World Electric Vehicle Journal

Lithium-ion battery management systems (BMSs) for electric vehicles (EVs) require equivalent circuit models (ECMs) that balance prediction accuracy with identifiability, validation robustness, sensing requirements, and embedded computational cost. This PRISMA 2020-oriented systematic review evaluates recent ECM evidence for EV-oriented BMS applications and develops a function-driven model-selection framework. Scopus and Web of Science were searched using database-specific 2020–2025 publication-year filters. Of 721 identified records, 497 were unique, 478 full texts were retrieved, and 256 studies were included; 230 were original research studies and 26 were reviews. Among the 230 original research studies, experimental evidence was documented in 219 (95.2%), whereas 89 (38.7%) were rated sufficient for validation-data independence. These results show that terminal-voltage accuracy alone is insufficient to establish ECM deployability. The review therefore introduces a minimum-sufficient-complexity framework that links the target BMS function to model structure, parameterization, validation evidence, available sensors, and computational constraints. The resulting guidance supports selection of the least complex ECM that is both physically relevant to the target BMS decision and adequately identifiable and validated.

Terahertz metamaterial sensors offer potential for non-contact monitoring of electric vehicle battery-related films. This study proposes a dual-frequency self-referenced terahertz metamaterial sensor based on a metal–dielectric–metal structure comprising a split-ring resonator, an I-shaped resonator, a TOPAS dielectric layer, and a continuous metal reflector. Without a loaded film, the sensor exhibits near-perfect absorption peaks at 0.8354 and 1.3250 THz, with absorptivities of 99.95% and 99.99%, respectively. Field and current analyses identify the low-frequency I-shaped-resonator mode as the reference channel and the high-frequency split-ring-resonator mode as the sensing channel. A film thickness of 4 μm was selected from the thickness-response analysis for refractive-index sensing. For equivalent refractive indices of 1.30–1.60, the low- and high-frequency peak sensitivities are 96.43 and 272.57 GHz/RIU, respectively. The dual-peak frequency interval results in a fitted sensitivity of 176.14 GHz/RIU and provides enhanced stability against common-mode perturbations rather than higher raw refractive-index sensitivity. Under ±3% and ±5% TOPAS-permittivity perturbations, the interval drift (1.8–25.2 GHz) is lower than the high-frequency peak drift (12.6–39.6 GHz), producing suppression ratios of 36.36–85.71%. The proposed numerical design provides a potential self-referenced sensing approach for equivalent dielectric perturbations in electric vehicle battery-related films.

Hybrid Electric Vehicles (HEVs) are recognized as one of the effective technologies for reducing fuel consumption and greenhouse gas emissions. However, selecting the most suitable battery technology requires balancing technical performance, manufacturing feasibility, customer expectations, resource security, and sustainability objectives. Existing studies primarily evaluate battery technologies from technical, economic, or environmental perspectives independently, with limited consideration of stakeholder conflicts and trade-offs. Therefore, this study develops a stakeholder-driven Fuzzy Analytic Hierarchy Process (FAHP) framework to evaluate Nickel–Metal Hydride (NiMH) and Lithium-Ion (LIB) batteries for HEV applications. The framework integrates three key stakeholder groups: final customers, vehicle manufacturers (OEMs), and society and policymakers, and evaluates battery technologies using four criteria: Customer Value, Technical and Commercial Performance, Production Feasibility, and Resource Security and Social Responsibility. The results indicate that OEMs are the most influential stakeholders (54.30%), followed by final customers (24.81%) and society and policymakers (20.89%). Overall, NiMH achieved the highest score (53.10%), outperforming LIB (46.90%) because of its superior production feasibility and resource security, despite LIB’s higher technical performance. Sensitivity analysis under equal-criteria and equal-stakeholder weighting scenarios confirmed the stability of the rankings.