Research on the Prediction of Electric Vehicle Range Based on an Improved Neural Network Algorithm

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Mechanical Engineering and Design

Mechanical Engineering and Design is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of mechanical engineering theory and technology application, on the combination of mechanical theory and modern industrial technology. The main focus of the journal is the academic papers and comments of latest mechanical engineering design theory research improvement in the fields of nature science, engin... [More] Mechanical Engineering and Design is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of mechanical engineering theory and technology application, on the combination of mechanical theory and modern industrial technology. The main focus of the journal is the academic papers and comments of latest mechanical engineering design theory research improvement in the fields of nature science, engineering technology, economy and science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of mechanical engineering technology theory for professionals, scholars and researchers in this field, reflecting the academic front level, promote academic change and foster the rapid expansion of mechanical manufacture, mechanical technology theory and technology research.

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Paper Infomation

Research on the Prediction of Electric Vehicle Range Based on an Improved Neural Network Algorithm

Full Text(PDF, 685KB)

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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