Open AccessEnergy storage battery SOC estimate based on improved BP neural network
Author(s) -
Xiaojing Liu,
Yawen Dai
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012042
Subject(s) - battery (electricity) , artificial neural network , state of charge , genetic algorithm , computer science , voltage , nonlinear system , energy (signal processing) , control theory (sociology) , engineering , artificial intelligence , power (physics) , machine learning , electrical engineering , control (management) , mathematics , statistics , physics , quantum mechanics
The SOC estimation of the battery is the most significant functions of batteries’ management system, and it is a quantitative evaluation of electric vehicle mileage. Due to complex battery dynamics and environmental conditions, the existing data-driven battery status estimation technology is not able to accurately estimate battery status. Aiming at this problem, the multi-implicit BP neural network model and the error elimination due to genetic algorithm are combined to appraise the battery’s state of charge. Firstly, a multi-hidden layer BP neural network is applied to learn about the nonlinear connection between the battery SOC and the measurable variables of lithium-ion batteries, for instance, current, voltage, and temperature. Secondly, the prediction error of the neural network type is denoised by the genetic method to smooth the prediction results. The method proposed in this paper captures long-term dependencies between measurable variables and battery state. Finally, the improvement effect of the method proposed in this paper is verified by comparison with the traditional neural network method.