State of charge (SOC) estimation in electric vehicle (EV) battery management systems using ensemble methods and neural networks

Battery management systems (BMS) are critical in ensuring the performance, reliability, and safety of battery systems through accurate estimation of the State of Charge (SOC) of batteries. As on-board SOC estimation, together with other functionalities by the BMS can result in its high design complexity, high cost, and high energy consumption, this study explores a data-driven estimation of a Lithium battery state of charge (SOC) while discharging, using simple linear regression, ensemble methods, and neural networks respectively to ensure an accurate low time complexity solution as compared to existing methods. A known dataset of 835,248 records from Li [NiMnCo]O2 (H-NMC)/Graphite + SiO battery was used to…

• LB
  Leeds Beckett University
• SO
  School of Computing, Engineering and Built Environment, Glasgow Caledonian University