The triadic evolution of energy and automation: Challenges, innovations, and future perspectives of lithium-sulfur batteries for next-generation AEVs

The shift from combustion-powered cars to Autonomous Electric Vehicles (AEVs) brings with it an “energy penalty”, as the power consumption of real-time processing of the sensor data and the AI edge computing, along with that of drive-by-wire systems, is far from negligible. This review examines evidence from 303 studies (2015–2026) retrieved from eight databases through a narrative literature review to systematically assess Lithium-Sulfur (Li-S) batteries as a high-energy-density alternative. Li-S batteries offer transformative benefits, including a theoretical specific energy of 2600 Wh/kg, low-cost S cathodes, and reduced supply chain dependence, driven by the triadic evolution among energy storage, automation, and electric mobility. Challenges to be addressed include: Polysulfide shuttle effect, low conductivity, lithium dendrite growth, and ∼80% volumetric expansion, which affect cycle life and coulombic efficiency. System-level solutions are needed for issues specific to AEs, such as pulsed-load degradation and the thermal management paradox. Performance has been improved by the recent development of the cathode architecture, the anode protection, the electrolyte optimization, as well as the solid-state Li-S batteries (SSLSBs). Limitations are mitigated with the integration strategies: AI-based smart (Battery Management System) BMS, thermal loop co-optimization, and a hybrid Li-S/supercapacitor system. Compared with Li-ion, Li-air, and other solid-state battery technologies, Li-S batteries could also offer higher energy density, higher pulse power, greater safety, and lower pack-level cost. The proposed commercial roadmap for the technology is aimed at phased trials (2026–2028), phased deployment of SSLSB (2028–2030), and commercial-scale packs with V2G (Vehicle-to-Grid) integration (2030+). Future research efforts should be directed towards automotive test profiles for AEV workloads, scale up solid-state Li-S architectures, and design smart BMS using deep learning.