scholarly journals H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery

2021 ◽  
Vol 9 ◽  
Author(s):  
Hailong Feng ◽  
Zhifu Wang ◽  
Fujun Zhang
Keyword(s):  
Lithium Ion ◽  
Time Frame ◽  
Hysteresis Effect ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Ambient Temperatures ◽  
Transportation Equipment ◽  
Soc Estimation ◽  
Battery Model ◽  
Combined Algorithm

Accurate state of charge (SoC) estimation is crucial for the safe and reliable running of lithium-ion batteries in electrified transportation equipment. To enhance the estimation accuracy and robustness under different ambient temperatures, H∞ and the adaptive H∞ filterings were first combined to simultaneously forecast the parameters and SoC of the battery model considering the hysteresis effect in this paper. To drop the computational complexity to the most extent, the hysteresis unit was integrated into the first-order RC battery model and the aforementioned combined algorithm was developed under a dual-time frame. Then, the battery model with the hysteresis effect is evaluated against the model without that in terms of the estimation accuracy. Subsequently, the proposed algorithm is compared with the dual H∞ algorithm based on the employed battery model. The results demonstrate the excellent performance of the utilized battery model and the proposed algorithm in terms of both the estimation accuracy and the convergence speed.

scholarly journals A comparative study of fractional order models on state of charge estimation for lithium ion batteries

2020 ◽  
Author(s):  
Jinpeng Tian ◽  
Rui XIONG ◽  
Weixiang Shen ◽  
Ju Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Unscented Kalman Filter ◽  
Critical Role ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Accuracy ◽  
Estimation Accuracy ◽  
Ambient Temperatures ◽  
Soc Estimation

Abstract State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, for the first time, we compare five state-of-the-art FOMs in terms of SOC estimation. Firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy and generalisation ability. It can also maintain high accuracy even under the occurrence of sensor drift.

scholarly journals A comparative study of fractional order models on state of charge estimation for lithium ion batteries

2020 ◽  
Author(s):  
Jinpeng Tian ◽  
Rui XIONG ◽  
Weixiang Shen ◽  
Ju Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Unscented Kalman Filter ◽  
Critical Role ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Accuracy ◽  
Estimation Accuracy ◽  
Ambient Temperatures ◽  
Soc Estimation

Abstract State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

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 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 An Open-Circuit-Voltage Model of Lithium-Ion Batteries for Effective Incremental Capacity Analysis

2013 ◽  
Author(s):  
Caihao Weng ◽  
Jing Sun ◽  
Huei Peng
Keyword(s):  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Parametric Model ◽  
Open Circuit ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Cell Behavior ◽  
Capacity Analysis ◽  
Soc Estimation ◽  
Incremental Capacity

Open-Circuit-Voltage (OCV) is an essential part of battery models for state-of-charge (SOC) estimation. In this paper, we propose a new parametric OCV model, which considers the staging phenomenon during the lithium intercalation/deintercalation process. Results show that the new parametric model improves SOC estimation accuracy compared to other existing OCV models. Moreover, the model is shown to be suitable and effective for battery state-of-health monitoring. In particular, the new OCV model can be used for incremental capacity analysis (ICA), which reveals important information on the cell behavior associated with its electrochemical properties and aging status.

State of Charge Estimation Error due to Parameter Mismatch in a Generalized Explicit Lithium Ion Battery Model

2011 ◽  
Author(s):  
Xinfan Lin ◽  
Anna Stefanopoulou ◽  
Patricia Laskowsky ◽  
Jim Freudenberg ◽  
Yonghua Li ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Hybrid Electric Vehicle ◽  
Estimation Error ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Parameter Mismatch ◽  
Linear Diffusion ◽  
Soc Estimation ◽  
Battery Model

Model-based state of charge (SOC) estimation with output feedback of the voltage error is steadily augmenting more traditional coulomb counting or voltage inversion techniques in hybrid electric vehicle applications. In this paper, the state (SOC) estimation error in the presence of model parameter mismatch is calculated for a general lithium ion battery model with linear diffusion or impedance-based state dynamics and nonlinear output voltage equations. The estimation error due to initial conditions and inputs is derived for linearized battery models and also verified by nonlinear simulations. It is shown that in some cases of parameter mismatch, the state, e.g. SOC, estimation error will be significant while the voltage estimation error is negligible.

scholarly journals State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter

2021 ◽  
Vol 13 (9) ◽  
pp. 5046
Author(s):  
Jie Xing ◽  
Peng Wu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Error Covariance ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Statistics

State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

scholarly journals An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1366 ◽  
Author(s):  
Jinqing Linghu ◽  
Longyun Kang ◽  
Ming Liu ◽  
Bihua Hu ◽  
Zefeng Wang
Keyword(s):  
Computational Efficiency ◽  
Model Equation ◽  
Gaussian Function ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Proposed Model ◽  
Model Equations ◽  
The Impact

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

scholarly journals Towards Accurate State of Charge Estimation for Lithium-ion Batteries using Self-supervised Transformer Model: A Deep Learning Approach

2021 ◽  
Author(s):  
M Hannan ◽  
Dickson How ◽  
M. S. Hossain Lipu ◽  
M Mansor ◽  
Pin Ker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lithium Ion ◽  
Absolute Error ◽  
State Of Charge ◽  
Training Data ◽  
Ambient Temperatures ◽  
Soc Estimation ◽  
Proposed Model ◽  
Li Ion ◽  
Transformer Model

Abstract Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning-enabled transformer model achieves the lowest root-mean-square-error (RMSE) of 1.2% and a mean-absolute-error (MAE) of 0.7% on the test dataset at various ambient temperatures. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

scholarly journals Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2749 ◽  
Author(s):  
Nicolae Tudoroiu ◽  
Mohammed Zaheeruddin ◽  
Roxana-Elena Tudoroiu
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Matlab Simulation ◽  
Soc Estimation ◽  
Battery Model ◽  
Li Ion

Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis.

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