Battery electric vehicles (BEVs) represent a possible sustainable solution for personal urban transportation. Presently, the most limiting characteristic of BEVs is their short range, mainly because of battery technology limitations. A proper design and control of the drivetrain, aimed at reducing the power losses and thus increasing BEV range, can contribute to make the electrification of urban transportation a convenient choice. This paper presents a simulation-based comparison of the energy efficiency performance of six drivetrain architectures for BEVs. Although many different drivetrain and transmission architectures have been proposed for BEVs, no literature was found regarding BEVs equipped with infinitely variable transmissions (IVTs). The analyzed drivetrain configurations are: single- (1G) and two-speed (2G) gear drives, half toroidal (HT) and full toroidal (FT) continuously variable transmissions (CVTs), and infinitely variable transmissions (IVTs) with two different types of internal power flow (IVT-I and IVT-II). An off-line procedure for determining the most efficient control action for each drivetrain configuration is proposed, which allows selecting the optimal speed ratio for each operating condition. The energy consumption of the BEVs is simulated along the UDC (Urban Driving Cycle) and Japanese 10-15 driving cycle, with a backward facing approach. In order to achieve the lowest energy consumption, a trade-off between high transmission efficiency and flexibility in terms of allowed speed ratios is required.

This paper presents a novel four-wheel-drive electric vehicle layout consisting of one on-board electric drivetrain per axle. Each drivetrain includes a simplified clutch-less two-speed transmission system and an open differential to transmit the torque to the wheels. This drivetrain layout allows eight different gear state combinations at the vehicle level, thus increasing the possibility of running the vehicle in a more energy-efficient state for the specific wheel torque demand and speed. To compensate for the torque gap during gearshifts, a “torque-fill” controller was developed that varies the motor torque on the axle not involved in the gearshift. Experimental tests show the effectiveness of the developed gearshift strategy extended with the torque-fill capability. Energy efficiency benefits are discussed by comparing the energy consumptions of the case study vehicle controlled through a constant front-to-total wheel torque distribution and conventional gearshift maps, and the same vehicle with an energy management system based on an offline optimization. Results demonstrate that the more advanced controller brings a significant reduction of the energy consumption at constant speed and along different driving cycles.

Extensive literature discusses traction control system designs for electric vehicles. In general, the proposed control structures do not include consideration of the actuation dynamics, which are especially important for vehicles with on-board drivetrains, usually characterized by significant torsional dynamics of the halfshafts. This paper compares the performance of a selection of traction controllers from the literature, with that of PID and H _∞ control structures specifically designed for on-board electric drivetrains. The analysis in the frequency domain and the simulation results in the time domain show the significant performance improvement provided by the control system designs considering the actuation dynamics.

In the United States, the DARPA (Defense Advanced Research Projects Agency) Grand Challenges [Thrun et al. (J. Field Robot. 23(9):661–692, 2006); Urmson et al. (J. Field Robot. 25(8):425–466, 2008); Urmson et al. (J. Field Robot. 23(8):467–508, 2006); Campbell (Steering Control of an Autonomous Ground Vehicle with Application to the DARPA Urban Challenge. Massachusetts Institute of Technology, 2007)] demonstrated that autonomous driving can be achieved through vision and sensor systems capable of detecting and interpreting the vehicle operating environment, rather than through autonomous driving options relying on the infrastructure (e.g., through magnets installed on the road surface to indicate the lanes), or vehicle-to-vehicle or vehicle-to-infrastructure communication systems. The latter options are very useful to further enhance the performance, safety, and energy efficiency, but are not strictly required. In a typical automated driving system, a reference path and a reference speed profile are defined based on the sensed environment. At a lower level of the control system hierarchy, a path tracking controller is responsible for calculating the steering angle for achieving the reference trajectory, while a speed controller determines the wheel torque demand for tracking the reference speed. Speed control implementations are already quite common in production vehicles equipped with cruise control and adaptive cruise control systems. Hence, the core element of novelty for autonomous driving in the area of vehicle control is represented by the steering control function for path tracking. Different steering-based path tracking algorithms, ranging from geometrical methods to model-predictive controllers, are presented and discussed in this contribution, together with the expected future research and vehicle implementation directions in the field.