Key result
Explainable AI enhances electric vehicle battery management versus traditional systems by improving diagnostic accuracy.
Why the study?
Traditional battery monitoring systems face challenges in accurately predicting electric vehicle battery health due to complex electrochemical processes.
Explainable AI can improve the accuracy and transparency of electric vehicle battery health management systems.
Answers are grounded in this paper and cite their sources.
May support XAI for EV battery maintenance; leaves open prospective validation before adoption.
Effective battery health management is crucial for the performance, safety, and longevity of electric vehicles (EVs). Traditional battery monitoring systems face challenges in accurately predicting battery health due to the complex electrochemical processes involved. This paper explores the use of Explainable Artificial Intelligence (XAI) to enhance battery health management in electric vehicles. XAI provides insights into complex machine learning models, allowing for transparent decision-making and improved understanding of the factors affecting battery health. This approach not only enhances the accuracy of battery diagnostics and prognostics but also fosters greater trust in AI-driven battery management systems. The study highlights the potential of XAI to offer actionable insights, facilitate predictive maintenance, and contribute to the overall efficiency and reliability of EV batteries, paving the way for safer and more sustainable electric mobility.
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Divya et al. (2025) studied Electric Vehicle Battery Health. Explainable Artificial Intelligence (XAI) vs. Traditional battery monitoring systems was evaluated on Battery diagnostics and prognostics accuracy. Explainable Artificial Intelligence (XAI) enhances battery health management in electric vehicles by providing transparent decision-making, improving diagnostic accuracy, and facilitating predictive maintenance.
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