Abstract
The limited amount of energy stored on board of battery electric vehicles (BEV) spurs research activities in the field of efficiency optimization for electric drive train applications in order to achieve an enhanced mileage. In this work a control method for BEV applications with two drive trains (e.g., one at the front and one at the rear axle) is presented. Herein, a simple optimization algorithm is introduced enabling to operate the two drives with different torque values, depending on the instantaneous operation point, leading to a reduction of apparent power losses on board. Simulations on a virtual BEV yield a decrease in the cumulated energy consumptions during typical BEV operation, leading to an increase in the achievable mileage.
Highlights
In recent years, the need for zero emission transportation spurred the broad market introduction of battery electric vehicles (BEV)
The limited amount of energy stored on board of battery electric vehicles (BEV) spurs research activities in the field of efficiency optimization for electric drive train applications in order to achieve an enhanced mileage
The proposed approach to optimize the efficiency of power conversion of a battery electric vehicle is implemented in the control strategy of a virtual BEV in MATLAB/Simulink
Summary
The need for zero emission transportation spurred the broad market introduction of battery electric vehicles (BEV). One main drawback of battery powered vehicles is the very limited range, due to the very limited amount of energy stored in the battery. The efficiency of energy conversion of electric power trains is 2-3 times higher (η > 80%) than the efficiency of combustion engines (η = 20– 40%). An electric drive train substituting the combustion engine is one possible vehicle concept. During vehicle acceleration and even speed, the drive train components (power converters and electric motors) convert the chemical energy stored in the battery in mechanical work. From the literature several approaches are known referring to range prediction algorithms for HEV applications (e.g., in [6]) and for BEVs [7] incorporation track profile information. Zhang et al [8] presented methods for driving range prediction of BEVs considering
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