SOC-agnostic rapid state-of-health estimation of lithium-ion batteries

Abstract

The rapid growth of retired electric vehicle batteries presents a major opportunity for low-cost stationary storage through second-life use, but current repurposing standards rely on lengthy diagnostic tests that limit throughput. Fast and reliable screening methods are therefore needed to assess suitability for a second life before full recertification. This work investigates whether a short-duration (∼ 100 s) direct-current pulse voltage response contains sufficient information for such rapid screening. We address four questions: (1) how accurately state of health (SOH) can be inferred from a short-duration pulse sequence; (2) how pulse-derived features vary with state of charge (SOC) and SOH across chemistries; (3) whether physics-informed equivalent circuit model (ECM) parameters improve extrapolation across SOC; and (4) whether lightweight, SOC-agnostic models can provide practically useful accuracy and be deployed on embedded hardware. We develop a framework that extracts both statistical and physics-informed ECM features from the pulse response, characterizes their dependence on SOC and chemistry, and trains compact ElasticNet-based ensembles for LFP and NMC cells. Feature-quality analysis reveals when ECM parameters provide complementary diagnostic information and when statistical features alone suffice. Across both chemistries, we quantify the accuracy of SOH estimation under matched and mismatched SOC conditions, establishing the regimes in which cross-SOC generalization is feasible. Finally, we deploy and experimentally validate the LFP model on microcontroller-based hardware, demonstrating a practical pathway for rapid, low-cost second-life battery screening that reduces reliance on traditional, multi-hour tests.