Metaheuristic Optimization and Artificial Intelligence for Lithium-Ion Battery Modelling, Parameter Identification, and State Estimation: A Critical Review

The validity of the models incorporated into battery management systems (BMSs) is crucial to the safe and effective use of lithium-ion batteries (LIBs), the predominant electrochemical energy storage technology for electric vehicles (EVs) and stationary storage. Over the past ten years, two methodological families have transformed battery modeling and diagnostics: artificial intelligence (AI) models that learn state estimators directly from data and metaheuristic optimization algorithms that recast parameter identification as a black box optimization problem. This paper critically and comprehensively analyzes both families. This paper first examines the primary battery model classes, integer-order and fractional-order equivalent circuit models (ECM/FO-ECMs) and electrochemical models, and the identifiability problems that underpin any identification process. Second, the paper presents a critical synthesis of reported accuracy trends across standard datasets and driving profiles, along with a taxonomy of metaheuristic algorithms for battery parameter identification, including evolutionary, swarm-based, physics-based, and bio-inspired approaches. Third, AI-based methods for estimating state-of-charge (SOC) and state-of-health (SOH) are analyzed. These methods range from simple machine learning to deep recurrent and attention-based architectures, with a focus on the new class of hybrid physics-informed methods (physics-informed neural networks and neural ordinary differential equations coupled with ECMs). Fourth, the synergy between the two approaches (AI-assisted optimization and metaheuristic tuning of AI estimators) is examined. In addition, this review addresses the “novel algorithm inflation” impacting the metaheuristic literature, the implications of the no-free-lunch theorem, the frequent lack of statistical validation and fair benchmarking, and the discrepancy between reported laboratory accuracy and real-time feasibility in embedded systems. Lastly, research perspectives are suggested, such as hardware-aware hybrid estimators, standardized open benchmarks, and chemistry-transferable identification (LFP, NMC, sodium-ion). The most practical route to scalable, field-ready battery management systems is to incorporate physical structures into artificial neural network designs, moving from strictly data-driven models to physics-informed hybrid frameworks.