{"id":7903,"date":"2026-06-15T10:51:51","date_gmt":"2026-06-15T02:51:51","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7903"},"modified":"2026-06-19T19:30:08","modified_gmt":"2026-06-19T11:30:08","slug":"jase-202610-33-014","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-014","title":{"rendered":"Research on Lithium Battery Health State Prediction Model Based on Multimodal Signal Decomposition and Uncertainty Quantification Technology"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Jiangjiang Li1,2<\/sup>, Lijuan Feng1,2<\/sup><\/i><\/a>, Junyan Zhao1,2<\/sup>, and Jingfen Li1,2<\/sup><\/p>\n\n\n\n

1<\/sup>School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, 450064, Zhengzhou, China<\/p>\n\n\n\n

2<\/sup>Zhengzhou Intelligent Safety Control Engineering Research Center for Unmanned Aerial Vehicles<\/p>\n<\/div>\n\n\n\n

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Received: March 26, 2026
Accepted: May 18, 2026
Publication Date: June 15, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Proposed lithium battery SOH prediction model <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.014<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Precise estimation of lithium-ion battery State of Health (SOH) is essential for the safe and efficient operation of energy systems and electronic devices. However, nonlinear degradation, multimodal noise, and prediction uncertainty limit the accuracy and credibility of traditional models. To address this, we propose a novel SOH prediction model integrating multimodal signal decomposition and uncertainty quantification. First, Successive Variational Mode Decomposition (SVMD) separates degradation features from noise by decomposing multimodal monitoring signals (voltage, current, temperature, and EIS) into intrinsic modal functions (IMFs). Second, an AM-CNN-BiLSTM hybrid network extracts deep spatial-temporal features from the IMFs for preliminary SOH prediction. Third, an MCMC-based Bayesian inference framework quantifies prediction uncertainty (data noise, model parameters, and extraction errors), providing a probability distribution that improves overall model credibility. Validated on the NASA and Oxford datasets against traditional and advanced models (including Battery GPT), the proposed method outperformed comparisons, achieving an RMSE under 0.46%, a MAPE under 0.91%, and a determination coefficient above 0.991. Additionally, uncertainty quantification showed a prediction interval coverage probability (PICP) of 95.2% at a 95% confidence level. Ultimately, this model provides a high-precision, credible technical approach with strong engineering prospects for Battery Management Systems (BMS).<\/p>\n\n\n\n

Keywords: Lithium battery; State of Health (SOH); Multimodal signal decomposition; Successive Variational Mode Decomposition (SVMD); Uncertainty quantification; Bayesian inference<\/em><\/p>\n\n\n\n

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