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

To unlock the promise of electrified transportation and smart grid, emerging advanced battery management systems (BMSs) shall play an important role in health-aware monitoring, diagnosis, and control of widely used lithium-ion (Li-ion) batteries. Sophisticated physics-based battery models incorporated in the advanced BMS can offer valuable battery internal information to achieve improved operational safety, reliability and efficiency, and to extend the lifetime of the batteries. However, developed from the fundamental electrochemical and thermodynamic principles, the rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. This article reviews prevailing order reduction techniques of physics-based Li-ion battery models to facilitate the development of next-generation BMSs. We analyze and comparatively characterize these techniques, mainly from perspectives of model fidelity, computational efficiency, and the scope of applications. By representing many effective and flexible reduced-order models as equivalent circuits, designers and practitioners, who do not have electrochemical expertise but with knowledge of circuit theory, can readily gain insights into multi-physical dynamics as well as their coupling effects inside the batteries. In addition, recommendations are made on how to select appropriate physics-based models for various model-based applications in battery management. Finally, the prospect of physical model-enabled BMSs is discussed, including the potential challenges and future research directions.

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Model Order Reduction Techniques for Physics-Based Lithium-ion Battery Management: A Survey

http://manuscript.elsevier.com/S0378775322001513/pdf/S0378775322001513.pdf

Developing extreme fast charge battery protocols – A review spanning materials to systems

To unlock the promise of electrified transportation and smart grids, emerging advanced battery management systems (BMSs) will play an important role in the health-aware monitoring, diagnosis, and control of lithium-ion (Li-ion) batteries (see “Acronyms Used in This Article”). Sophisticated physics-based battery models incorporated into BMSs can offer valuable internal battery information to achieve improved operational safety, reliability, and efficiency and to extend the battery lifetimes. However, because they are developed from fundamental electrochemical and thermodynamic principles, rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. 

https://eprints.qut.edu.au/212826/1/IEM_ROM_2021.pdf

Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Many battery applications require timely estimates of the maximum power that may be either extracted during operation or added during charge. Traditional methods of computing these estimates rely mostly on simple computations using empirically derived limits imposed on cell voltage, current and possibly temperature. While generally effective, such methods do not fully account for the dynamic aspects of the underlying model. This paper proposes a novel method of real-time power limit estimation that uses a model predictive control-inspired approach which enlarges the functional window right up to true established bounds of cell performance.

A Low-Cost MPC-Based Algorithm for Battery Power Limit Estimation.

Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This letter develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This letter establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This paper develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This work establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

Kiana Karami

Papers

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model