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<p>Lithium based battery gives huge exposure in Battery Electric Vehicle in recent decade. The main dis-appointment in the field of Pure Electric Vehicle is range estimation. Many researcher work on range maximization of Li-ion Battery (LIB), the range of LIB is affected due to following reason: Initial SOC (State of charge), chemistry used in cell, temperature, road condition, driving performances and behavior. Main objective of this paper to use of adaptive algorithm for estimate accurate SOC and terminal voltage (E<sub>t</sub>) of battery pack. Here we use online available experiment battery data for Evaluation: Turnigy Graphene for three drive cycle HWFET, LA92 and US06 for 25&#xa0;℃. This experimental data used to find estimated SOC, terminal voltage and error estimation for above drive cycle and temperature range. Compare the result using Extended Kalman filter, Support Vector Machine (SVM), RNN-LSTM and Hybrid approach to estimate SOC error and terminal voltage error. The result shows that for error is minimized using data driven model, SOC estimation error is decrease 33.69%, 55.06% and 62.71% Second, Voltage error also decrease with respect to EKF model-based estimation method is 1.88%, 3.12% and 20.04% for the drive cycle of HWFET, LA92 and US06 respectively.</p>

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Deep learning based state of charge estimation of lithium ion battery for electric vehicle application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Arvind S. Pande,
  • Ankit Kumar Sharma,
  • Bhanu Pratap Soni

摘要

Lithium based battery gives huge exposure in Battery Electric Vehicle in recent decade. The main dis-appointment in the field of Pure Electric Vehicle is range estimation. Many researcher work on range maximization of Li-ion Battery (LIB), the range of LIB is affected due to following reason: Initial SOC (State of charge), chemistry used in cell, temperature, road condition, driving performances and behavior. Main objective of this paper to use of adaptive algorithm for estimate accurate SOC and terminal voltage (Et) of battery pack. Here we use online available experiment battery data for Evaluation: Turnigy Graphene for three drive cycle HWFET, LA92 and US06 for 25 ℃. This experimental data used to find estimated SOC, terminal voltage and error estimation for above drive cycle and temperature range. Compare the result using Extended Kalman filter, Support Vector Machine (SVM), RNN-LSTM and Hybrid approach to estimate SOC error and terminal voltage error. The result shows that for error is minimized using data driven model, SOC estimation error is decrease 33.69%, 55.06% and 62.71% Second, Voltage error also decrease with respect to EKF model-based estimation method is 1.88%, 3.12% and 20.04% for the drive cycle of HWFET, LA92 and US06 respectively.