Browsing by Author "Moreno de la Carrera, Jorge Alejandro"

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    Design, construction and performance of a buck-boost converter for an ultracapacitor-based auxiliary energy system for electric vehicles
    (IEEE, 2003) Ortuzar Dworsky, Micah Etan; Dixon Rojas, Juan; Moreno de la Carrera, Jorge Alejandro
    This paper describes step by step the process of designing, constructing and testing a bidirectional buck-boost converter. This converter is conceiving to be used as a controlled energy-transfer-equipment between the main energy source of an electric vehicle (a battery pack in this case) and an auxiliary energy system based on ultracapacitors. The converter is able to transfer energy in both directions, at rates of more than 40 kW. The battery pack's nominal voltage is 330 V, while the ultracapacitor's voltage depends on their state of charge (SOC), ranging from 100 V to 300 V. Equations governing current transfer and current ripple are analyzed. These equations will be used as guidelines for the control system design and smoothing inductor size requirement. The topology used is a buck-boost configuration. Special care had to be taken in designing the smoothing inductor and managing thermal loses, for these are critical to the overall performance. The inductor constructed, rating l.5 mH, is capable of transferring 200 A for several minutes with low loses and no core saturation (air core was used). A special water-cooled heatsink was designed and constructed, with a very low volume of less than 900 cc and a thermal resistance of less than 0.011/spl deg/C/W. The control system was implemented on a TMS320F241 DSP from Texas Instruments, which consists in two control loops. The first one controls the converter's current, using as a reference the value obtained from the second loop, which controls the ultracapacitors state of charge (SOC). Criteria ruling this second loop are not discussed in this paper. Finally, some experimental results of the overall system are displayed.
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    Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks
    (2006) Moreno de la Carrera, Jorge Alejandro; Ortúzar Dworsky, Micah Etan; Dixon Rojas, Juan
    A very efficient energy-management system for hybrid electric vehicles (HEVs), using neural networks (NNs), was developed and tested. The system minimizes the energy requirement of the vehicle and can work with different primary power sources like fuel cells, microturbines, zinc-air batteries, or other power supplies with a poor ability to recover energy from a regenerative braking, or with a scarce power capacity for a fast acceleration. The experimental HEV uses lead-acid batteries, an ultracapacitor (UCAP) bank, and a brushless dc motor with nominal power of 32 kW, and a peak power of 53 kW. The digital signal processor (DSP) control system measures and stores the following parameters: primary-source voltage, car speed, instantaneous currents in both terminals (primary source and UCAP), and actual voltage of the UCAP. When UCAPs were installed on the vehicle, the increase in range was around 5.3% in city tests. However, when optimal control with NN was used, this figure increased to 8.9%. The car used for this experiment is a Chevrolet light utility vehicle (LUV) truck, similar in shape and size to Chevrolet S-10, which was converted to an electric vehicle (EV) at the Universidad Catolica de Chile. Numerous experimental tests under different conditions are compared and discussed.