Analysis and optimization of energy efficiency for an electric vehicle with four independent drive in-wheel motors

Abstract

The independent driving ability of the four-wheel independent drive electric vehicles can be used to improve the energy efficiency of the vehicle. The composition and structure of a prototype were introduced in this article and the rapid prototype was applied to design the upper-level control system. Experimental data obtained from the bench tests and the chassis dynamometer tests were employed to generate the torque response performance and power efficiency map of the in-wheel motor. The control logic of the in-wheel motor was obtained based on the least squares method. Besides, a front and rear axle torque distribution method was proposed to reduce the energy consumption of vehicle, and the simulating test of the prototype was conducted based on the in-wheel motor efficiency map. Simulation analysis of vehicle energy consumption in vehicle driving cycle was carried out in ways of rear axle drive, average torque allocation, and optimized torque energy-saving allocation; the results show that the optimized torque energy-saving allocation strategy can decrease the energy consumption up to approximately 0.117 and 0.426 kJ per unit mileage comparing with the rear axle drive and average torque allocation.

Energy minimization for an electric bus using a genetic algorithm

“…For fully electric vehicles, however, only few methods have been proposed for generating optimized speed (or torque) profiles (see e.g. Dib et al [12], Rohkämper et al [13], and Jiang et al [14]) similar to those optimized for vehicles equipped with internal combustion engines.…”

“…Similar DP-based speed trajectory optimization approaches have been introduced for fully electric vehicles as well; see e.g. Dib et al [12], Jiang et al [14], Maamria et al [15], and Rios-Torres et al [16]. Energy-efficient driving strategies developed for fully electric vehicles and HEVs are typically considered as driving assistance systems that provide reference speed recommendations for a human driver to follow.…”

Advanced powertrain dynamic modelling and control for electrified vehicles

“…The dynamic model and efficacy maps of the in-wheel motors are established by using real experimental data from bench tests as well as chassis dynamometer tests, and the optimal torque distribution between front and rear axles is guaranteed by using a novel control scheme. 14 Apart from the research of ground vehicles, one paper titled 'Path planning for underwater glider under control constraint' introduces a novel optimal threedimensional path planning method for underwater vehicles, for example, an underwater glider, and is also selected in this Special Collection. The proposed path planning method is derived by using Pontryagin's minimum principle, which can result in a lower rate of energy consumption compared with traditional threedimensional Dubins path.…”

Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines

“…For the proper design, diagnosis, use and control of the EMs, many modeling algorithms have been developed, from classical [2,3] to more advanced ones, which are currently based on artificial intelligence methods [4][5][6]. At the EM design stage, the model is dedicated to optimizing both its structure and parameters [7,8]. In terms of diagnosis, the model is the basis for synthesizing the appropriate state examination algorithm [9][10][11], while in the case of their use and control, the model is necessary for making the right decisions related to relevant exploiting activities [12,13].…”