SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters

Abstract

The state of charge (SOC) of the battery is an important basis for the battery management system to perform state monitoring and control decisions. In this paper, by identifying the internal parameters of the battery model at different temperatures and SOCs of the lithium-ion battery, the specific factors that affect the change of the parameters are analyzed, the segmentation basis of the model and the fitting method of related parameters are discussed, the second-order equivalent circuit model of the lithium-ion battery whose parameters vary with SOC and temperature is established, the unscented Kalman filter (UKF) is used to estimate the SOC of the lithium-ion battery, and an improved SOC estimation method based on optimized equivalent circuit model is proposed. Simulation and experimental results show that the improved SOC estimation strategy can obtain high estimation accuracy in a wide temperature range.

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

“…Model-based methods for lithium-ion battery SOC estimation frequently employ techniques such as Kalman filtering, 14 particle filtering, and other mathematical models. 15,16 These methods establish reliable equivalent circuit models and state equations for lithium-ion batteries and use filtering techniques to estimate the SOC based on these models. 17 Fusion-based SOC estimation methods aim to integrate multiple estimation techniques to leverage their respective strengths and compensate for weaknesses.…”

Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis

“…By using a linear variable parameter circuit model, this approach can simulate the non-linear running characteristics of batteries with high accuracy and computational efficiency [32][33] . There are several types of equivalent circuit models available [34][35][36] , including the Rint, Thevenin, PNGV, second-order RC, and GNL models. The description equations and model parameters are shown in the Tab.…”

A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems

“…As demonstrated in [53][54][55], it is clear that the variation of the model parameters is significant in correspondence with decidedly accentuated T batt variations, i.e., with gradients even higher than ∆T >> 10 • C; therefore, it is legitimate to state that for insignificant variations in the temperature profile the parameters can be considered unchanged, i.e., not explanatory of particular changes in their values. It should be specified that the environments in which the BESSs are placed have an actively controlled temperature in order to stabilize performance and avoid possible excessive increases (or reductions) such as to jeopardize behavior (such as thermal leaks, although in such contexts, there is also the corrective intervention of the BMS) and at the same time convective heat exchange mechanisms are implemented with the surrounding environment described by the following relationship:…”