{'fraction_non_alphanumeric': 0.019815059445178335, 'fraction_numerical': 0.012549537648612946, 'mean_word_length': 5.338912133891213, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A hybrid technique based energy management in hybrid electric vehicle system

In this article, a novel hybrid method is proposed to optimally manage the energy for a hybrid electric vehicle system. The proposed technique is the joint execution of both the Kernel Wingsuit Flying Search Algorithm and Sea Lion Optimization Algorithm, hence it is called WF2SLOA. The main objective of the WF2SLOA method is integrated in the energy management system to split the torque between the engine and electric machine. During the WF2SLOA‐based energy management development, this article performs a parametric investigation on numerous main factors, such as state types and number of states, states and action discretization, exploration and exploitation, and learning experience selection. The proposed method is implemented in MATLAB/Simulink, and the performance is assessed with the existing methods. Consequently, the outcomes illustrate that the selection of the learning experience can diminish the fuel consumption of the vehicle. Furthermore, the states and action discretization study indicates the fuel consume of the vehicle diminishes as action discretization enhances while raising the states discretization is harmful to the fuel consume. The maximizing count of states also raises the economy of fuel. Thus, the simulation outcomes show that the performance of the proposed method is more efficient than the existing methods. The mean, median, and SD of the WF2SLOA method attains 1.5420, 1.5043, and 0.0509.