An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

Abstract:

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

• Periodical: Advanced Materials Research (Volumes 953-954)
• Pages: 800-805
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.953-954.800
• Online since: June 2014
• Authors: Meng Di Liang, Tie Zhou Wu*
• Keywords: Genetic Algorithm-Flexible-Radial Basis Function, Neural Network (NN), Neural Network Structure Optimization, State-of-Charge (SOC) Prediction

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