The purpose is to ensure the big data acquisition of parallel battery back state, and the safe and effective operation of the energy management system. Edge devices are combined with cloud computing to achieve a big data acquisition and processing model based on the edge computing, which makes the speed of the big data acquisition of parallel battery back state faster, and avoids data fitting. The algorithm is optimised based on the combination of the Lyapunov method and the distributed method of ADMM (Alternating Direction Multipliers Method). The optimised edge computing improves the performance of the energy management system of parallel battery back state. The experimental results show that the two methods can effectively avoid the fitting phenomenon of data acquisition, and the distributed method can simplify the complexity of data processing and make the energy management system consume minimum energy. The big data acquisition speed of parallel battery back state based on the improved edge computing is faster, and the battery energy management is more effective, which has enormous significance for enlarging the application of parallel battery.
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