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Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications.
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A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning
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A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery
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Deep learning to estimate lithium-ion battery state of health without additional degradation experiments
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Deep neural network battery charging curve prediction using 30 points collected in 10 min
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A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters
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A voltage reconstruction model based on partial charging curve for state-of-health estimation of lithium-ion batteries
Sijia Yang, Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yang Gao, and Linjing Zhang. 2021 · 2021
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Ching Chang, Wei-Yao Wang, Wen-Chih Peng, and Tien-Fu Chen. 2024 · 2024
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Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson. 2024 · 2024
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Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen. 2024 · 2024
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Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, and Yonghong Tian. 2024 · 2024
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IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, and Dongjin Song. 2024 · 2024
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Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels
Tianyu Wang, Zhongjing Ma, Suli Zou, Zhan Chen, and Peng Wang. 2024 · 2024
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BatteryML: An Open-source Platform for Machine Learning on Battery Degradation. In The Twelfth International Conference on Learning Representations
Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, and Jiang Bian. 2024 · 2024
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