Abstract
In this paper, an artificial hummingbird optimization technique (AHOT), a novel bio-inspired meta-heuristic algorithm, is developed to identify the unknown parameters of Li-Ion batteries that are usually used in electric vehicles. The AHOT simulates the unique flying abilities and clever foraging tactics of hummingbirds in the wild. Three types of flying talents used in foraging tactics are modeled: axial, diagonal, and omnidirectional flights. In addition, directed, territorial, and migrant foraging methods are performed, and a visiting table is built to mimic hummingbirds' memory function for food sources. The AHOT is proposed to validate the objective function and standard deviation error on the dynamic model of Li-Ion batteries. Moreover, the AHOT findings are contrasted to newly established techniques that are African Vultures Optimizer, Jellyfish Search Optimizer, Tuna Swarm Optimizer, Grey Wolf Optimizer, Arithmetic Optimizer, and Heap-based Optimizer to demonstrate the effectiveness and efficiency of the proposed technique. The simulation investigations are combined with experimental applications on the 40 Ah Kokam Li-Ion batteries and the ARTEMIS driving cycle pattern. The statistical results show that the proposed AHOT has excellent characteristics compared to other algorithms as its obtained fitness function yields the lowest value. For minimum, mean, maximum, and standard deviation values, the AHOT achieves the lowest indices of 0.007657, 0.009266, 0.012265, and 0.001671. The suggested AHOT exhibits a substantial precision and outperforms other strategies for the ARTEMIS cycle because it produces the lowest objective basis of 0.004467.