DETECTION PROCESS OF ENERGY LOSS IN ELECTRIC RAILWAY VEHICLES

The paper deals with the detection process of energy loss in electric railway hauling vehicles. The importance of efficient energy use in railways and cost-effective rail transport tendency toward regenerative braking energy are considered. In addition, the current situation and improvement opportunities to achieve efficient energy use are examined. Seven measurement series were performed with scheduled Railjet trains between Hegyeshalom and Győr railway stations in Hungary. This railway section is related to the Hungarian State Railways' No. 1 main railway line (between Budapest-Kelenföld and Hegyeshalom state board), which is a part of the international railway line between Budapest and Vienna (capitals of Hungary and Austria, respectively). This double-track, electrified railway line with traditional ballasted superstructures and continuously welded rail tracks is important due to the international passenger and freight transport between Germany, Austria, and Hungary. The value of the regenerative braking energy can be even 20-30% of the total consumed energy. This quite enormous untapped energy can be used for several aims, e.g., for comfort energy demand (air conditioning, heating-cooling, lighting, etc.) or energy-intensive starts. The article also investigates the optimization of regenerative braking energy by seeking the energy-waste locations and the reasons for the significant consumption. The train operator's driving style and habit have been identified as one of the main reasons. Furthermore, train driver assistance systems are recommended to save energy, which is planned for future research

Kísérleti célú duális funkciójú napelem cella és panel vizsgáló berendezés: Dual Function Solar Cell and Panel Testing Equipment for Experimental Purposes / Echipament pentru analiza experimentală a celulelor și panourilor solare cu funcție duală

In our paper two dual-function solar panel testing equipment will be introduce which are capable to examine solar panels in a complex way. Besides that the result of the measurements both with natural and artificial ligth will be introduced. The aim of our project is to reduce the temperature on the surface of the solar panels and thus improve the daily energy-balance. Kivonat A cikkünkben bemutatunk két kísérleti célú duális funkciójú napelem vizsgáló berendezést, amely képes a napelem cellák, panelek komplex vizsgálatára. A napelemcella vizsgáló berendezés képes növelni vagy csökkenteni a vizsgált cella hőmérsékletét, változtatni a fény beesési szögét és a beérkező fényforrás részleges hullámhosszúságát. A berendezés figyeli a hullámhossz tartományt spektrométerrel, a napelemcella villamos paramétereit a terhelés és a hőmérséklet függvényében. A duális funkciójú panelvizsgáló berendezés, elsősorban szabadtéri, természetes fénynél való mérésekre lett tervezve. A berendezés könnyen mozgatható, mobil rendszer, amely önhordóan tartalmaz minden mérő, villamos, informatikai és folyadékellátó egységet. Különböző folyadék fényszűrő rendszerek tesztelésére alkalmas. A publikációban tovább ismertetjük a mesterséges és természetes fényforrásoknál végzett mérések eredményeit. A projektünk célja az infra sugarakból származó hő elszállítása napelemek felületéről és ezzel a napi energiamérleg javítása

Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data

Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in design, capacity, and chemical composition. In addition, the battery monitoring systems of the various vehicles are also diverse, so communication across the board is not available or can only be achieved with significant difficulty. Hence, unique type-dependent data queries and filtering are necessary in most cases. In this paper, a Volkswagen e-Golf electric vehicle is investigated; communication with the vehicle was implemented via an onboard diagnostic port (so-called OBD), and the data stream was recorded. The goal of the research is principally to filter out, identify, and localize defective/weak battery cells. Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels. The methodology demonstrated in this article uses a fault diagnosis technique based on voltage abnormalities. In addition, it employs a hybrid algorithm that executes calculations on measurement and recorded data. In the evaluation, a status line comprising three different categories was obtained by parametrizing and prioritizing (weighting) the individual measured values. It allows the cells to be divided into the categories green (adequate region), yellow (to be monitored), and red (possible error). In addition, several querying strategies were developed accordingly to clarify and validate the measurement results. The several strategies were examined individually and analyzed for their strengths and weaknesses. Based on the results, a data collection, processing, and evaluation strategy for an electric vehicle battery system have been developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery

Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data

Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in design, capacity, and chemical composition. In addition, the battery monitoring systems of the various vehicles are also diverse, so communication across the board is not available or can only be achieved with significant difficulty. Hence, unique type-dependent data queries and filtering are necessary in most cases. In this paper, a Volkswagen e-Golf electric vehicle is investigated; communication with the vehicle was implemented via an onboard diagnostic port (so-called OBD), and the data stream was recorded. The goal of the research is principally to filter out, identify, and localize defective/weak battery cells. Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels. The methodology demonstrated in this article uses a fault diagnosis technique based on voltage abnormalities. In addition, it employs a hybrid algorithm that executes calculations on measurement and recorded data. In the evaluation, a status line comprising three different categories was obtained by parametrizing and prioritizing (weighting) the individual measured values. It allows the cells to be divided into the categories green (adequate region), yellow (to be monitored), and red (possible error). In addition, several querying strategies were developed accordingly to clarify and validate the measurement results. The several strategies were examined individually and analyzed for their strengths and weaknesses. Based on the results, a data collection, processing, and evaluation strategy for an electric vehicle battery system have been developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption

A Risk Assessment Technique for Energy-Efficient Drones to Support Pilots and Ensure Safe Flying

Unmanned Aerial Vehicles, also known as UAVs, play an increasingly important part in daily life. However, the ever-increasing number of UAVs pose an ever-increasing threat to the transportation infrastructure. Despite their precision and general efficiency, infrastructural-scale Unmanned Aerial Systems (UASs) have a disadvantage regarding their capability of being implanted in the ecosystem. There are several reasons for this, but the primary bottleneck is that their systems are not transparent to society and have very complicated processes. As a result, the authors decided to investigate the functional properties of UASs and make improvements to those properties. Throughout the study, the authors’ primary focus was on analysis, which boosts productivity and ensures a significant level of safety for routine flights. The amount of power that a UAV uses depends on several variables, including the amount of power that its individual components require, the temperature of its surroundings, and the condition of the battery that it is powered by. Therefore, critical parameters and interdependencies are taken into account in the risk assessment strategy for energy-efficient Unmanned Aerial Vehicles (UAVs). In the case of UAVs, the algorithm performs a risk calculation before take-off to estimate the amount of risk that can be associated with the given flight time when using the provided battery. On the one hand, several instances of the pre-take-off state and how its parameters interact are investigated. On the other hand, they demonstrate the calculation of the risk while in flight, which is based on actual flight data

Vibration Diagnostic Methods of Automatic Transmission Service Requirement Prediction

Automatic transmission is a key factor for autonomous driving. The transmission condition is highly affected by the quality and quantity of transmission oil in the system. However, the oil condition is not monitored in the system, and the oil change interval and method are still a subject of discussion. This paper analyzes the effects of oil changes in automatic transmissions. The measurements were carried out before and after the oil change with the same external conditions. With the vibration measurement method, data can be collected without disassembling the parts and during operational conditions. Furthermore, time- and frequency-based analyses were conducted to compare different transmissions’ operations. The results have shown that the effect of oil degradation is measurable on the amplitude of the signals and, therefore, predictable with vibration diagnostics. During the evaluation, the maximum values were compared on measurements with at least a 2-s length