A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization problem.The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state.The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,respectively.The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.Therefore,two objective functions are being used to achieve these objectives.The first refers to a design with high power output and efficiency.The second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is needed.The EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality constraints.The simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed algorithms.This paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem.

State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Deep Neural Network

It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.