Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging

Changfu Zou, Xiaosong Hu*, Zhongbao Wei, Torsten Wik, Bo Egardt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

180 Citations (Scopus)

Abstract

Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical-thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints.

Original languageEnglish
Pages (from-to)6635-6645
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume65
Issue number8
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

  • Electrochemical model
  • fast charging
  • lithium-ion (Li-ion) battery
  • model predictive control (MPC)
  • moving horizon estimation (MHE)
  • state estimation

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