Leveraging Artificial Intelligence and Machine Learning for Designing Next-Generation Electrochemical Energy Storage and Conversion Devices

Dear Colleagues,

The quest for high-performance, safe, and economically viable electrochemical energy storage and conversion devices (EESCDs) stands as a pivotal endeavor in propelling the evolution of portable electronics, electric vehicles, and large-scale energy storage systems. In this pursuit, the meticulous engineering of EESCD materials across scales—from the micro to the macroscale—is imperative to enhance performance, bolster safety measures, and mitigate overall costs. Furthermore, the precise engineering of operating parameters is essential to maximize battery lifecycle. Equally crucial is the accurate assessment of the state of health (SOH) and state of charge (SOC) during device operation, enabling the timely detection of cell degradation and facilitating optimal operation through advanced device management systems (DMSs).

The integration of artificial intelligence (AI) and machine learning (ML) emerges as a transformative force in expediting material discovery, optimizing operational parameters, and estimating the SOH to extend the lifecycle of EESCDs. Recognizing the pivotal role of AI/ML in shaping next-generation EESCD architectures, this Special Issue is dedicated to showcasing novel and pioneering approaches rooted in AI/ML for material design, operational parameter engineering, and SOH assessment in prolonged operational scenarios.

We cordially invite submissions of original research contributions and critical review papers from leading and emerging research groups alike.

Dr. Yasser Ashraf Gandomi
Guest Editor

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