DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
- Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
- Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
- Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
- Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
- Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
- Jichao Hong & Fengwei Liang & Xun Gong & Xiaoming Xu & Quanqing Yu, 2022. "Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network," Energies, MDPI, vol. 15(24), pages 1-14, December.
- Angeles Cabañero, Maria & Altmann, Johannes & Gold, Lukas & Boaretto, Nicola & Müller, Jana & Hein, Simon & Zausch, Jochen & Kallo, Josef & Latz, Arnulf, 2019. "Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries," Energy, Elsevier, vol. 171(C), pages 1217-1228.
- Nikita V. Martyushev & Boris V. Malozyomov & Olga A. Filina & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
- Liming Deng & Wenjing Shen & Kangkang Xu & Xuhui Zhang, 2024. "An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration," Energies, MDPI, vol. 17(7), pages 1-15, April.
- Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
- Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
- Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
- Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
- Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
- Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
- Boris V. Malozyomov & Nikita V. Martyushev & Nikita V. Babyr & Alexander V. Pogrebnoy & Egor A. Efremenkov & Denis V. Valuev & Aleksandr E. Boltrushevich, 2024. "Modelling of Reliability Indicators of a Mining Plant," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
- Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.
- Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
- Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
- Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
- Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
- Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
More about this item
Keywords
mathematical model; state of charge; electric vehicle; machine learning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4635-:d:1279412. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.