Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model

Xin Lu,Ning Chen,Hui Li,Siyuan Chen,Jun Xu

doi:10.3390/en11040714

Abstract

In recent years, the fractional order model has been employed to state of charge (SOC) estimation. The non integer differentiation order being expressed as a function of recursive factors defining the fractality of charge distribution on porous electrodes. The battery SOC affects the fractal dimension of charge distribution, therefore the order of the fractional order model varies with the SOC at the same condition. This paper proposes a new method to estimate the SOC. A fractional continuous variable order model is used to characterize the fractal morphology of charge distribution. The order identification results showed that there is a stable monotonic relationship between the fractional order and the SOC after the battery inner electrochemical reaction reaches balanced. This feature makes the proposed model particularly suitable for SOC estimation when the battery is in the resting state. Moreover, a fast iterative method based on the proposed model is introduced for SOC estimation. The experimental results showed that the proposed iterative method can quickly estimate the SOC by several iterations while maintaining high estimation accuracy.

Highlights

Lithium-ion batteries become the main energy storage medium in electric vehicles.A battery’s state of charge (SOC) indicates the battery remaining power which is of great significance for battery management system (BMS)
This paper presents a fractional continual variable order model and a rapid SOC estimation method for the battery system
It includes the fractional order modeling method of the battery, the order identification based on least square method and the SOC estimation method based on the correspondence between the fractional order and SOC

Summary

Introduction

A battery’s state of charge (SOC) indicates the battery remaining power which is of great significance for battery management system (BMS). Accurate estimation method of the SOC can prevent over-charging and over-discharging, which can prolong the lifetime of the battery [1]. The electrochemical characteristics of the battery can be considered as an electrochemical system with strong non-linearity and the battery SOC cannot be directly measured [2]. Temperature and cell degradation, vehicle batteries are usually divided into several groups, which switch between dynamic condition and resting state. Each battery pack is equipped with BMS to monitor the state of batteries. In the switching process of battery packs, a fast estimation method is needed to estimate the SOC for multiple battery groups, which can provide accurate initial values for other real-time estimation methods

Methods

Results

Conclusion