Abstract
This article focuses on the energy-saving of each driving distance for battery electric vehicle (BEV) applications, by developing a more effective energy management strategy (EMS), under different driving cycles. Fuzzy logic control (FLC) is suggested to control the power management unit (PMU) for the battery management system (BMS) for BEV applications. The adaptive neural fuzzy inference system (ANFIS) is a modeling technique that is mainly based on data. Membership functions and FLC rules can be improved by simply training the ANFIS with real driving cycle data gathered from the MATLAB/SIMULINK program. Then, FLC console blocks are rewritten by enhanced membership functions by ANFIS traineeship. Two different driving cycles are chosen to check the improvement in the efficiency of this proposed system. The suggested control system is validated by simulation and comparison with the traditional proportional-integral (PI) control. The optimized FLC shows better energy-saving.
Highlights
The main cause of global warming is the pollution produced through exhaust emission from the traditional internal combustion engine (ICE), and cars today are a critical part of our lifetime
The energy management strategy achieves a promising performance for energy saving, and the control method achieves good power flow management in battery electric vehicle (BEV)
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Summary
The main cause of global warming is the pollution produced through exhaust emission from the traditional internal combustion engine (ICE), and cars today are a critical part of our lifetime. A comprehensive review is presented in [4], alongside the overall relationship between measured battery power, measured velocity, accelerated, and road gradient, to obtain specially created BEVs. In [22], the authors using a particle swarm optimization technique to perfectly control the power flow inter the power train and other car assistances for specific BEVs. Try to reduce energy consumption in the vehicle while at the same time keeping passengers comfortable, by offering some suggestions to the driver. The energy management strategy achieves a promising performance for energy saving, and the control method achieves good power flow management in BEVs. This article aims to improve the travel distance of an electric vehicle by identifying the optimal BEV powertrain configuration that reduces battery discharge without reducing the vehicle’s performance during standard driving cycles.
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