A Review on Testing of Electrochemical Cells for Aging Models in BESS
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- Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
- Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
- Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
- Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
- Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
- Matteo Moncecchi & Alessandro Borselli & Davide Falabretti & Lorenzo Corghi & Marco Merlo, 2020. "Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment," Energies, MDPI, vol. 13(11), pages 1-19, May.
- Liu, Chang & Wang, Yujie & Chen, Zonghai, 2019. "Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system," Energy, Elsevier, vol. 166(C), pages 796-806.
- Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
- Nebuloni, Riccardo & Meraldi, Lorenzo & Bovo, Cristian & Ilea, Valentin & Berizzi, Alberto & Sinha, Snigdh & Tamirisakandala, Raviteja Bharadwaj & Raboni, Pietro, 2023. "A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model," Applied Energy, Elsevier, vol. 334(C).
- Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
- Su, Xiaojia & Sun, Bingxiang & Wang, Jiaju & Zhang, Weige & Ma, Shichang & He, Xitian & Ruan, Haijun, 2022. "Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression," Applied Energy, Elsevier, vol. 322(C).
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Keywords
BESS degradation test; storage system aging tests; battery degradation; BESS aging mechanisms;All these keywords.
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