AI-Assisted Physics-Based Model of Lithium-ion Battery for Power Systems Operation Research

Degradation of energy capacity and energy efficiency of lithium-ion battery energy storage systems depend on their operation conditions and are key factors in their business case feasibility. Using traditional energy reservoir models with energy throughput methods to quantify degradation leads to less accurate revenue estimates and unfeasible dispatch scenarios. This paper proposes a new neural network-based model of a lithium-ion battery energy storage system that replicates both degradation process and energy efficiency as functions of the operating conditions. The neural networks are trained using datasets derived from a detailed physics-based digital twin of lithium-ion cell models. The architecture of the neural networks is designed to allow their straightforward integration into mixedinteger optimization frameworks that are often used for energy storage systems simulations. The proposed model is tested for long-term energy arbitrage studies and the findings support the effectiveness of the proposed model, leading to extended lifespan and higher revenues for energy arbitrage studies when compared to conventional models.