Estimation and Prediction on State of Health of Electric Vehicle Battery Based on BP Neural Network

2021 ◽  
Author(s):  
Peng Wang ◽  
Xu Li
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Bp Neural Network ◽  
State Of Health ◽  
Estimation And Prediction ◽  
Electric Vehicle Battery

Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

2020 ◽  
Author(s):  
Sohel Anwar
Keyword(s):  
Electric Vehicle ◽  
Constant Current ◽  
Capacity Fade ◽  
State Of Health ◽  
Current Profile ◽  
Combined Model ◽  
Battery Model ◽  
Electric Vehicle Battery ◽  
Electrochemical Battery ◽  
Constant Current Charge

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

scholarly journals Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

2021 ◽  
pp. 173-180
Author(s):  
Huang Zongjian
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Electric Vehicle ◽  
Bp Neural Network ◽  
Switched Reluctance Motor ◽  
Network Parameter ◽  
Nonlinear Simulation ◽  
Switched Reluctance ◽  
Speed Regulation ◽  
Reluctance Motor

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

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