Simulation-Based Evaluation of Lithium-Ion Batteries Manufacturing Performance

Lithium-ion batteries (LIBs) have become indispensable components in diverse energy applications driven by their high energy density, long cycle life, and low self-discharge. These excellent characteristics are directly influenced by their manufacturing processes, where variations in battery design and processing parameters will lead to significant differences in performance. Therefore, reliable and efficient evaluation of battery performance across manufacturing processes is essential for quality assurance and process improvement. Traditional methods rely on formation cycling and associated electrochemical tests, which are time and cost intensive. Different from them, a simulation-based approach for manufacturing performance evaluation is proposed in this study. The method employs the pseudo two dimensions (P2D) electrochemical model within the PyBaMM framework, where model parameters such as electrode type, electrode size, and particle size are derived from manufacturing data and built-in parameter data. The model predicts key performance indicators including capacity, resistance, and loss of lithium inventory (LLI) under specified tests conditions. A LGM50T cell was tested under varying operational scenarios, demonstrating the feasibility of the approach. Results indicate that low temperature condition significantly accelerates degradation and Lithium plating, with the capacity degradation at 5 °C reaching about three times that at 25 °C, while moderate variations in charge current rate induce only minor differences of about 0.5%. Simultaneously, depth of discharge (DOD) and average state of charge (SOC) have similar effects on capacity degradation and LLI. By replacing extensive reality tests with physics-based simulations, this method enables rapid evaluation of manufactured or unprocessed battery process formulas, substantially reducing time and material costs while providing mechanistic insights into process performance interactions.