Review on Research Progress in High-fidelity Modeling, Parameter Identification and Lifetime Prognostics of Lithium-ion Battery

doi:10.3901/JME.2022.22.019

Abstract

Abstract: Lithium-ion batteries with high energy density, low self-discharge rate, and long cycle life are the main energy storage devices in electric vehicles. The performance of lithium-ion batteries has direct effects on the power and security of electric vehicles.However, lithium-ion battery is a complex electrochemical system, and its internal states are time-varying and unmeasurable. Besides,the performance and life of the lithium-ion battery gradually deteriorate during use, which brings potential safety hazards. To operate the battery efficiently, safely, and reliably in demanding vehicle driving conditions, effective battery management is required. Battery modeling is the theoretical basis of management algorithms, and parameter identification is the premise of model application.Moreover, battery lifetime prediction is the key technology to ensuring battery safety. Aiming at real-world applications, a comprehensive review of high-fidelity electrochemical-thermal coupled modeling, non-destructive parameter identification, and battery lifetime prognostics is presented. The model reformulation and simplification of the macro-scale electrochemical models are introduced. The characteristics of experimental measurement and non-destructive methods for parameter identification are compared and analyzed. The algorithm architectures of the model-based, data-driven, and hybrid approaches for lifetime prediction are summarized. On these bases, upcoming challenges and future research directions are identified and discussed.

Key words: lithium-ion battery, electrochemical model, thermal model, parameter identification, lifetime prognostics

CLC Number:

TM912

XU Le, DENG Zhong-wei, XIE Yi, HU Xiao-song. Review on Research Progress in High-fidelity Modeling, Parameter Identification and Lifetime Prognostics of Lithium-ion Battery[J]. Journal of Mechanical Engineering, 2022, 58(22): 19-36.

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