One of the most important environmental problems in large cities is the vehicular emission. Electric Vehicles (EVs) are a growing alternative for internal combustion engine (ICE) vehicles. Since this kind of vehicle has low autonomy yet, it is important to optimize energy consumption, for instance by planning a suitable infrastructure of battery recharge and/or battery-switch stations. This paper presents an architecture for EV simulation, important to analyze traffic flow, its dynamics and the performance when there are obstructions or intense traffic. There are several tools for traffic simulation, SUMO (Simulation of Urban MObility) is one of them. But none of the existing traffic simulators integrates models of EV that allow, for example, perform simulation studies regarding energy consumption. SUMO is a portable open source simulator with multi-modal traffic feature capabilities that permit the simulation of various types of vehicles. This work is an extension of the SUMO, two-dimensional (2D) vehicular simulation package. To allow the simulation of energy consumption of EV, two extensions were incorporated in SUMO: EV models and modeling of altitude, transforming SUMO into a three-dimensional (3D) simulator. The energy model effectiveness and correctness with 3D capabilities has been validated using two driving schedules (Urban Dynamometer Driving Schedule and Highway Fuel Economy Driving Schedule). This new tool will also support the study of better routes choice in 3D environment with EV aiming minimum energy consumption.

This paper describes the ISRobotCar, an experimental autonomous electric vehicle that integrates Robot Operating System (ROS) and several sensors such as IBEO laserscanner, Inertial Measurement Unit (IMU), RTK-GPS, vision cameras, and magnet detectors for magnetic guidance. The development of a power steering controller and of a fuzzy path-following controller are particularly addressed. Finally simulations and experimental results of autonomous path-following control are presented.

In this paper we present a multistage method applied in pedestrian detection using information from a LIDAR and a monocular-camera mounted on an electric vehicle driving in urban scenarios. The proposed method is a cascade of classifiers trained in two subsets of features, one with laser-based features and the other with a set of image-based features. A specific training approach was developed to adjust the cascade stages in order to enhance the classification performance. The proposed method differs from the conventional cascade regarding the way the selected samples are propagated through the cascade. Thus, the subsequent stages of the proposed cascade receive both negatives and positives from previous ones, relying on a decision margin process. Experiments were conducted in off-line mode, for a set of single component classifiers and for the proposed cascade technique. The results are compared in terms of classification performance metrics and ROC curves.

Electric vehicle (EV) powertrain dynamics and steady state parameters can only be acquired in controlled test conditions, what is not possible to obtain experimentally in real road traffic. This paper presents a test bench designed for modelling purposes of small EVs such as ISRobotCar [2]. The EV powertrain was tested to acquire the motor power driver, motor, gearbox and tire efficiencies. The experimental study covers the particularly interesting feature of EVs that consists on the regenerative braking capability. Tire slippage is also addressed. As main result, two efficiency maps are provided for ISRobotCar in traction and regenerative braking modes, respectively.