Research On Lithium-ion Battery Energy Storage Management System Based On GA-BP Neural Network

In recent years, electric energy storage technology has achieved great progress, lithium-ion batteries are efficient and have high quality, it is widely used in the electric energy storage system, and the management of lithium-ion battery energy storage system has become one of the hotspots in energy storage area. In this paper, we studied a battery management system which is based on a new kind lithium-ion battery of soft carbon anode materials, established a battery SOC forecasting model based on hybrid genetic algorithm and BP neural network, achieves a design and implementation scheme of an electric energy storage and management system.Introduce the development process of lithium-ion batteries briefly and analysis it’s principle and characteristics, gives an overview of the structural features and function advantages of soft carbon anode materials lithium-ion batteries. In the battery state of charge (SOC) prediction aspects, this paper describes the common battery state of charge prediction method and analysis each method’s proper usage scenarios, studied the latest advances and development trend.Discusses the prediction algorithm based on hybrid GA-BP neural network, establish a SOC prediction model for a new kind lithium-ion battery. Combined the advantage of BP neural network in nonlinear prediction area with genetic algorithm’s global optimization capability, use genetic algorithms to optimize neural network’s connection weights and thresholds, describes the algorithm processes and core implementation, import actual test data and performed simulation experiments to verify the performance of the algorithm.Designed a battery management system based on soft carbon anode materials lithium-ion batteries and GA-BP neural network algorithm, including communications module and sampling module, introduce the structure of the management system, build a SOC prediction module based on GA-BP neural network, analysis the design plan of battery voltage and current monitoring, charging and discharging control. SOC prediction, temperature monitoring and control, hazard warning functions, etc.The prediction algorithm this paper studied has high accuracy and the applicability of the forecasting model is good, the design of the battery management system meets the function requirements of energy storage battery management system, it is reliable and with important application value.