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Abstract Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

Abstract Metal‐sulfur batteries have received intensive research attention owing to their potential to achieve higher energy density and lower cost than conventional Li‐ion batteries. However, metal‐sulfur batteries suffer from a fundamental challenge, the shuttle effect, the crossover of soluble reaction intermediates polysulfide leading to low efficiency and poor cycle life. Electrolyte design becomes the center of the sulfur redox chemistry since it dictates the properties of the soluble polysulfide intermediates in metal‐sulfur batteries. Here, we discuss the influence of electrolytes on sulfur reactions and cell performance, focusing on the polysulfide chemistry including polysulfide solubility and metal sulfide deposition. Based on the extensive analysis of literature, we highlight the design requirement of electrolytes to enable optimized sulfur reaction kinetic and realize high‐performance metal‐sulfur batteries.