Metaheuristic Optimization and Artificial Intelligence for Lithium-Ion Battery Modelling, Parameter Identification, and State Estimation: A Critical Review
- 1Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
- 2Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
- 3Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Abstract
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.
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BibTeX
@article{nassef2026metaheuristic,
title = {{Metaheuristic Optimization and Artificial Intelligence for Lithium-Ion Battery Modelling, Parameter Identification, and State Estimation: A Critical Review}},
author = {Ahmed M. Nassef and Hossam Magdy Balaha and Mostafa A. Elhosseini},
journal = {Batteries},
year = {2026},
month = nov,
doi = {10.3390/batteries12110451},
url = {https://www.mdpi.com/2313-0105/12/11/451},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Metaheuristic Optimization and Artificial Intelligence for Lithium-Ion Battery Modelling, Parameter Identification, and State Estimation: A Critical Review
AU - Ahmed M. Nassef
AU - Hossam Magdy Balaha
AU - Mostafa A. Elhosseini
PY - 2026
DA - 2026/11/02
JO - Batteries
DO - 10.3390/batteries12110451
UR - https://www.mdpi.com/2313-0105/12/11/451
AB - 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.
SN - 2313-0105
ER -