Battery SOC Estimation Method Based on BP Neural Network Optimized by Genetic Algorithm

The state of charge (SOC) of battery is an important indicator of battery condition assessment. Accurate SOC estimation can reflect the real-time capacity of battery and effectively extend the service life of battery. In this paper, aiming at the problem that the error function of BP neural network is easy to fall into local optimum and the effect of initial weight and threshold reverse iterative updating is limited, a neural network optimized by genetic algorithm is proposed to improve the estimation accuracy and global fast optimization. By studying the equivalent first-order RC model of the battery and building the simscape simulation model to simulate the charging and discharging process of the battery, the parameters of the battery terminal voltage, current, internal resistance, temperature and battery SOC are obtained. After the data is processed, the voltage, current, internal resistance and temperature are taken as the factors of battery SOC estimation. The experimental results show that the neural network optimized by genetic algorithm can obtain the optimal initial threshold and weight globally, thus establishing a high-precision SOC estimation model of battery, avoiding local optimization and improving the estimation accuracy of battery.

• Record URL:
  https://doi.org/10.1007/978-981-16-9913-9
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811699122
• Supplemental Notes:
  • © Beijing Oriental Sun Cult. Comm. CO Ltd 2022.
• Corporate Authors:

  Springer Singapore

  ,    
• Authors:
  • Ma, Xingyuan
  • Liu, Yang
  • Li, Chenxu
  • Tang, Geng
  • Zhang, Gang
  • Qiu, Ruichang
• Conference:
  • The 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation 2021 (EITRT 2021)
  • Location: Qingdao , China
  • Date: 2021-10-22 to 2021-10-24
• Publication Date: 2022

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 45-56
• Monograph Title: Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021: Rail Transportation System Safety and Maintenance Technologies
• Serial:
  • Lecture Notes in Electrical Engineering
  • Volume: 868
  • Publisher: Springer
  • ISSN: 1876-1100

Subject/Index Terms

• TRT Terms: Condition surveys; Electric batteries; Estimating; Optimization; Service life
• Subject Areas: Energy; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01891908
• Record Type: Publication
• ISBN: 9789811699122
• Files: TRIS
• Created Date: Aug 30 2023 9:11AM