Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

TL;DR

This paper presents a fast and accurate method for estimating the states of a detailed lithium-ion battery model using orthogonal collocation and an extended Kalman filter, enabling reliable real-time monitoring.

Contribution

It introduces a novel combination of Chebyshev orthogonal collocation with an extended Kalman filter for high-fidelity battery state estimation.

Findings

State estimation error drops below 1% in under 200 seconds

Method is robust to 30% initial state-of-charge error

Achieves accurate estimates despite measurement noise

Abstract

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.