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Investigation of the Vehicle Application of Fuel Cell-Battery Hybrid Systems

All electric vehicles (AEV) are developed using the new technology; they can be separated in two groups as battery-operated electric vehicles (EV) and fuel cell vehicles (FCV). The subject of this study, fuel cell-battery operated hybrid vehicles, is about full electric hybrid vehicles (FEHV). EV and FCV have different advantages and disadvantages, therefore it is necessary to develop the FEHV vehicles. The fuel cell-battery hybrid systems are under investigation. In the literature, some automotive applications are reported as examples for fuel cell-battery hybrid powered systems. The light hybrid vehicles have been tested in terms of the system design, power management, road tests, and efficiency. All of the presented systems consist of a proton exchange membrane fuel cell system, battery pack, powertrain system, and a connection strategy for powering the traction of an electrical car. However, these are not perfect vehicles, they require further work to address major drawbacks with the connection as well, to increase the efficiency and management of the hybrid components (fuel cell, battery, supercapacitors, motor drive, and electrical systems), motor drive and electrical systems. Their tests and the demonstration results are satisfactory.

The multi-windings forward structure battery balancing

A structure for active battery balancing with using Multi-Windings Transformer is presented. Most of applications in electrical vehicles need smart voltage control and high effective battery systems. In generally, the battery stack systems consist of these three parts: management, safety and balancing. In these parts, balancing is the most significant part since the management demands to improve the cycle life, battery storage packs, and power capacity. This paper presents the real-time Multi-Windings Transformer balancing topology forward structure model, simulation, and real circuit implementation with using Texas Instruments Stellaris® Launchpad Evaluation Kit (EK-LM4F120XL) with Energia based coding. Also, this paper indicates the emphasis of using forward structure model of Multi-Windings Transformer balancing topology since we use forward structure converter for dimishing the extra energy loses and decrease cost of the system.

Embedded Face Detection and Recognition

The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras - of which the popularity has been increasing - is one step ahead. With sensors and Digital Signal Processors (DSPs), smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high- bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras - which are real-life applications of such methods - the widest use is on DSPs. In the present study, the Viola-Jones face detection method - which was reported to run faster on PCs - was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform). As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub- regions and from each sub-region the robust coefficients against disruptive elements - like face expression, illumination, etc. - were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis) and then employed for recognition. Thanks to its operational convenience, codes that were optimized for a DSP received a functional test after the computer simulation. In these functional tests, the face recognition system attained a 97.4% success rate on the most popular face database: the FRGC.The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on‐site and on‐time. At this point, the use of smart cameras ‐ of which the popularity has been increasing ‐ is one step ahead. With sensors and Digital Signal Processors (DSPs), smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image‐processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high‐ bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general‐purpose processors. In smart cameras ‐ which are real‐life applications of such methods ‐ the widest use is on DSPs. In the present study, the Viola‐Jones face detection method ‐ which was reported to run faster on PCs ‐ was optimized for DSPs; the face recognition method was combined with the developed sub‐region and mask‐based DCT (Discrete Cosine Transform). As the employed DSP is a fixed‐point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub‐ regions and from each sub‐region the robust coefficients against disruptive elements ‐ like face expression, illumination, etc. ‐ were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis) and then employed for recognition. Thanks to its operational convenience, codes that were optimized for a DSP received a functional test after the computer simulation. In these functional tests, the face recognition system attained a 97.4% success rate on the most popular face database: the FRGC.

Fuel cell powered hybrid system controlled by the maximum peak power tracking technique

In the present study a borohydride/peroxide fuel cell/Li-ion battery hybrid system was developed. Output power of DBPFC was controlled and kept constant by MPPT algorithm. The voltage ripples of the fuel cell were prevented. Moreover, Li-ion battery was charged with a stable power of direct borohydride/peroxide fuel cell.