Reduced-order electrochemical models with shape functions for fast, accurate prediction of lithium-ion batteries under high C rates

TL;DR

This paper introduces reduced-order electrochemical models using shape functions that enable fast, accurate predictions of lithium-ion battery behavior under high C rates, significantly improving simulation speed and reliability.

Contribution

The paper develops innovative shape function-based reduced-order models, RSPM and FCP2D, that are faster and maintain high accuracy compared to traditional P2D models under high C-rate conditions.

Findings

RSPM is over 33 times faster than P2D at moderate C rates.

FCP2D is about 8 times faster than P2D at high C rates.

Models predict battery parameters with less than 2% error.

Abstract

This paper proposes physical-based, reduced-order electrochemical models that are much faster than the electrochemical pseudo 2D (P2D) model, while providing high accuracy even under the challenging conditions of high C-rate and strong polarization of lithium ion concentration and potential in a battery cell. In particular, an innovative weak form of equations are developed by using shape functions, which reduces the fully coupled electrochemical and transport equations to ordinary differential equations, and provides self-consistent solutions for the evolution of the polynomial coefficients. Results show that the models, named as revised single-particle model (RSPM) and fast-calculating P2D model (FCP2D), give highly reliable prediction of battery operations, including under dynamic driving profiles. They can calculate battery parameters, such as terminal voltage, over-potential, interfacial current density, lithium-ion concentration distribution, and electrolyte potential distribution with a relative error less than 2%. Applicable for moderately high C rates (below 2.5 C), the RSPM is up to more than 33 times faster than the P2D model. The FCP2D is applicable for high C rates (above 2.5 C) and is about 8 times faster than the P2D model. With their high speed and accuracy, these physics-based models can significantly improve the capability and performance of the battery management system and accelerate battery design optimization.