リチウムイオン電池性能を説明する新しいモデルを開発（Modelling lithium-ion batteries: 30 seconds to impact）

Predicting lithium-ion battery behavior is critical for advancing next-generation energy storage. Conventional Doyle–Fuller–Newman models can simulate many materials, but they fail in phase-separating single-crystal systems, such as lithium iron phosphate (LiFePO₄, LFP), where the electrical connectivity of primary particles limits charge transport. We redefine the electrode as a network of reactive primary particles, each governed by validated electrochemical kinetics and interconnected through tomographic-informed contact resistances. Without empirical tuning, the model predicts voltage responses of LiFePO₄ electrodes across temperatures, rates, loadings, and dynamic load conditions using a single fitted physical parameter. It also captures and explains charge–discharge asymmetries and hysteresis. By bridging particle-scale physics up to cell-level performance, while retaining computational efficiency, this physics-based framework provides a foundation for the design, and control of single-crystal electrode systems.