Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

Download Full-text

Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽

10.3390/en14041054 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1054

Author(s):

Kuo Yang ◽

Yugui Tang ◽

Zhen Zhang

Keyword(s):

Kalman Filter ◽

Parameter Identification ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Recursive Least Squares ◽

Model Parameters ◽

Soc Estimation ◽

Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

Download Full-text

Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.2211 ◽

2011 ◽

Vol 403-408 ◽

pp. 2211-2215 ◽

Cited By ~ 5

Author(s):

Ke Xin Wei ◽

Qiao Yan Chen

Keyword(s):

Kalman Filter ◽

State Of Charge ◽

Model Parameters ◽

Adaptive Kalman Filter ◽

Single Model ◽

Soc Estimation ◽

Battery Model ◽

Kalman Filter Algorithm ◽

Estimation Precision ◽

State Of Charge Estimation

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

Download Full-text

Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽

10.3390/electronics10020122 ◽

2021 ◽

Vol 10 (2) ◽

pp. 122

Author(s):

Peipei Xu ◽

Junqiu Li ◽

Chao Sun ◽

Guodong Yang ◽

Fengchun Sun

Keyword(s):

Equivalent Circuit Model ◽

Lithium Ion ◽

Maximum Error ◽

State Of Charge ◽

Recursive Least Squares ◽

Accurate Estimation ◽

Model Parameters ◽

Soc Estimation ◽

Capacity Degradation ◽

Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Download Full-text

A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

Applied Sciences ◽

10.3390/app8112028 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2028 ◽

Cited By ~ 9

Author(s):

Xin Lai ◽

Dongdong Qiao ◽

Yuejiu Zheng ◽

Long Zhou

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Estimation Algorithm ◽

State Of Charge ◽

Distribution Area ◽

Model Parameters ◽

Model Accuracy ◽

Soc Estimation ◽

Fuzzy Fusion ◽

Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

Download Full-text

An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽

10.3390/electronics7110321 ◽

2018 ◽

Vol 7 (11) ◽

pp. 321 ◽

Cited By ~ 9

Author(s):

Xin Lai ◽

Wei Yi ◽

Yuejiu Zheng ◽

Long Zhou

Keyword(s):

Particle Swarm Optimization ◽

Kalman Filter ◽

Lithium Ion Batteries ◽

Extended Kalman Filter ◽

Particle Swarm ◽

Lithium Ion ◽

State Of Charge ◽

Model Parameters ◽

Swarm Optimization ◽

Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

Download Full-text

Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/1757384 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Wei Xiong ◽

Yimin Mo ◽

Cong Yan

Keyword(s):

Least Squares ◽

Computational Efficiency ◽

High Accuracy ◽

State Of Charge ◽

Joint Estimation ◽

Model Parameters ◽

Battery Management System ◽

Battery Management ◽

Soc Estimation ◽

Bias Compensation

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

Download Full-text

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Mathematical Problems in Engineering ◽

10.1155/2018/7480602 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Zheng Liu ◽

Xuanju Dang ◽

Hanxu Sun

Keyword(s):

Moving Average ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

State Of Charge ◽

Parameters Identification ◽

Estimation Methods ◽

Model Parameters ◽

Battery Management System ◽

H Infinity ◽

Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

Download Full-text

State of charge estimation by using extended Kalman filter based on improved open circuit voltage model

International Journal of Applied Power Engineering (IJAPE) ◽

10.11591/ijape.v10.i1.pp1-10 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

Maamar Souaihia ◽

Bachir Belmadani ◽

Rachid Taleb ◽

Kamel Tounsi

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Equivalent Circuit Model ◽

Open Circuit ◽

State Of Charge ◽

Soc Estimation ◽

Battery Model ◽

Filter Approach ◽

State Of Charge Estimation ◽

Good Agreement

This paper focuses on the state of charge estimation (SOC) for battery Li-ion. By modeling a battery based on the equivalent circuit model, the extended Kalman filter approach can be applied to estimate the battery SOC. An electrical battery model is developed in Matlab, Where the structure of the model is detailed by equations and blocks. The battery model has been validated from the experiment results. The comparison shows a good agreement in predicting the voltage, SOC estimation and the model performs better in SOC estimation.

Download Full-text

Battery Model Parameters Estimation Using Simulated Annealing

TEMA (São Carlos) ◽

10.5540/tema.2017.018.01.0127 ◽

2017 ◽

Vol 18 (1) ◽

pp. 127 ◽

Cited By ~ 4

Author(s):

Marcia De Fatima Brondani ◽

Airam Teresa Zago Romcy Sausen ◽

Paulo Sérgio Sausen ◽

Manuel Osório Binelo

Keyword(s):

Mathematical Modeling ◽

Experimental Data ◽

Simulated Annealing ◽

Visual Analysis ◽

Lithium Ion ◽

Parameters Estimation ◽

Model Parameters ◽

Algorithm Efficiency ◽

Battery Model ◽

Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

Download Full-text