A Mixed Continuous-Discrete Approach to Fast Charging of Li-ion Batteries While Maximizing Lifetime. Issue 30 (2022)

• Record Type: Journal Article
• Authors: Berliner, Marc D.; Cogswell, Daniel A.; Bazant, Martin Z.; Braatz, Richard D.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 30(2022)
• Page Start: 305
• Page End: 310
• Publication Date: 2022
• Subjects: Energy systems -- Control of partial differential equations -- Distributed parameter systems -- Descriptor systems Mathematics subject classification: Partial differential equations; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.11.070
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Fast charging studies for lithium-ion batteries aim to minimize charging time while maximizing battery lifetime. Real-time optimal control problems are typically solved using empirical or simplified physical models with constraint-based model predictive control (MPC). In this article, we derive physics-based operating modes based on degradative governing equations, which are used to ensure safe use and minimal degradation during long-term cycling. The fast-charging protocols are efficiently and deterministically simulated using a mixed continuous-discrete (aka hybrid) approach to fast charging. This simultaneously solves the battery system of equations and the constraint-based control problem. The approach is evaluated using a Porous Electrode Theory-based model that includes solid-electrolyte interface (SEI) capacity fade. Three physics-based charging protocols are compared to a conventional constant current-constant voltage (CC-CV) protocol. Given identical levels of capacity fade after 500 cycles, the physics-based protocols uniformly reach a greater charge capacity compared to CC-CV after charging for 10 and 15 minutes. The computational cost of simulating physics-based charging protocols is only about 30% greater than the CC-CV method. The fast charging framework is easily extendable to other battery models, irrespective of model complexity.