Leveraging AI for Failure Diagnosis: Insights into Clean Energy Storage & Power Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: 15 May 2026 | Viewed by 4
Special Issue Editors
Interests: electric vehicle; energy storage; thermal analysis; multi-physics modelling; artificial intelligence application
Special Issues, Collections and Topics in MDPI journals
Interests: lithium-ion battery safety; high-voltage electrolyte
Special Issues, Collections and Topics in MDPI journals
Interests: fuel cells; electrochemistry; automotive engineering; control technology
Special Issue Information
Dear Colleagues,
Transportation and energy storage electrification are considered the most promising ways to achieve the energy revolution and carbon neutralization worldwide. Developing clean energy devices has been investigated as the most critical energy storage technology to support all modes of electrified transportation and energy storage, such as electric vehicles, electric trains, electric ships, and electric aircraft, as well as energy storage stations. The ever-increasing demands of all-weather applications, long lifespan, high energy density, fast charging, low cost, and high safety in electrified transportation are calling for better batteries, fuel cells, and other devices. Appropriate energy storage device design is increasingly regarded as a necessary strategy to enhance next-generation power/energy supply, considering its potential advantages of low cost, environmental friendliness, high energy/power density, intrinsic safety, long service life, and fast-charging capability. Moreover, investigating the failure mechanism in batteries leads to the design of advanced energy storage devices, revolutionizing the existing manufacturing process. Furthermore, failure diagnosis is still far from meeting the standards of mass production due to several technical issues, including limited information, low calculation speed, and inefficient algorithm structures. Therefore, artificial intelligence-based further diagnosis is necessary to address the above issues. This Special Issue aims to reveal the failure mechanisms of energy storage devices, enhancing their overall performance from power, lifespan, and safety perspectives, unlocking artificial intelligence-based diagnosis methods, and providing guidance for the design of next-generation clean energy storage devices.
Dr. Siqi Chen
Dr. Dongxu Ouyang
Dr. Wei Tang
Dr. Ranjun Huang
Guest Editors
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Keywords
- clean energy storage device
- failure mechanism
- failure diagnosis
- operational performance enhancement
- safety enhancement
- long service life
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