A State of Charge Estimation Approach for Lithium-Ion Batteries Based on the Optimized Metabolic EGM(1,1) Algorithm

Abstract

The accurate estimation of the state of charge (SOC) for lithium-ion batteries’ performance prediction and durability evaluation is of paramount importance, which is significant to ensure reliability and stability for electric vehicles. The SOC estimation approaches based on big data collection and offline adjustment could result in imprecision for SOC estimation under various driving conditions at different temperatures. In the traditional GM(1,1), the initialization condition and the identifying parameter could not be changed as soon as they are confirmed. Aiming at the requirements of battery SOC estimation with non-linear characteristics of a dynamic battery system, the paper presents a method of battery state estimation based on Metabolic Even GM(1,1) to expand battery state data and introduce temperature factors in the estimation process to make SOC estimation more accurate. The latest information data used in the optimized rolling model is introduced through the data cycle updating. The experimental results show that the optimized MEGM(1,1) effectively considers the influence of initial data, and has higher accuracy than the traditional GM(1,1) model in the application of data expansion. Furthermore, it could effectively solve the problem of incomplete battery information and battery capacity fluctuation, and the dynamic performance is satisfactory to meet the requirements of fast convergence. The SOC estimation based on the presented strategy for power batteries at different temperatures could reach the goal of the overall error within 1% under CLTC conditions with well robustness and accuracy.

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

“…For instance, a state-space representation can be introduced as a gray box model that can define the relationship between the input and output of the battery through differential equations, as in [39]. Gray Model-GM (1,1), shown in Figure 3, and the traditional Even Gray Model-EGM (1,1) are considered the most used approaches of gray relational analysis (GRA), and they can be used in many different applications, not only with batteries [40][41][42].…”

“…introduced as a gray box model that can define the relationship between the input and output of the battery through differential equations, as in [39]. Gray Model-GM (1,1), shown in Figure 3, and the traditional Even Gray Model-EGM (1,1) are considered the most used approaches of gray relational analysis (GRA), and they can be used in many different applications, not only with batteries [40][41][42]. Because of their versatility in managing many types of data and their ability to adequately capture complicated non-linear phenomena, data-driven methods are presented, such as fuzzy logic (FL) [43], artificial neural networks (ANNs) [44], genetic algorithms (GAs) [45], and support vector machines (SVMs) [46].…”

Current control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

“…New prediction information is continuously added to the original sequence to achieve long-term disaster rolling prediction in Taiwan [13]. The latest optimized rolling data information are continuously introduced into the original data to achieve the life performance prediction of lithium batteries [14]. The above applications on rolling gray prediction are multi-step prediction and require too much original data, and the updating process of original data is more complicated, which is not conducive to the digital process of EAST fast control power supply implementation.…”