Aiming at the uncertainty of data collected in multi-sensor networks, a multi-sensor fusion technique based on the PSO algorithm is suggested to optimize the RBF neural network with the aim of reducing the uncertainty of data gathered in multi-sensor networks. The RBF neural network's weight and threshold parameters are modeled as moving particles, with vectors used to describe their positions. The PSO algorithm chooses the proper values for the parameters. The ideal parameter values of the RBF neural network are ultimately established following iterative training. It has been demonstrated that the PSO algorithm-based RBF neural network multi-sensor data fusion algorithm has higher fusion accuracy and shorter running times.
In order to meet the working requirements of power lithium battery energy storage system and provide the necessary basis for energy conversion and distribution, it is imperative for battery management system (BMS) to accurately estimate state of charge (SOC) of battery pack. This paper systematically shows the significance and basic theory of SOC estimation, summarizes and compares advantages and disadvantages of existing methods such as open circuit voltage method, support vector regression and system filtering method, even puts forward the development trend of SOC estimation methods in the future.
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