Models for Battery Health Assessment: A Comparative Evaluation
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- Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
- Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
- Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
- Yun Bao & Yuansheng Chen, 2021. "Lithium-Ion Battery Real-Time Diagnosis with Direct Current Impedance Spectroscopy," Energies, MDPI, vol. 14(15), pages 1-16, July.
- Miaomiao Zeng & Peng Zhang & Yang Yang & Changjun Xie & Ying Shi, 2019. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm," Energies, MDPI, vol. 12(16), pages 1-15, August.
- United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
- Sungwoo Jo & Sunkyu Jung & Taemoon Roh, 2021. "Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge," Energies, MDPI, vol. 14(21), pages 1-16, November.
- Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
- Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
- Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
- Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
- Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
- Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
- Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
- Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
- Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
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Keywords
state of health; incremental capacity analysis; electrochemical impedance spectroscopy; equivalent electric circuit model; aging model; neural network; support vector regression;All these keywords.
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