From state estimation to active intelligence: safety-aware battery management for electric vehicles

Abstract

Battery management systems (BMSs) are increasingly required to convert incomplete electrical and thermal measurements into safe control actions rather than merely report battery states. This Mini Review focuses on two deployment questions that connect electrochemistry with control: how electrochemical states can be made observable with affordable diagnostics, and how charging, thermal, balancing, and health objectives can be arbitrated when their constraints conflict. We compare physics-based, equivalent-circuit, and data-driven representations using reported quantitative benchmarks, and show why cross-dataset accuracy alone is insufficient evidence of deployability. Particular attention is given to electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT), galvanostatic intermittent titration technique (GITT), and active pulse diagnostics as routes for linking measurable responses to interfacial kinetics, resistance growth, and transport limitations. A layered vehicle–edge–cloud architecture is proposed in which a deterministic safety supervisor enforces voltage, temperature, lithium-plating, power, imbalance, and uncertainty limits before performance optimization. The same framework is then examined for solid-state, lithium–sulfur, and sodium-ion batteries, where pressure, observability, and chemistry-specific calibration introduce new constraints. We conclude with a testable short-, mid-, and long-term roadmap centered on uncertainty calibration, pack-level validation, electrochemical observability, version-controlled learning, and safety-case evidence.