PERFORMANCE SIMULATION OF VEHICLES EQUIPPED WITH TRACTION DRIVE CVTS

A computer model for the performance simulation of vehicles equipped with traction drive continuously variable transmission （CVT） is presented. The model integrates the traction drive CVT subsystem into an existing overall vehicle system. The characteristics of engine output torque are formulated using neural networks, and torque converter is modeled using lookup tables. Component inputs and outputs are coupled in the dynamic equations and interfaces in the powertrain system. The model simulation can provide evaluation of vehicle performance in drivability, fuel economy and emission levels for various drive ranges prior to the prototyping of the vehicle. As a design tool, the model assists engineers in understanding the effect ofpowertrain components on vehicle performance and making decisions in the selection of key design parameters. The model is implemented in the MATLAB/Simulink environment. The performance simulation of a test vehicle is included as a numerical example to illustrate the effectiveness of the model.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.