A Scientific Machine Learning Approach for Predicting and Forecasting Battery Degradation in Electric Vehicles

• URL: http://arxiv.org/abs/2410.14347v1
• Date: Fri, 18 Oct 2024 09:57:59 GMT
• Title: A Scientific Machine Learning Approach for Predicting and Forecasting Battery Degradation in Electric Vehicles
• Authors: Sharv Murgai, Hrishikesh Bhagwat, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat,
• Abstract summary: We present a novel approach to the prediction and long-term forecasting of battery degradation using Scientific Machine Learning framework. We incorporate ground-truth data to inform our models, ensuring that both the predictions and forecasts reflect practical conditions. Our approach contributes to the sustainability of energy systems and accelerates the global transition toward cleaner, more responsible energy solutions.
• Score: 1.393499936476792
• License:
• Abstract: Carbon emissions are rising at an alarming rate, posing a significant threat to global efforts to mitigate climate change. Electric vehicles have emerged as a promising solution, but their reliance on lithium-ion batteries introduces the critical challenge of battery degradation. Accurate prediction and forecasting of battery degradation over both short and long time spans are essential for optimizing performance, extending battery life, and ensuring effective long-term energy management. This directly influences the reliability, safety, and sustainability of EVs, supporting their widespread adoption and aligning with key UN SDGs. In this paper, we present a novel approach to the prediction and long-term forecasting of battery degradation using Scientific Machine Learning framework which integrates domain knowledge with neural networks, offering more interpretable and scientifically grounded solutions for both predicting short-term battery health and forecasting degradation over extended periods. This hybrid approach captures both known and unknown degradation dynamics, improving predictive accuracy while reducing data requirements. We incorporate ground-truth data to inform our models, ensuring that both the predictions and forecasts reflect practical conditions. The model achieved MSE of 9.90 with the UDE and 11.55 with the NeuralODE, in experimental data, a loss of 1.6986 with the UDE, and a MSE of 2.49 in the NeuralODE, demonstrating the enhanced precision of our approach. This integration of data-driven insights with SciML's strengths in interpretability and scalability allows for robust battery management. By enhancing battery longevity and minimizing waste, our approach contributes to the sustainability of energy systems and accelerates the global transition toward cleaner, more responsible energy solutions, aligning with the UN's SDG agenda.

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