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Battery technology is advancing due to the demand of safe energy storage in systems such as Electric Vehicles (EVs) and portable electronics systems. Estimating battery conditions and increasing the range of electric vehicles requires specific models and optimize control strategies. Anticipate battery performance is complicated due to several variables, such as temperature, deterioration, and usage models. Knowing battery performance entails an advanced understanding of time series analysis, particularly forecasting. This study proposes the use of Artificial Neural Networks (ANN) to create a Lithium Ion Battery (LIB) prediction model. The experiments were carried out in a 59.2 VDC, 120 Ah LIB, recording the voltage and temperature while simulating the charge and discharge. A hybrid model is developed with internal local models using clustering. The model prognostic the State of Charge (SOC) evolution and reach a Mean Squared Errors with values between \(10^{-3}\) and \(10^{-4}\) . This research shows that hybrid models are adecuated instrument for predict the battery SOC.

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A Hybrid Intelligence Model Forecasts the SOC of Electric Vehicle’s Battery

  • 会议论文

  • 发表日期：

    2025年

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  • Manuel Rubiños,
  • Paula Arcano-Bea,
  • Míriam Timiraos,
  • Álvaro Michelena,
  • Rafael Vega Vega,
  • José Manuel Andújar,
  • José-Luis Casteleiro-Roca

摘要

Battery technology is advancing due to the demand of safe energy storage in systems such as Electric Vehicles (EVs) and portable electronics systems. Estimating battery conditions and increasing the range of electric vehicles requires specific models and optimize control strategies. Anticipate battery performance is complicated due to several variables, such as temperature, deterioration, and usage models. Knowing battery performance entails an advanced understanding of time series analysis, particularly forecasting. This study proposes the use of Artificial Neural Networks (ANN) to create a Lithium Ion Battery (LIB) prediction model. The experiments were carried out in a 59.2 VDC, 120 Ah LIB, recording the voltage and temperature while simulating the charge and discharge. A hybrid model is developed with internal local models using clustering. The model prognostic the State of Charge (SOC) evolution and reach a Mean Squared Errors with values between \(10^{-3}\) and \(10^{-4}\) . This research shows that hybrid models are adecuated instrument for predict the battery SOC.