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.

翻译：碳排放正以惊人的速度增长，对全球减缓气候变化的努力构成重大威胁。电动汽车已成为一种前景广阔的解决方案，但其对锂离子电池的依赖带来了电池退化的关键挑战。准确预测和预报电池在短期和长期时间跨度内的退化，对于优化性能、延长电池寿命以及确保有效的长期能源管理至关重要。这直接影响电动汽车的可靠性、安全性和可持续性，支持其广泛采用，并与联合国关键可持续发展目标保持一致。在本文中，我们提出了一种利用科学机器学习框架预测和长期预报电池退化的新方法，该框架将领域知识与神经网络相结合，为预测短期电池健康状况和预报长期退化提供了更具可解释性和科学依据的解决方案。这种混合方法捕捉了已知和未知的退化动态，提高了预测准确性，同时减少了数据需求。我们纳入真实数据来指导模型，确保预测和预报结果反映实际条件。在实验数据中，模型使用UDE（通用微分方程）实现了9.90的均方误差（MSE），使用NeuralODE（神经微分方程）实现了11.55的MSE；使用UDE的损失为1.6986，使用NeuralODE的MSE为2.49，这证明了我们方法增强的精确性。这种数据驱动洞见与SciML在可解释性和可扩展性方面优势的结合，实现了稳健的电池管理。通过延长电池寿命和减少浪费，我们的方法有助于能源系统的可持续性，并加速全球向更清洁、更负责任的能源解决方案转型，这与联合国的可持续发展目标议程相一致。