8433 results about "Battery management systems" patented technology

The invention relates to the technical field of battery health state analysis, and discloses a battery health state comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of data collected by a battery management system, and obtaining a standardized data sequence; calculating a first battery characteristic parameter according to the standardized data sequence; performing covariance matrix adaptive Kalman filtering processing on the first battery characteristic parameter to obtain a second battery characteristic parameter; constructing a capacity-internal resistance change rate index and a temperature-efficiency correlation index based on the second battery characteristic parameters to obtain a dual-threshold evaluation matrix; inputting the dual-threshold evaluation matrix into a deep reinforcement learning model, and performing forward propagation calculation and iterative training through an Actor network and a Critic network to obtain a feature fusion weight coefficient; and carrying out weighted calculation on the dual-threshold evaluation matrix to generate a battery health state value and a residual life prediction value, and realizing multi-dimensional evaluation of the battery health state.

The invention relates to the technical field of battery management systems, and particularly discloses a battery signal acquisition precision optimization method of a BMS (Battery Management System), which comprises the following steps of: dynamically adjusting a covariance matrix of process noise and measurement noise by monitoring the voltage, current and temperature of a battery in real time so as to cope with the change of the working state of the battery; according to the change trend of the charge and discharge rate and the temperature, noise parameters are predicted and corrected, and the adaptability of the system in an extreme environment is improved; in combination with data from a plurality of sensors, weighted average and a self-adaptive adjustment mechanism of a Kalman filter are adopted, so that evaluation of the system on signal quality of different sensors and effective suppression of noise are enhanced; aiming at the sudden change of the battery state, the system can be ensured to quickly respond by automatically adjusting the filtering algorithm, the accurate monitoring of the battery health state is ensured, the stability and the accuracy of the battery management system can be improved, and the problem of estimation error under the extreme working condition of a traditional method is solved.

The present application discloses a battery pack for supplying power to a power tool, comprising: a sensor for generating sensor data indicating operation parameters of a battery pack; and an electronic control unit comprising a machine learning program. The electronic control unit is configured to: receive the sensor data, use the machine learning program to process the sensor data, and use the machine learning program to generate an output on the basis of the sensor data, wherein the machine learning program comprises a trained neural network model, and the output at least indicates at least one of a state of charge (SoC), a state of temperature (SoT), a state of health (SoH), and a state of power (SoP) of a battery cell. The effective utilization rate of a battery is improved.

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.

In an example, a method includes receiving, in a super frame, a first downlink on a first channel from a wireless master node at a wireless head node in a WBMS, where the wireless head node is a head node for a sub-cluster of one or more wireless devices. The method includes transmitting, in the super frame, a second downlink on a second channel from the wireless head node to each of the one or more wireless devices. The method includes receiving, in the super frame, an uplink from each of the one or more wireless devices at the wireless head node on the second channel. The method includes transmitting, in the super frame, an aggregated uplink from the wireless head node to the wireless master node on the first channel, where the aggregated uplink includes data from each of the one or more wireless devices.

The invention provides an energy consumption prediction and anomaly detection method and system based on digital twinning, and the method comprises the steps: collecting physical parameters in real time through a sensor, and the physical parameters comprise the cell temperature, the total current of a battery pack, the total voltage of the battery pack, the gradient, the vehicle speed and the mass; collecting battery state parameters by using a battery management system, wherein the battery state parameters comprise real-time energy consumption; the collected physical parameters are input into a Simulink and Cruise combined model for simulation, and model output prediction energy consumption is obtained; wherein the Simulink is used for establishing an equivalent circuit model of a power battery, and the control system, the sensor simulation module and the Cruise simulation model are used for establishing a whole-vehicle simulation model of the pure electric vehicle; comparing the model output energy consumption with the real-time energy consumption, and obtaining an energy consumption deviation value; whether the energy consumption deviation value exceeds a preset range or not is judged, and if yes, energy consumption is judged to be abnormal; and if not, evaluating the energy consumption of the whole vehicle so as to solve the technical problem that the actual energy consumption analysis, energy management and endurance mileage evaluation of the whole vehicle are difficult.

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 relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

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.

A battery management system, a battery pack, an electric vehicle and a battery charging time prediction method are provided. The battery management system includes a memory for storing a plurality of reference charging maps, a sensing unit for detecting voltage, current, and temperature of a battery, and a control unit for determining an estimated SOC value of the battery. Each reference charging map includes first to Mth reference current values and first to Mth reference power curves associated with first to Mth SOC sections. The control unit obtains a mth reference power curve and a mth reference current value associated with a mth SOC section to which the estimated SOC value belongs, from the reference charging map associated with the temperature section to which the temperature detection value of the battery belongs. The control unit determines the time required for the SOC of the battery to reach an end point of the mth SOC section, according to the result of comparing a maximum output power of a charger and the mth reference power curve, based on the difference between the end point of the mth SOC section and the estimated SOC value, and at least one of the mth reference current value, the maximum output power and the mth reference power curve.

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.

According to the power control method and system based on energy storage unit adjustment priority evaluation, an energy storage unit calibrates the charge state, the battery capacity and the health state of the energy storage unit and feeds back the charge state, the battery capacity and the health state to a battery management system; the battery management system verifies a built-in battery state estimation model of the battery management system by using the calibrated charge state and health state; based on the calibrated charge state and the battery capacity, calibrating the energy state range; calibrating the voltage standard deviation coefficient, the voltage range and the temperature range based on the measurement precision of the sensor; taking the weighted sum of each evaluation index score after calibration of the energy storage unit as a comprehensive evaluation score; determining the adjustment priority of the energy storage unit allowed to be adjusted by using the comprehensive evaluation score and the charge-discharge cycle times of the energy storage unit allowed to be adjusted; and selecting the adjustment-allowed energy storage unit with the highest adjustment priority as an execution unit for responding to the scheduling target power instruction, and ensuring the safe and stable operation of the energy storage system at the same time.

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 present application relates to a simulation test method and simulation test platform for an energy-storage battery management system. The method comprises: receiving an energy-storage system real-time simulation result that is sent by an energy-storage system real-time simulation module by means of a high-speed communication protocol; converting a data format of the energy-storage system real-time simulation result into a data format of a target communication protocol, and sending to a battery management control module a target energy-storage system real-time simulation result that is obtained after conversion; receiving a battery management control message that is sent by the battery management control module in response to the target energy-storage system real-time simulation result; and converting a data format of the battery management control message into the data format of the target communication protocol, and sending to the energy-storage system real-time simulation module a target battery management control message that is obtained after conversion, thus making the energy-storage system real-time simulation module perform a test on the battery management control module on the basis of the battery management control message. By using the present method, the simulation test efficiency of an energy-storage battery management system can be 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 provides a multifunctional controller for a new energy automobile and the new energy automobile, and relates to the technical field of new energy automobile control. The multifunctional controller for the new energy automobile comprises a data collection module, a data processing module, a prediction module, a storage system, a communication system, a user interface, a software system, a battery management system, an auxiliary driving system, a safety system, an emotion recognition module, a power supply system and a continuous learning system. Through the prediction model and time sequence analysis, the charging and discharging strategy of the battery is optimized, the service life of the battery is prolonged, the energy efficiency is improved, a power system is optimized according to real-time data and a prediction result, the performance and efficiency of the vehicle are improved, and thermal management of the vehicle is optimized according to weather forecast and real-time traffic information. The optimal performance can be kept under different weather conditions, and the system can automatically adjust the internal environment, such as temperature and music playing, of the vehicle by analyzing the emotional state of the driver so as to adapt to the emotion of the driver.

The invention relates to a temperature control technology of an energy storage system, and discloses a temperature control system and method of a lithium ion battery energy storage system, which can realize accurate temperature control, reduce the power consumption of temperature control and reliably guarantee high-efficiency and long-life operation of the lithium ion battery energy storage system. The temperature control system comprises a battery management system, a heat management system and a battery temperature sampling unit, the battery temperature sampling unit is used for sampling the temperature of each battery cell in the energy storage system in real time; the battery management system is used for generating a temperature control strategy according to a preset temperature feature point in combination with the current temperature data of the battery cell and the current working state of the energy storage system, and issuing the temperature control strategy to the thermal management system; the temperature characteristic point is preset according to characteristic data of a battery cell used in the energy storage system; and the heat management system is used for controlling the temperature of the energy storage system by executing a temperature control strategy. The method is suitable for the energy storage system.

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 discloses a redundant communication implementation device and method of a battery management system. The device comprises a master control unit, a master control unit and a slave control unit, the slave control unit is used for monitoring the battery module and sending monitoring data to the master control unit; the main control unit and the general control unit are connected to form a redundant communication ring network; the master control unit is used for responding to a control instruction or a query instruction of the master control unit, collecting monitoring data of the slave control units and feeding back the monitoring data to the master control unit; the main control unit is used for issuing a battery management control instruction or a query instruction to the main control unit and receiving feedback data of the main control unit; each main control unit comprises a main control board and a Bypass relay, the main control board is connected with the Bypass relay, and the Bypass relay is used for bypassing the main control board when the main control board fails and loses power. According to the device and the method, communication interruption caused by line signal interference can be avoided, and system communication faults caused by damage of the main control board can be prevented.