期刊
JOURNAL OF POWER SOURCES
卷 482, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2020.228895
关键词
Optimal charging; Non-linear model predictive control; Lithium-ion battery; Pseudo-spectral methods
资金
- Higher Education Commission (HEC) of Pakistan
This article introduces a novel control algorithm for online optimal charging of lithium-ion batteries by explicitly incorporating degradation mechanisms, focusing on minimizing the growth of the solid electrolyte interface (SEI) layer. The algorithm uses a first-order layer growth rate model in a nonlinear model predictive control approach, with results compared to traditional constant current constant voltage (CCCV) methods. The proposed strategy also ensures that overpotential caused by lithium plating remains in a healthy regime.
This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth the solid electrolyte interface (SEI) layer, which is the primary source of degradation of batteries. This article addresses the challenge of minimising SEI layer growth during charging by incorporating the first-order layer growth rate model into a non-linear model predictive control approach. A single particle model (SPM) is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage (CCCV) approach without considering SEI layer growth. is ensured that overpotential caused by lithium plating remains in a healthy regime which is another feature the proposed strategy. Simulation results are presented to demonstrate the advantages of the proposed charging method.
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