Artificial Intelligence-Driven Strategies for Advancing Lithium-Ion Battery Performance and Safety
Ayogoke Felix Omojola, Caleb Omata Ilabija, Chuks Ifeanyi Onyeka, Jude Ifeanyichukwu Ishiwu, Tosin Gideon Olaleye, Ifeoma Juliet Ozoemena, P.U. Nzereogu
Abstract
Artificial intelligence (AI) is revolutionizing the development and optimization of lithium-ion batteries (LIBs), which are critical in modern technologies like energy storage systems and electric vehicles (EVs). This review explores AI-driven strategies aimed at enhancing LIB performance, safety, and longevity. AI techniques, including machine learning models likeensemble methods, support vector machines, and neural networks, have been instrumental in predictive maintenance, state of charge (SoC) and state of health (SoH) estimation, and materials discovery. These AI approaches enable more accurate predictions of battery degradation and failures, optimizing charge cycles, and improving real-time diagnostics. Furthermore, AI enhances the design of safer and more efficient battery components by accelerating materials research, thus improving LIB capacity and safety profiles. However, despite these advancements, challenges like data quality, model interpretability, and the integration of AI models into existing industrial frameworks persist. Emerging technologies such as reinforcement learning and federated learning show great promise for addressing these obstacles, enabling dynamic optimization of charge cycles and the collaborative development of more generalized AI models. As collaborative research and open data-sharing initiatives expand, AI’s transformative potential in driving more sustainable, efficient, and safer energy storage solutions will continue to grow, shaping the future of LIBs and their applications in a greener, more energy-efficient world.