4.6 Article

Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries

Journal

CONTROL ENGINEERING PRACTICE
Volume 124, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2022.105176

Keywords

Lithium-ion battery; Control-oriented management; Energy storage; Electrochemical model; Model reduction; Parameter identification

Funding

  1. EPSRC [EP/R030243/1]
  2. National Natural Science Foundation of China (NSFC) [62173218, 52007119]
  3. Key Project of Science and Technology Commission of Shanghai Municipality [19500712300]
  4. 111 Project [D18003]
  5. International Corporation Project of Shanghai Science and Technology Commission [21190780300]
  6. High Value Manufacturing Catapult project [8248 CORE]

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This paper provides a systematic review of recent advancements in electrochemical model development and parameterization for battery management systems. It summarizes and analyzes classic pseudo-two-dimensional models and related model order reduction methodologies, as well as enhanced models considering cell internal inhomogeneity. The paper also discusses parameter identification techniques and solutions for optimizing the parameterization procedure, and highlights current research gaps and challenges.
Battery management systems based on electrochemical models could achieve more accurate state estimations and efficient battery controls with access to cell unmeasurable physical variables. As battery electrochemical models are governed by first-principle partial differential equation sets, model complexity and multiple parameter determination are bottlenecks for their wider applications. This paper gives a systematical review of recent advancements in electrochemical model development and parameterization. Specifically, classic pseudo-two-dimensional model and related model order reduction methodologies are first summarized and analyzed. Given that the homogenization hypothesis of the pseudo-two-dimensional model could lead to significant model mismatch under some operational conditions, enhanced models considering cell internal inhomogeneity with multi-particles, multi-scales, aging and thermal dynamics are examined. To facilitate model portability, parameter identification techniques of these models are classified, and solutions for optimizing the parameterization procedure are explored. Finally, current research gaps in the literature and remaining challenges are discussed and highlighted with some suggestions. This review will therefore inform the engineers of battery management and control engineering, whilst boosting the research, design and operation of control-oriented electrochemical models for smarter battery management at different readiness levels.

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