Facing the pursuit of global carbon neutrality goals and exponential energy consumption driven by industrialization and digital infrastructure, electrochemical energy storage technology (EEST) has emerged as a pivotal focus in the modern energy transition. Although a diversified ecosystem necessary to meet varying demands is constituted by lithium-ion batteries, sodium-ion batteries, and flow batteries, their development is severely constrained by the low efficiency of traditional material development cycles and inherent performance bottlenecks. Unlike previous reviews that primarily focused on isolated technical fields, this study presents a comprehensive, comparative review of the characteristics, costs, and performance of the mainstream electrochemical energy storage technologies. Furthermore, it is expounded how Artificial Intelligence (AI) overcomes the limitations of traditional material research and development modes by revolutionizing processes such as material design and identification. By conducting a comprehensive and in-depth examination of the specific challenges inherent in the current EEST and aligning with AI-enabled solutions, this review formulates forward-looking strategies for subsequent research endeavors. Consequently, it aims to foster the development of EEST under the conditions of high performance, low cost, and sustainability.
Qu et al. (Thu,) studied this question.
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