Abstract
In this paper we present an electric go-kart suitable for an instructional laboratory in electric drives. An overview of propulsion system design, power conversion structure and control is presented. A three-phase squirrel-cage induction motor is used as propulsion system. The motor is controlled at different operating conditions by means of a simple scalar control using a low cost controller board developed for light electric vehicles used in local areas. The prototype has been designed specifically to meet the requirement of low cost and it contains all of the active functions required to implement the control of the go-kart.

Abstract
A new FPGA based platform is presented for controlling a Multi-Motor Electric Vehicle (EV). By exploring the FPGA parallel processing capabilities, two induction motor controllers, based on Field Orientation Control and Space Vector Modulation techniques, were merged in a single and compact chip. Implementation issues related with the limited number of dedicated multipliers were overcome using an efficient computational block, based on resource sharing strategy. The developed IP Cores were carefully optimized to fit in a low cost XC3S1000. Experimental results, obtained with a multi-motor EV prototype, demonstrate the proper operation of the proposed propulsion system.

Abstract
This paper evaluates parameter identification of induction motor (IM) using two different methods. The proposed methods provide an accurate estimation on the parameters for the IM steady state model. Both algorithms were tested with real data and then used to estimate the parameters of the motor. The disturbances in the acquired signals were reduced using some signal processing techniques. The processed signals were then applied in both identification procedures. The first method is based on optimization by nonlinear least squares algorithm that permits establishing convergence constrains, avoiding impossible physical results. The second is an indirect method that allows keeping the physical meaning of the continuous time parameters when converting to discrete time domain. The effectiveness of the proposed methods is verified by experimental tests and results of the methods are discussed.

Abstract
This paper presents a comparative study between a new approach for robust speed estimation in induction motor sensorless control, using a reduced order Extended Kalman Filter (EKF), and the one performed by the full order EKF. The new EKF algorithm uses a reduced order state-space model that is discretized in a particular and innovative way. In this case only the rotor flux components are estimated, besides the rotor speed, while the full order EKF also estimates stator current components. This new approach strongly reduces the execution time and simplifies the tuning of covariance matrices. The performance of speed estimation using both EKF techniques is compared with respect to computation effort, tuning of the algorithms, speed range including low speeds, load torque conditions and robustness relatively to motor parameter sensitivity.

Abstract
In spite of several technical advances made in recent years by the automotive industry, the driver's behaviour still influences significantly the overall fuel consumption. With the rise of smartphones adoption, there are also new opportunities to increase the awareness to this issue. The main aim of this paper is to present a new smartphone application that will help drivers reduce the fuel consumption of their vehicles. This is accomplished by using the smartphone's sensors and the vehicle state to detect the driving pattern and suggest new behaviours in real time that will lead to a more efficient driving experience. The preliminary results show the potential for significant energy savings and their relevance for changing the drivers' behaviour.

Abstract
This paper presents a new control chip design, based on Field Programmable Gate Array (FPGA) technology, for multi-motor electric vehicles. The control chip builds around a reusable intellectual property (IP) core, named Propulsion Control System (PCS); which features motor control functions with field orientation methods, and energy loss minimization of induction motors. To reduce the cost, implementation issues related with the limited number of dedicated multipliers were overcome using an efficient computational block, based on resource sharing strategy. Due to the parallel processing offered by FPGAs, the resulting implementation can be effortlessly adapted to different electric vehicles topologies, like single or multi-motor drive. As proof of concept, two prototypes with single and multi-motor configurations were developed with the control chip design implemented in a low cost Xilinx Spartan 3 FPGA. Experimental verification of the energy loss minimization algorithm is provided, showing considerable energy savings (>15%) in low speed conditions and improving the electric vehicle range per charge.