REVIEW

AI-driven next-generation lithium-ion battery design automation (BDA) software

¹ School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
² EACOMP, Shenzhen 518000, China
³ Fujian Science and Technology Innovation Laboratory for Energy Devices of China (21C-LAB), Ningde 352100, China
⁴ CATL Hong Kong Research Institute, Hong Kong 999077, China
⁵ Laboratory of Computational Materials Physics, Department of Physics, Institute of Condensed Matter, Jiangxi Normal University, Nanchang 330022, China

^* Corresponding authors (emails: genmingl@pku.edu.cn (Genming Lai); zuoyunxing@eacomp.com (Yunxing Zuo); zhengjx@pkusz.edu.cn (Jiaxin Zheng); GongJD@catl.com (Jiadong Gong); xubo@catl-21c.com (Bo Xu); ouyangcy@catl.com (Chuying Ouyang))

Received: 30 September 2025
Revised: 29 October 2025
Accepted: 3 November 2025

Abstract

This review presents battery design automation (BDA) as a transformative artificial intelligence (AI)-driven paradigm for the next-generation lithium-ion battery research and development. Addressing the intricacy of the problems and challenges in developing lithium-ion batteries with better performance, which are cross-scale, long-process, and multi-factor, BDA integrates multi-scale simulations and artificial intelligence into a unified platform. It ranges from atomic-scale material screening to system-level performance prediction. By bridging the gap between scientific innovation and industrial applications, BDA facilitates the development of lithium-ion battery, enhancing its efficiency, safety, and energy density. The paper outlines BDA’s architecture, core technologies, current progress, and future challenges, highlighting its potential to revolutionize the battery design process and strengthen the pivotal role of lithium-ion battery in energy storage technology.