. The techniques may utilize a “digital twin” to adaptively charge the battery while preserving the life of the cells.

The invention discloses a battery change cabinet battery abnormity detection method and system based on a thermal image, and relates to the related field of battery safety monitoring, and the method comprises the steps: collecting a surface thermal image sequence in a battery compartment through an infrared thermal imager arranged in a battery change cabinet, and enabling the surface thermal image sequence to have a time sequence and a charging stage label; extracting temperature field features of a space-time dimension from the thermal image sequence, and identifying an abnormal state and an abnormal region of the battery based on the temperature field features; and acquiring real-time battery management system (BMS) data of the battery, performing correlation verification on the abnormal state or the abnormal area and the BMS data, and determining an abnormal level and positioning a fault battery unit according to a correlation verification result. According to the invention, the technical problems of inaccurate and untimely detection and inaccurate fault positioning in the existing battery abnormality detection of the battery changing cabinet are solved, and the technical effects of improving the accuracy, timeliness and positioning accuracy of battery abnormality detection are achieved.

The invention discloses an electric motorcycle lithium ion battery management system and method, and relates to the technical field of electric motorcycle battery management, the system comprises the following components: a data acquisition module, a digital twin model construction module, a simulation analysis module and a strategy making module; by integrating the data acquisition module, the digital twinborn model construction module, the simulation analysis module, the strategy making module and the like, the system can acquire battery operation data in real time, a digital model consistent with the state of an entity battery is constructed in a virtual space, and the system can simulate the performance change of the battery under different working conditions and environments by utilizing the model, so that the performance of the battery is improved. Therefore, the service life attenuation condition of the battery is accurately predicted, accurate battery maintenance and replacement strategy suggestions are provided for users, and the accuracy and efficiency of battery management are remarkably improved.