scholarly journals SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part II: SOC Estimators

Batteries ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 41 ◽  
Author(s):  
Roxana-Elena Tudoroiu ◽  
Mohammed Zaheeruddin ◽  
Nicolae Tudoroiu ◽  
Sorin-Mihai Radu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Response Speed ◽  
Electrical Circuit ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Generic Model ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

The purpose of this paper is to analyze the accuracy of three state of charge (SOC) estimators of a rechargeable Li-ion SAFT battery based on two accurate Li-ion battery models, namely a linear RC equivalent electrical circuit (ECM) and a nonlinear Simscape generic model, developed in Part 1. The battery SOC of both Li-ion battery models is estimated using a linearized adaptive extended Kalman filter (AEKF), a nonlinear adaptive unscented Kalman filter (AUKF) and a nonlinear and non-Gaussian particle filter estimator (PFE). The result of MATLAB simulations shows the efficiency of all three SOC estimators, especially AEKF, followed in order of decreasing performance by AUKF and PFE. Besides, this result reveals a slight superiority of the SOC estimation accuracy when using the Simscape model for SOC estimator design. Overall, the performance of all three SOC estimators in terms of accuracy, convergence of response speed and robustness is excellent and is comparable to state of the art SOC estimation methods.

scholarly journals Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3733
Author(s):  
Benedikt Rzepka ◽  
Simon Bischof ◽  
Thomas Blank
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Electric Vehicles ◽  
Cell Types ◽  
Estimation Methods ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Filter Tuning ◽  
Li Ion ◽  
Structured Approach

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

scholarly journals Online Estimation of Li-ion Battery SOC for Electric Vehicles Based on An Improved AEKF

2019 ◽  
Vol 118 ◽  
pp. 02025 ◽  
Author(s):  
Kaihui Feng ◽  
Bibin Huang ◽  
Qionghui Li ◽  
Hu Yan
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Equivalent Circuit Model ◽  
Estimation Accuracy ◽  
Extend Kalman Filter ◽  
Online Estimation ◽  
Soc Estimation ◽  
Kalman Filter Algorithm ◽  
Li Ion ◽  
Influence Of Noise

The purpose of this paper is to discuss how to eliminate the influence of noise time -varying characteristics on the accuracy of SOC estimation. Based on the matlab/simulink platform, the Thevenin equivalent circuit model of the battery is built, and an improved Adaptive Extend Kalman Filter (AEKF) is designed, which is compared with the Extend Kalman filter algorithm (EKF).The simulation results are shown that the improved AEKF algorithm results in effective online estimation SOC and the estimation accuracy is higher than the EKF algorithm.

scholarly journals State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Luping Chen ◽  
Liangjun Xu ◽  
Ruoyu Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Square Root ◽  
Battery Management ◽  
Balance Performance ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

scholarly journals State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Xian Wang ◽  
Zhengxiang Song ◽  
Kun Yang ◽  
Xuyang Yin ◽  
Yingsan Geng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Liquid Metal ◽  
Equivalent Circuit ◽  
Unscented Kalman Filter ◽  
State Of Charge ◽  
Convergence Time ◽  
Constant Current ◽  
Estimation Methods ◽  
Soc Estimation ◽  
Liquid Metal Batteries

Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms.

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