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

Abstract

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.

Fast Charging Control of Lithium-Ion Batteries: Effects of Input, Model, and Parameter Uncertainties

“…During the charging and discharging process of the battery, lithium ions travel through the separator between the cathode and the anode back and forth, causing the concentration (c) of lithium ions, the current density (i), the potential (Φ) and other physical quantities to change. The corresponding dynamic behaviors can be described by a series of partial differentialalgebraic equations (PDAEs) [5], where the superscript j ∈ {+, sep, −}, ± ∈ {+, −}.…”

“…In contrast, in an electrochemical model, a set of partial differential equations are used to describe the charging and discharging processes inside the cells. One of the most popular electrochemical models of Li-ion batteries is the pseudo-two-dimensional (P2D) model [5]. One dimension is along the particle radius as the porous electrodes are assumed to be composed of many spherical particles.…”

History-Agnostic Battery Degradation Inference

“…Comprehensive overviews of physics-based modeling of LIB degradation can be found in Refs. [14][15][16], but we will briefly summarize the attributes as well as strengths and weaknesses of some common models. The first category relies on empirical formulae with constraints set by physical principles, which can sometimes yield advantages of immediate interpretability after fitting to data.…”

Thermal uncertainty analysis of a single particle model for a Lithium-Ion cell

“…We remark that this is not a trivial task since "...the task of parameter identification is challenging. [...] many parameters are weakly identifiable from current and voltage measurements....", see [7]. In this context, we must highlight that we make inferences with a computer model whose solutions mimic the continuous model solutions with a specific order of error in practice.…”