Advanced Intelligent Management Technologies of New Energy Batteries

Dear Colleagues,

New energy batteries are pivotal for sustainable energy storage solutions, from electric vehicles to grid storage. Their reliable and safe operation hinges on accurate state monitoring and prediction. However, challenges such as nonlinear degradation mechanisms, varied/extreme operating conditions, and limited on-board computing resources hinder the accuracy and robustness of existing methods. This Special Issue aims to gather cutting-edge research advances in the state monitoring and prediction of new energy batteries, with a focus on fundamental challenges and innovative solutions in sensor technology, data-driven algorithms, electrochemical modelling and hybrid methodologies, hoping to bridge the lab-scale innovations and on-board/grid-scale applications. We encourage submissions exploring both theoretical breakthroughs and empirical studies to foster a comprehensive understanding of battery behavior and predictive capabilities. The scope includes state estimation (state-of-charge, state-of-health, state-of-power, state-of-energy, state-of-safety, remaining useful life, etc.), fault diagnosis and their prediction/prognosis across diverse new energy battery chemistries (lithium-ion, sodium-ion, fuel cell, flow batteries, etc.).

Potential topics include, but are not limited to, the following:

• Advanced SOC, SOH, SOP, SOE and SOS estimation algorithms.
• Fault diagnosis and prognosis for new energy batteries.
• Multi-physics coupling modeling for battery state estimation/prediction.
• Novel sensing technologies and multi-sensor data fusion for new energy battery monitoring.
• Physics-informed artificial intelligentmodels for battery state estimation/prediction.
• Low-computational-cost algorithms for on-board battery management systems.

Dr. Sijia Yang
Prof. Dr. Zeyu Chen
Dr. Jichao Hong
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

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