Paper Infomation
Research on the Prediction of Electric Vehicle Range Based on an Improved Neural Network Algorithm
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Author: Guoliang Zhang, Yanlliang Zhang, Lei Xue, Shengjian Li, Yang Zhang
Abstract: The driving range of electric vehicles is influenced by various factors such as battery temperature, current, cell voltage, load, driving behavior, remaining battery level, and vehicle speed, and there is a nonlinear relationship between these factors. Traditional Backpropagation (BP) neural network algorithms can be used to train the collected data to obtain a driving range training model. However, the BP algorithm has the drawbacks of being prone to local optima, slow convergence, and difficulty in determining initial weight values. Therefore, it is possible to combine genetic algorithms to optimize the parameters of the BP neural network to compensate for these shortcomings, while also using the collected data to repeatedly train the network to achieve a more accurate driving range training model.
Keywords: Backpropagation Algorithm, Genetic Algorithm, Driving Range, Artificial Neural Network
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