How Does Model Reduction Affect Lithium-Ion Battery State of Charge Estimation Errors? Theory and Experiments

Abstract

This article examines the impact of unmodeled dynamics on the accuracy of model-based lithium-ion battery state of charge (SOC) estimation. The article is motivated by the need for accurate SOC estimation for online battery diagnostics and control. Reduced-order battery models can lessen the computational cost of online SOC estimation. However, this comes at a price: model reduction is known to cause an estimation bias, in addition to the estimation noise typically induced by sensor measurement noise. This article derives analytic expressions for the expected estimation bias for two lithium-ion battery models: a nonlinear equivalent circuit model (ECM), and a nonlinear electrolyte enhanced single particle model. This derivation assumes a least squares SOC estimation law, but can be extended to other estimation laws. The article validates the analytic SOC estimation error expressions using both Monte Carlo simulation and experiments, for two different commercial lithium-ion batteries: a LiFePO4 battery and a LiNixCoyMnzO2 battery. The end result is, to the best of the authors’ knowledge, the first body of experimentally-validated analytic expressions for the expected SOC estimation errors resulting from lithium-ion battery model reduction.

Hybrid electric topology for short sea ships with high auxiliary power availability requirement

“…The main question to answer is whether the 20% SoC reserve, which is always present in the battery, is enough to perform this task without the SoC falling below 5%. At such SoC levels, the open circuit voltage of the cells drops drastically, and the state of health of the batteries is altered significantly [25].…”

A Novel Method for State of Charge Estimation in Lithium-Ion Batteries Using Temporal Convolutional Network and Multi-Verse Optimization

“…The state of charge (SOC) of a lithium-ion power battery is a critical parameter reflecting the ratio between its remaining capacity and maximum available capacity [3]. This parameter plays a fundamental role in assessing the durability, reliability monitoring, and cruising range estimation within the battery management system [4,5]. Accurate SOC estimation is crucial for optimizing battery performance, including the control strategy, balancing technology, energy utilization efficiency, and cycle life.…”

On the Error of Li-ion Battery Parameter Estimation Subject to System Uncertainties

“…The effectiveness of these analyses is thus significantly restricted, as estimation bias and system uncertainties are major sources of estimation error and are inevitable in practice. 8 Specifically, estimation accuracy has been shown to be strongly influenced by constant and varying uncertainty in model (e.g., due to unmodeled dynamics), 29 measurement (e.g., due to sensor bias/noise), 30 and (non-estimated) parameters (e.g., due to changing operating conditions/degradation). 31 We sought to address these limitations in a prior work through the derivation of a univariate estimation error equation for the least-squares objective, which directly predicts the estimation error through consideration of uncertainties in model, measurement, and parameter.…”