Key result
NRBO algorithm outperforms SCA, HHO, and SSA for equivalent circuit model parameter identification accuracy.
Why the study?
Precise estimation of equivalent circuit model parameters is essential for improving lithium-ion battery predictive capability and enhancing electric vehicle battery management systems.
The NRBO algorithm offers superior accuracy for lithium-ion battery parameter estimation compared to other state-of-the-art metaheuristic algorithms, potentially enhancing battery management systems in electric vehicles.
May advance lithium-ion battery modeling accuracy for EVs; leaves open prospective BMS validation.
Precise estimation of the equivalent circuit model (ECM) parameters is essential for improving the predictive capability of lithium-ion battery (LiB) models and enhancing the performance of battery management systems (BMSs) in electric vehicles. This study proposes an artificial intelligence (AI)-driven methodology for ECM parameter identification based on the Newton-Raphson-Based Optimizer (NRBO) algorithm. The proposed method minimizes the deviation between simulated and experimental terminal voltages, achieving high estimation accuracy. The NRBO’s effectiveness is validated using real-world data and benchmarked against state-of-the-art metaheuristic algorithms, including the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA). Comparative results demonstrate that NRBO provides superior accuracy, underscoring its potential as an efficient and robust optimization framework for LiB parameter estimation and advanced BMS applications.
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Abouomar et al. (2026) studied Lithium-ion battery (LiB) parameter estimation. Newton-Raphson-Based Optimizer (NRBO) algorithm vs. Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA) was evaluated on Deviation between simulated and experimental terminal voltages. The Newton-Raphson-Based Optimizer (NRBO) algorithm provided superior accuracy for equivalent circuit model parameter identification compared to SCA, HHO, and SSA.
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