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Accurately predicting the state of charge of Lithium-ion batteries is essential to the performance of battery management systems of electric vehicles. One of the main reasons for the slow global adoption of electric cars is driving range anxiety. The ability of a battery management system to accurately estimate the state of charge can help alleviate this problem. In this paper, a comparison between data-driven state-of-charge estimation methods is conducted. The paper compares various neural network-based models and common regression models for SOC estimation. These models include several ablated transformer networks, a neural network, a lasso regression model, a linear regression model and a decision tree. Results of various experiments conducted on data obtained from natural driving cycles of the BMW i3 battery show that the decision tree outperformed all other models including the more complex transformer network with self-attention and positional encoding. The decision tree model scored perfect values of performance metrics (MSE = 0, \(R^2\) = 1, RMSE = 0, MAE = 0). This paper could be helpful for researchers to select appropriate data-driven methods for SOC estimation of lithium-ion batteries in real driving cycles.

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A Comparison of Baseline Models and a Transformer Network for SOC Prediction in Lithium-Ion Batteries

  • 会议论文

  • 发表日期：

    2025年

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  • Hadeel Aboueidah,
  • Abdulrahman Altahhan

摘要

Accurately predicting the state of charge of Lithium-ion batteries is essential to the performance of battery management systems of electric vehicles. One of the main reasons for the slow global adoption of electric cars is driving range anxiety. The ability of a battery management system to accurately estimate the state of charge can help alleviate this problem. In this paper, a comparison between data-driven state-of-charge estimation methods is conducted. The paper compares various neural network-based models and common regression models for SOC estimation. These models include several ablated transformer networks, a neural network, a lasso regression model, a linear regression model and a decision tree. Results of various experiments conducted on data obtained from natural driving cycles of the BMW i3 battery show that the decision tree outperformed all other models including the more complex transformer network with self-attention and positional encoding. The decision tree model scored perfect values of performance metrics (MSE = 0, \(R^2\) = 1, RMSE = 0, MAE = 0). This paper could be helpful for researchers to select appropriate data-driven methods for SOC estimation of lithium-ion batteries in real driving cycles.