Condition Monitoring and Diagnostics of Energy Storage Systems Components

Dear Colleagues,

Nowadays, the importance of energy storage has become paramount in different areas, such as the production and distribution of electric energy, portable tools and devices, electric vehicles (EVs), etc. Large scale energy storage also allows today’s electrical systems to run significantly more efficiently, thus meaning lower prices, less emissions, and more reliable power. The principle of energy storage systems is based on the use of alternative resources of energy, where they operate in most applications with electricity stored in batteries, fuel cells (FCs), capacitors, and supercapacitors (SCs). However, energy storage systems (ESSs) are currently facing some challenges because they need to be cost-competitive, compact, efficient, safe and reliable, take up little space, and be long-lasting. Periodic maintenance practices currently assess the future failures of energy storage systems components. Alternatively, in order to eliminate unintended failures, it is important to be able to diagnose the components' underlying degradation and to forecast the extent of unsatisfactory performance through an online real-time monitoring system. Furthermore, the hybridization of ESSs with advanced power electronic technologies has a major effect on the optimum use of power to lead advanced ESS technologies.

The aim of this Special Issue is to provide an opportunity for scientists, researchers, and practicing engineers to share and disseminate their latest discoveries and results in the aforementioned fields, indicating the future trends for condition monitoring and diagnostics of energy storage systems components.

Topics include, but are not limited to, the following research areas:

• Fault detection and fault tolerance in energy storage systems
• Battery-management systems (BMS)
• Life time diagnostics of capacitors and supercapacitors
• State-of-charge and state-of-health estimation
• Thermal performance of energy storage systems
• Emerging battery technologies
• Power electronics for energy storage systems
• Life cycle analysis
• Machine learning for the performance analysis, diagnosis, prognostics, and management of energy storage systems

Prof. Dr. Antonio J. Marques Cardoso
Dr. Khaled Laadjal
Guest Editors

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