scholarly journals State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Quanchun Yan ◽  
Kangkang Yuan ◽  
Wen Gu ◽  
Chenlong Li ◽  
Guoqiang Sun ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Ensemble Kalman Filter ◽  
Stress Test ◽  
Lithium Ion ◽  
State Of Charge ◽  
Least Square ◽  
Constant Current ◽  
Model Parameters ◽  
Fast Tracking

Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC.

scholarly journals Online Parameters Identification and State of Charge Estimation for Lithium-ion Battery Using Adaptive Cubature Kalman Filter

2021 ◽  
Vol 12 (3) ◽  
pp. 123
Author(s):  
Wei Li ◽  
Maji Luo ◽  
Yaqian Tan ◽  
Xiangyu Cui
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Battery Management System ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

scholarly journals Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

2019 ◽  
Vol 9 (6) ◽  
pp. 4876-4882
Author(s):  
Y. Muratoglu ◽  
A. Alkaya
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Balance Control ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Model ◽  
State Of Charge Estimation

Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

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

Energies ◽  
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.

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

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
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.

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

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