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The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness. <\/jats:p>","DOI":"10.1142\/s0218001424520207","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T06:58:46Z","timestamp":1719644326000},"source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Lithium-Ion Battery SOH Estimation Using Bayesian-Optimized CNN Deep Learning Approach"],"prefix":"10.1142","volume":"38","author":[{"given":"Xiaorong","family":"Huang","sequence":"first","affiliation":[{"name":"Dongguan Power Supply Bureau of Guangdong Power Grid Corporation, Dongguan, Guangdong, P.\u00a0R.\u00a0China"}]},{"given":"Jionghui","family":"Wei","sequence":"additional","affiliation":[{"name":"Dongguan Power Supply Bureau of Guangdong Power Grid Corporation, Dongguan, Guangdong, 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