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<p>As electric vehicles (EVs) gain popularity, enhancing battery charging systems and health management is critical for extending battery life and driving range. This research proposes an improved AC-DC power-factor-corrected resonant converter integrated with a cascaded modular multilevel three-level inverter using stacked SiC MOSFETs, an isolated LLC converter, and a front-end rectifier to optimize EV charging efficiency. To accurately predict battery State of Health (SOH), a long short-term memory (LSTM) neural network combined with an attention mechanism is employed. The LSTM model captures temporal dependencies in battery data, while the attention mechanism emphasizes the most relevant features over time, improving prediction accuracy. The model is trained on a dataset with six key features, including current, voltage, and temperature, and optimized using a Differential Evolution algorithm for hyperparameter tuning. Simulation results demonstrate a 15% improvement in SOH estimation accuracy compared to traditional machine learning approaches. This advancement supports more efficient battery management, helping to reduce degradation and extend EV battery lifespan. Future work will focus on further optimizing power factor correction converters for faster charging and enhancing battery health prediction using advanced deep learning techniques. Integration with renewable energy sources will also be explored to improve overall system sustainability.</p>

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Improving the charging system and battery health of electric vehicles using AC–DC power factor correction resonant converters

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Prakash A. Kharade,
  • Rajendra B. Mohite,
  • Jeyavel Janardhanan,
  • Shankar M. Patil

摘要

As electric vehicles (EVs) gain popularity, enhancing battery charging systems and health management is critical for extending battery life and driving range. This research proposes an improved AC-DC power-factor-corrected resonant converter integrated with a cascaded modular multilevel three-level inverter using stacked SiC MOSFETs, an isolated LLC converter, and a front-end rectifier to optimize EV charging efficiency. To accurately predict battery State of Health (SOH), a long short-term memory (LSTM) neural network combined with an attention mechanism is employed. The LSTM model captures temporal dependencies in battery data, while the attention mechanism emphasizes the most relevant features over time, improving prediction accuracy. The model is trained on a dataset with six key features, including current, voltage, and temperature, and optimized using a Differential Evolution algorithm for hyperparameter tuning. Simulation results demonstrate a 15% improvement in SOH estimation accuracy compared to traditional machine learning approaches. This advancement supports more efficient battery management, helping to reduce degradation and extend EV battery lifespan. Future work will focus on further optimizing power factor correction converters for faster charging and enhancing battery health prediction using advanced deep learning techniques. Integration with renewable energy sources will also be explored to improve overall system sustainability.