Multiphysics-Constrained Fast Charging of Lithium-Ion Battery With Active Set Predictive Control

Hao Zhong, Shujuan Meng, Xinan Zhang, Zhongbao Wei, Caizhi Zhang, Liang Du

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Fast charging of lithium-ion batteries (LIBs) is critical for the further popularity of electric vehicles (EVs). However, overlooking physical limits of LIBs may cause quick health degradation or even catastrophic safety issues. Motivated by this, this paper proposes a hybrid multi-physics-constrained charging strategy for LIBs combining an active set method (ASM)-based model predictive control (MPC) and a rule-based method. A general form of the optimal charging problem is constructed as a constrained quadratic program (QP) to balance the charging rapidity, thermal safety, and battery degradation. Enabled by this formulation, the cost-efficient ASM is proposed to solve the optimization problem, which virtually gives a safety-and health-aware fast charging strategy. Comparative results suggest that the proposed strategy outperforms the traditional MPC solutions remarkably in terms of the computational tractability. Long-term cycling experiments validate the superiority of the proposed strategy in the optimal balance among the charging rapidity, thermal safety, and life extension.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Batteries
  • Degradation
  • Fast charging
  • Integrated circuit modeling
  • Mathematical models
  • Optimization
  • Predictive control
  • Safety
  • active set method
  • battery health
  • lithium battery
  • model predictive control

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