Recovering large-scale battery aging dataset with machine learning

Tang, Xiaopeng; Liu, Kailong; Li, Kang; Widanage, Widanalage Dhammika; Kendrick, Emma; Gao, Furong

doi:10.1016/j.patter.2021.100302

Cited by 86 publications

(27 citation statements)

References 41 publications

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“…The SOH in this paper is defined as the ratio of current interval capacity to reference interval capacity. Since there is a certain conversion relationship between interval capacity and battery capacity, to obtain the battery capacity value, please refer to the relevant contents proposed by Tang et al in paper [12].…”

Section: Battery Pack Soh Calibration Resultsmentioning

confidence: 99%

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

Wang

et al. 2021

WEVJ

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In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. Particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform.

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Section: Battery Pack Soh Calibration Resultsmentioning

confidence: 99%

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

Wang

et al. 2021

WEVJ

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“…Accurate modeling such as equivalent circuit (Tang et al, 2021) or electrochemical models (Liu et al, 2022b) and state estimation are the basis of energy storage system management. Moreover, the accurate estimation of the battery state-of-charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles.…”

Section: System Modeling and State Estimationmentioning

confidence: 99%

Editorial: Hybrid energy storage systems: Materials, devices, modeling, and applications

et al. 2022

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“…Notwithstanding the error shown in Figure 6 and Table 3, the FOM model has higher precision than the IOM model. It should be noted that the battery's performance can change significantly with factors such as battery ageing (Tang et al, 2021). The modelling accuracy also will change with battery ageing.…”

Section: Model Verificationmentioning

confidence: 99%

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

et al. 2021

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The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge-discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters.

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Recovering large-scale battery aging dataset with machine learning

Cited by 86 publications

References 41 publications

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

Editorial: Hybrid energy storage systems: Materials, devices, modeling, and applications

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

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