A Mixed Continuous-Discrete Approach to Fast Charging of Li-ion Batteries While Maximizing Lifetime

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 phys...

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Bibliographic Details
Published inIFAC-PapersOnLine Vol. 55; no. 30; pp. 305 - 310
Main Authors Berliner, Marc D., Cogswell, Daniel A., Bazant, Martin Z., Braatz, Richard D.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2022
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ISSN2405-8963
2405-8963
DOI10.1016/j.ifacol.2022.11.070

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Summary: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.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2022.11.070