Experimental data of lithium-ion battery and ultracapacitor under DST and UDDS profiles at room temperature

Wang, Yujie; Liu, Chang; Pan, Rui; Chen, Zonghai

doi:10.1016/j.dib.2017.01.019

Cited by 31 publications

(5 citation statements)

References 1 publication

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“…Data integrity is another barrier to applying AI strategies in practical situations. A top-notch battery dataset has been created by a few well-known automotive research teams and is openly available to the public [131]. The charge/discharge current pattern in this dataset is stable and guarantees the different EV driving cycle methods.…”

Section: Data Integritymentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

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Section: Data Integritymentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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show abstract

“…This dataset can be used for lithium battery SOC estimation, lithium battery performance measurement, and dynamic characteristic analysis of the pack operation. Wang et al [53] used the BTS-8000 to perform discharge tests on four LiFePO4 battery packs and supercapacitors under DST and UDDS conditions at room temperature, and the data can be used not only for the SOC estimation of a Li-ion battery but also for Li-ion battery and supercapacitor performance measurements, model parameter calibration, and dynamic characterization.…”

Section: Li-ion Battery Datasetmentioning

confidence: 99%

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Zhang

Zhong

et al. 2022

Machines

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As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.

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“…This dataset can be used for lithium battery SOC estimation, lithium battery performance measurement, and dynamic characteristics analysis of the pack operation. Wang et al [48] used the BTS-8000 to perform discharge tests on four LiFePO4 battery packs and supercapacitors under DST and UDDS conditions at room temperature, and the data can be used not only for Li-ion battery SOC estimation but also for Li-ion battery and supercapacitor performance measurement, model parameter calibration, and dynamic characterization.…”

Section: Li-ion Battery Datasetmentioning

confidence: 99%

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

Zhong¹,

Zhang²,

Xu³

et al. 2022

Preprint

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As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.

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Experimental data of lithium-ion battery and ultracapacitor under DST and UDDS profiles at room temperature

Cited by 31 publications

References 1 publication

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

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