Browsing by Author "Prieto, P."
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Item Geo-Fence Based Route Tracking Diagnosis Strategy for Energy Prediction Strategies Applied to EV(IEEE, 2019-10) Prieto, P.; Trancho, E.; Arteta, B.; Parra, A.; Coupeau, A.; Cagigas, D.; Ibarra, E.; POWERTRAIN; Tecnalia Research & InnovationNowadays, the shortage of energy and environmental pollution are considered as relevant problems due to the high amount of traditional automotive vehicles with internal combustion engines (ICEs). Electric vehicle (EV) is one of the solutions to localize the energy source and the best choice for saving energy and provide zero emission vehicles. However, their main drawback when compared to conventional vehicles is their limited energy storage capacity, resulting in poor driving ranges. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of EV. In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation, Driver in the Loop (DiL) test and experimental tests.Item A practical approach to HFI based sensorless control of PM-assisted synchronous reluctance machines applied to EVs and HEVs(IEEE, 2017-12-18) Trancho, E.; Ibarra, E.; Arias, A.; Kortabarria, I.; Prieto, P.; POWERTRAINSensorless control is a promising alternative for controlling Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) propulsion systems without the need of complex devices, such as resolvers or encoders. As the usage of a physical sensor is avoided, this allows significant cost reductions of the drive, and the reliability of the system is also improved. EVs require an operation range from standstill to high speeds. At low speeds, the back-EMF of the electric machine is low, and signal injection techniques are required in order to estimate the position and speed of the machine. This paper presents practical implementation details of the High Frequency Injection (HFI) technique, giving special attention to signal processing, offset compensation due to filtering delays and robust speed estimation. The approach is validated in an automotive Permanent Magnet Assisted Synchronous Reluctance Machine (PM-assisted SynRM) of 51 kW.Item Route tracking diagnosis algorithm for EV energy prediction strategies(2019) Prieto, P.; Trancho, Elena; Arteta, B.; Parra, A.; Coupeau, A.; Cagigas, D.; Ibarra, E.Current pollution issues generated by internal com bustion engine (ICE) based vehicles have lead to their progressive introduction of electrified transport systems. However, their main drawback is their poor autonomy when compared to conventional vehicles. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of electric vehicles (EV). In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a geo-fence based route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming Interface) to calculate the vehicles reference route. Additionally, a Global Positioning System (GPS) device is used to monitor the real vehicle position. The proposed strategy is validated throughout simulation and experimental tests.Item Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles(2018-08-15) Trancho, E.; Ibarra, E.; Arias, A.; Kortabarria, I.; Prieto, P.; Martínez de Alegría, I.; Andreu, J.; López, I.; POWERTRAINSensorless control of Electric Vehicle (EV) drives is considered to be an effective approach to improve system reliability and to reduce component costs. In this paper, relevant aspects relating to the sensorless operation of EVs are reported. As an initial contribution, a hybrid sensorless control algorithm is presented that is suitable for a variety of synchronous machines. The proposed method is simple to implement and its relatively low computational cost is a desirable feature for automotive microprocessors with limited computational capabilities. An experimental validation of the proposal is performed on a full-scale automotive grade platform housing a 51 kW Permanent Magnet assisted Synchronous Reluctance Machine (PM-assisted SynRM). Due to the operational requirements of EVs, both the strategy presented in this paper and other hybrid sensorless control strategies rely on High Frequency Injection (HFI) techniques, to determine the rotor position at standstill and at low speeds. The introduction of additional high frequency perturbations increases the power losses, thereby reducing the overall efficiency of the drive. Hence, a second contribution of this work is a simulation platform for the characterization of power losses in both synchronous machines and a Voltage Source Inverters (VSI). Finally, as a third contribution and considering the central concerns of efficiency and autonomy in EV applications, the impact of power losses are analyzed. The operational requirements of High Frequency Injection (HFI) are experimentally obtained and, using state-of-the-art digital simulation, a detailed loss analysis is performed during real automotive driving cycles. Based on the results, practical considerations are presented in the conclusions relating to EV sensorless control.