State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Sun, Daoming; Yu, Xiaoning; Wang, Chongmin; Zhang, Cheng; Huang, Rui; Zhou, Quan; Amietszajew, Tazdin; Bhagat, Rohit

doi:10.1016/j.energy.2020.119025

Cited by 122 publications

(47 citation statements)

References 62 publications

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“…[ 17,18 ] The EKF and UKF methods are introduced to expand their applications into non‐linear systems. [ 19,20 ] Based on a battery state‐space analysis, these methods efficiently removed process and measurement noise, producing robust SOC information. The authors then introduce the ACKF (adaptive cubature Kalman filter) for the estimation of the SOC.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Sakile

Sinha

2022

Advcd Theory and Sims

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As a new means of transportation, electric vehicles (EVs) have a lot of potential. On the other hand, EVs that employ lithium‐ion batteries face certain difficulties in forecasting the battery's health and remaining useful life. This paper uses the adaptive joint algorithm approach to calculate the battery's online parameters and accurate state of charge (SOC). To establish the battery online parameters, the forgetting factor recursive least square (FFRLS) technique is utilized, and the extended Kalman filter (EKF), unscented Kalman filter (UKF) are employed to estimate accurate SOC. Compared to the EKF/UKF method, the joint algorithm (FFRLS‐UKF) approach produces better results. The results are validated using the urban dynamometer driving schedule cycle and the ECE extra‐urban driving cycle (low powered vehicles) to determine the performance of the proposed algorithm. The error of the estimated SOC has fallen from 3.3% to 2%. The proposed adaptive joint algorithm has substantially improved the system's accuracy and provides better results than the EKF/UKF technique. Furthermore, the random variable noise is also supplied to the test data to ensure that the proposed method is robust.

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Section: Introductionmentioning

confidence: 99%

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Sakile

Sinha

2022

Advcd Theory and Sims

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“…The observer‐based SOC estimation methods are such as H ∞ observer, [ 21 ] H ∞ ‐based nonlinear observer, [ 22 ] discrete‐time nonlinear observer, [ 23 ] optimal adaptive gain nonlinear observer. [ 11 ] The filter‐based SOC estimation methods are such as extended Kalman filter (EKF), [ 24–26 ] multi‐innovation extended Kalman filter (MI‐EKF), [ 27 ] adaptive extended Kalman filter (AEKF), [ 28,29 ] dual extended Kalman filter (DEKF), [ 17 ] unscented Kalman filter (UKF), [ 30,31 ] and adaptive unscented Kalman filter (AUKF). [ 32,33 ] Most studies among them, however, have been restricted to only regarding the OCV as a function of SOC; the effect of temperature on OCV has received little attention.…”

Section: Introductionmentioning

confidence: 99%

Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

2021

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Herein, the improved open‐circuit voltage (OCV) estimation method is developed and applied to estimate the model parameters and state of charge (SOC) for lithium‐ion batteries simultaneously. First, the OCV and SOC mapping relationship with temperature dependence is explored with the help of the low‐current OCV test and the improved OCV estimation method is proposed for all test temperatures. Afterward, the dual adaptive extended Kalman filter (DAEKF) based on the residual sequence is utilized to identify the model parameters and estimate the SOC simultaneously. Finally, the proposed approach is verified with 50% initial SOC error and compared with the residual sequence‐based DEKF method at different temperatures under the Federal Urban Driving Schedule (FUDS) test. The results of this study indicate that the proposed DAEKF based on the proposed improving OCV estimation method and residual sequence could achieve higher SOC estimation accuracy with good robustness at all test temperatures.

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“…Lithium‐ion batteries (LIBs) are the preferred power source for transportation electrification as they are energy dense and relatively inexpensive. State‐of‐charge (SOC), a metric used to define available battery capacity, which in‐turn translates to available range in electric vehicle (EV), has been a topic of research interest 2–8 .

C

‐rate is another common terminology employed to indicate the amount of current that is discharged from the battery in 1 hour, for example, for a 3000 mAh or 3 Ah battery, a discharge rate of 1 C indicates a discharge current of 3 A. C‐rate is particularly useful for charging purposes as well, for the same 3 Ah battery a charge rate of

\frac{C}{2}

and

C

indicates that the battery is charged at a rate of 1.5 A (completely charged in 2 hours) and 3 A (completely charged in 1 hour), respectively.…”

Section: Introductionmentioning

confidence: 99%

A generative adversarial network‐based synthetic data augmentation technique for battery condition evaluation

et al. 2021

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Summary Energy storage systems have been in the spotlight for the past decade as they offer tangible solutions to the ever‐growing pollution problem faced by the planet. These storage systems, primarily lithium‐ion based, power most of the mobile devices and electric vehicles (EVs). Substantial efforts are being made to electrify every mode of transportation to combat climate change. Accurate state‐of‐charge (SOC) and state‐of‐health (SOH) assessment of lithium‐ion batteries play an important role for determining the available range in EVs. Research in this area of capacity estimation demands extensive curated battery parameter data such as voltage, current, and temperature. Performing repeated experiments to collect these data is expensive and tedious. Also, lack of diversity in open access datasets has limited research in this area. This paper introduces a generative adversarial network (GAN)‐based approach for data augmentation. This technique enables expansion of sparse datasets in‐turn enhancing the learning capability of Neural Networks used for SOC/SOH estimation. A time series GAN is used in the present work to produce synthetic data. This technique was evaluated on two publicly available battery parameter datasets to test its effectiveness. A Kullback‐Leibler Divergence value of 0.2317 and 1.0572 was obtained for the battery dataset obtained from NASA prognostics repository and Oxford battery degradation dataset, respectively. The contributions of the paper include: (a) synthetic time‐series data augmentation of battery parameters, (b) high‐fidelity diverse data generation of battery profile data such as voltage, current, temperature, and SOC.

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State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Cited by 122 publications

References 62 publications

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

A generative adversarial network‐based synthetic data augmentation technique for battery condition evaluation

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