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Research Paper | Computer Science & Engineering | India | Volume 11 Issue 12, December 2022

Data-Driven Approach for SOC Estimation of Battery using Long-Short Term Memory Network

S. Sai Rahul

Abstract: Lithium-ion batteries have a wide range of applications in many industries but mostly used in electronic devices and electrical vehicles as an energy storing equipment. A proper battery management is necessary for efficient energy storage and usage. As Electric vehicles need to run for longer durations, we need efficient battery management which can be achieved by state of charge estimation. State of charge (SOC) estimation is a key performance indicator for Battery Management system (BMS) hence, an accurate prediction of SOC is required. SOC estimation can help, and guide users to take necessary actions based on battery behaviors to increase its battery life. Non-linear nature and complex chemical reactions in batteries makes it not possible to directly predict or measure a battery's state of charge. This paper proposes a novel method for accurate prediction of SOC percentage of Li-ion battery using Long short term Memory network.

Keywords: State of Charge Estimation, SOC, LSTM, Battery Management System, BMS

Edition: Volume 11 Issue 12, December 2022,

Pages: 481 - 483