A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Kim, Woo-Yong; Lee, Pyeong-Yeon; Kim, Jong-Hoon; Kim, Kyung-Soo

doi:10.3390/en12173383

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…26 In addition, through the establishment of a lithium-ion battery nonlinear model and a fractionalorder model, and applying them to the SOC estimation of electric vehicles, the accurate state of the effective state of electric vehicles is improved. 27,28 Taking into account the degree of polarization of the lithium-ion battery, accurate identification of the parameters of the battery's charging state and the state of health under complex operating conditions is realized. 29 By establishing a nonlinear degradation model, the energy storage effect and efficiency of lithium-ion batteries have been greatly improved.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries

Shi

Wang

Fernandez

et al. 2021

Intl J of Energy Research

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The internal nonlinearity of the lithium-ion battery makes its mathematical modeling a big challenge. In this paper, a novel lithium-ion battery spliceelectrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithium-ion battery models and refines the ohm and polarization characteristics of the electrochemical Nernst model and the differences in charge-discharge internal resistance. Moreover, by applying the one-sided limit to the discrete system, a multi-innovation least squares algorithm optimized based on the dynamic function (F-MILS) introduced to constrain the original innovation weight is put forward, which tackles the problem of large algorithm errors caused by huge changes in the system input. In order to evaluate the regulating ability of weight constraint factors, the relevant parameter values in the dynamic function are discussed as independent variables. Furthermore, parameters of the S-ECP model are identified online by HPPC experimental data combined with the multi-innovation least squares (MILS) algorithm ameliorated by the dynamic function, and the convergence speed of parameters in the identification process is analyzed. Finally, an urban dynamometer driving schedule experiment is carried out on the lithium-ion battery under more complex working conditions. It is revealed that the accuracy of F-MILS is 0.5% higher than that of unoptimized MILS, further confirming the accuracy of the S-ECP model and the superiority of the F-MILS algorithm. Highlights• A novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithiumion battery models and refines the ohm and polarization characteristics of

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Section: Literature Reviewmentioning

confidence: 99%

Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries

Shi

Wang

Fernandez

et al. 2021

Intl J of Energy Research

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show abstract

“…Because of the complexity of the battery characteristics, there are a number of methods such as the linear model, the nonlinear model, the Tremblay model and the Volterra model to estimate parameters and internal states of batteries [27][28][29]. Those are mathematical models to fulfill the different requirements such as battery life, efficiency, etc.. Basically, the solid electrolyte interface (SEI) layer is formed on the electrode surfaces from decomposition products of electrolytes with the battery during the charge and discharge operations.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

Lee

Park

et al. 2020

Electronics

Self Cite

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Renewable energy generation and energy storage systems are considered key technologies for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate forecasts of intermittent renewable energy resources that consider the impact of battery degradation on the system caused by the accumulation of charging and discharging cycles. In this study, a statistical model is presented for forecasting a day-ahead photovoltaic (PV) generation considering solar radiation and weather parameters. In addition, the technical performance of energy storage systems (ESS) should be evaluated by considering battery degradation that occurs during the charge and discharge cycles of the battery. In this study, a battery degradation model based on the data-driven method is used. Based on a suitable forecasting model, ESS scheduling is performed to charge the maximum amount of PV generation and discharge for the self-consumption of the customer load when PV generation ends. Since the battery is highly dependent on operating conditions such as depth of discharge, state of charge and temperature, two different ESS charge and discharge modes are proposed. From the simulation with the battery degradation model using parameters derived from experiments, we show that the battery is degraded along with charging cycles during testing periods. Variations in state of health are observed owing to the different characteristics of the battery according to the ESS operation modes, which are divided into the low and high SOC. Through experimental validation, it is proved that the state of charge (SOC), 0.45 is the optimal threshold that can determine the low and high SOC. Finally, the simulation results lead to the conclusion that the battery degradation in different operation modes should be taken into account to extend the end of life efficiently.

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“…Model-based approach leverages on an in-depth understanding of domain knowledge such as the internal chemical reaction in the cell, electrical properties of the components used to model them and complex mathematical equations to model the SOC 9 . Prominent model-based techniques include the Sliding Mode Observer 10 , Luenberger Observer 11 and Kalman filters 12 . While model-based approach can result in reliable and accurate models, it requires an extensive domain knowledge, rigorous feature engineering, and requires relatively long development time 13 .…”

Section: Introductionmentioning

confidence: 99%

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

Hannan

How

Lipu

et al. 2021

Preprint

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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.

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A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Cited by 15 publications

References 40 publications

Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries

Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries

An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

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

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