A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms

Abstract

Abstract-In order to guarantee safe and reliable operation of electric vehicle batteries and to optimise their energy and capacity utilisation, it is indispensable to estimate their state-of-charge (SoC). This study aimed to develop a novel estimation approach based on the Grey model and Genetic Algorithms without the need of a high-fidelity battery model demanding high computation power. A SoC analytical model was established using the Grey System theory based on a limited amount of incomplete data in contrast to conventional methods. The model was further improved by applying a sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the Genetic Algorithms were introduced to identify the optimal adjustment coefficient, λ, in a traditional Grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of Lithium-ion batteries were used as the device-under-test that underwent typical passenger car driving cycles. The proposed SoC estimation method were verified under diverse battery discharging conditions and it demonstrated superior accuracy and repeatability compared to the benchmarking GM method.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…The formula using identified diffusion capacitance was derived to determine the SOH of a Li-ion battery. The GA fused with some other model-based estimation methods was used to estimate the SOC [155,[167][168][169][170][171]. The GA was also utilized in tuning of the fading KF to further improve the estimation of the SOC for a Li-ion battery [126].…”

“…Zheng et al 2013 [165] ≤± 0.55% Xu et al 2014 [155] ≤± 2.0% Khan et al 2014 [168] ME ≤ ± 5.0% Lim et al 2016 [126] ME ≤ ± 2.0% (in UDDS); ≤± 3.0% (in real driving EV) Mu et al 2017 [171] ≤± 2.98% Yang et al 2017 [172] ME ≤ ± 2.1% Chen et al 2018 [169] ≤± 1.0%…”

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

“…The genetic algorithm, which is from natural selection and genetic inheritance, can search the optimal solutions by simulating the process of natural evolution. Nowadays, this algorithm has been widely employed to search for the best solution from a sizeable multidimensional solution space [33]. The main steps of the genetic algorithm consist of choosing, which is used to select excellent offspring, and crossing, variation, which are employed to maintain the diversity of the population.…”

Influence of chemical composition of windmill palm fibre on crystallinity after alkali peroxide bleaching by grey model

“…A GM can be developed by solving a system of linear equations using process data as inputs. 16 The GM has the advantages of high precision, lower data requirements and unordered data. 17 In this work, alkaline peroxide bleaching was used to extract hemicellulose, cellulose and lignin from raw windmill palm fibres.…”