Paweł Miązek

Bio: Paweł Miązek is an academic researcher. The author has contributed to research in topics: Profitability index & Environmental economics. The author has an hindex of 1, co-authored 1 publications receiving 13 citations.

Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas

Abstract: The article presents the methodology of isolating aperiodic phenomena constituting the basis of the energy balance of vehicles for the analysis of electromobility system indicators. The symptom observation matrix (SOM) and experimental input data are used to analyze periodic phenomena symptoms. The multidimensional nature of the engine efficiency shortage has been well defined and analyzed in terms of errors in the general model using neural networks, singular value decomposition, and principal component analysis. A more difficult task is the analysis of a multidimensional decision-making process. The research used a data fusion method and the concept of symptom reliability, which is applied to the generalized failure symptom obtained by applying the singular value decomposition (SVD). The model research has been based on the gray system theory (GST) and GM forecasting models (1,1). Input data were obtained from the assessment of driving cycles and analysis of the failure frequency for 1200 vehicles and mileage of 150,000 km. Based on this analysis, it can be concluded that with the current infrastructure and operating costs and the frequency of failure of PHEV and BEV drives, ICEV vehicles are unrivaled in terms of their operating costs.

Measures for Assessing the Effectiveness of Investments for Electricity and Heat Generation from the Hybrid Cooperation of a Photovoltaic Installation with a Heat Pump on the Example of a Household

Abstract: In recent years, one of the key postulates in the European Union’s policy has become the development of renewable energy sources. In order to achieve the desired synergy effect, the idea of combining two selected sources of energy appeared. This article presents a technical and economic analysis of a hybrid connection of a ground source heat pump with a photovoltaic installation. Taking into account the heat demand of the building, a ground heat pump with a catalog nominal heating power of 25 kW was selected. This article presents the problem of the economic profitability of using a hybrid combination of a heat pump and photovoltaic panels in domestic hot water and central heating systems. The justification for the use of such heat sources in these installations is due to global trends and the gradual departure from conventional energy sources such as oil or gas boilers. This paper presents the economic and ecological results of using the pump heat connected together with photovoltaic panels. In the economic analysis, with the assumed installation costs related to the use of the considered heat pump and PV, two parameters commonly used in the investment analysis (static and dynamic) were used, namely, the simple payback period and the net present value of the investment. For the adopted assumptions, the usable area of the facility and the number of years of use were indicated, at which the investment in question is competitive with other alternative investment interest methods and will start to bring tangible benefits. The performed analysis also has measurable environmental benefits in the form of a reduction in carbon dioxide emissions at the level of 2893 kg/year into the atmosphere. The presented solution will help future investors understand the investment profitability mechanism for their households.

The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions

Abstract: Plug-in hybrids (PHEV) have become popular due to zero-emission driving, e.g., in urban areas, and using an internal combustion engine on longer distances. Energy consumption by the PHEV depends on many factors which can be either dependent or independent of the driver. The article examines how the driver can use the vehicle’s capabilities to influence its wear. Determining the optimal driving technique, due to the adopted nature of the timetable, is the basic variable that determines the profitability of using a given drive system. Four driving techniques have been selected to determine which one can offer the largest advantages. A vehicle-dedicated application has recorded the drivetrain performance on a predetermined route through an urban area. The analysis of results has demonstrated which of the driving techniques provides measurable effects in terms of reduced energy consumption and the shortest travelling time. The study shows longitudinal acceleration and torque generated by the electric drive. The information included in the study can help any PHEV user reduce the operating cost by applying an appropriate driving technique. The proposed research introduces the possibilities of assessing the influence of the driving style on energy consumption. The innovative side of this research is the observation of stochastic phenomena that are difficult to detect when using approximation modelling.

A Method of Analyzing the Residual Values of Low-Emission Vehicles Based on a Selected Expert Method Taking into Account Stochastic Operational Parameters

Abstract: Increasing the market share of low-emission vehicles in relation to individual mobility is one of the main postulates of modern transport policy. In the discussion on low-emission and the new structure of the car fleet, the role of new vehicles is emphasized above all, ignoring the importance of the secondary market. In recent years, both in Poland and in other European Union countries, there has been a noticeable dynamic development of electromobility implementation processes in urban areas, the initial effect of which is increasing market accessibility to commercial vehicles with electric EV/BEV, hybrid HEV/PHEV and fuel cell powered FCEV. As in the case of vehicles powered by conventional ICEV fuels, also in relation to those defined as low-emission, their residual value is lost along with the operational process. Information on this variable is important both for the owner of a newly purchased vehicle, which after the period of its operation will decide to sell it as well as to the future buyer. The scientific aim of the study is to analyze the residual values of selected vehicle models from the primary and secondary market, with particular emphasis on stochastic operational phenomena. The subject of the research is to obtain extensive knowledge on the achieved changes in the residual values of low-emission vehicles in relation to ICEVs. For this purpose, a comparative analysis of the commercial program, data approximated from auction portals and own numerical modeling tool based on a neural network was performed. The research sample included, among others, selected models of passenger cars, the purchase offer of which included the choice of a drive unit powered by conventional and low-emission fuels. The use of this method allowed to answer the question whether low-emission vehicles are characterized by a greater or lesser loss of value in relation to conventionally powered vehicles ICEV.

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Abstract: Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

Numerical and Thermal Investigation of Magneto-Hydrodynamic Hybrid Nanoparticles (SWCNT-Ag) under Rosseland Radiation: A Prescribed Wall Temperature Case

Abstract: Thermal heat generation and enhancement have been examined extensively over the past two decades, and nanofluid technology has been explored to address this issue. In the present study, we discuss the thermal heat coefficient under the influence of a rotating magneto-hydrodynamic hybrid nanofluid over an axially spinning cone for a prescribed wall temperature (PWT) case. The governing equations of the formulated problem are derived by utilizing the Rivlin–Ericksen tensor and boundary layer approximation (BLA). We introduce our suppositions to transform the highly non-linear partial differential equations into ordinary differential equations. The numerical outcomes of the problem are drafted in MATLAB with the of help the boundary value problem algorithm. The influences of several study parameters are obtained to demonstrate and analyze the magneto-hydrodynamic flow characteristics. The heat and mass transfer coefficients increase and high Nusselt and Sherwood numbers are obtained with reduced skin coefficients for the analyzed composite nanoparticles. The analyzed hybrid nanofluid (SWCNT-Ag–kerosene oil) produces reduced drag and lift coefficients and high thermal heat rates when compared with a recent study for SWCNT-MWCNT–kerosene oil hybrid nanofluid. Maximum Nusselt (Nu) and Sherwood (Sh) numbers are observed under a high rotational flow ratio and pressure gradient. Based on the results of this study, we recommend more frequent use of the examined hybrid nanofluid.

A Review on Drive Train Technologies for Passenger Electric Vehicles

Abstract: Transportation is the second-largest sector contributing to greenhouse gas emissions due to CO2 gas generation from the combustion of fossil fuels. Electric vehicles (EVs) are believed to be a great solution to overcome this issue. EVs can reduce CO2 emissions because the vehicles use an electric motor as a propeller instead of an internal combustion engine. Combined with sustainable energy resources, EVs may become zero-emission transportation. This paper presents an overview of the EV drive train types, including their architecture with the benefits and drawbacks of each type. The aim is to summarize the recent progress of EV technology that always continues to be updated. Furthermore, a comparative investigation on energy density and efficiency, specific energy and power, cost, and application is carried out for batteries as the main energy storage. This discussion provides an understanding of the current development of battery technology, especially the batteries used in EVs. Moreover, the electric motor efficiency, power density, fault tolerance, reliability, and cost are also presented, including the most effective electric motor to use in EVs. The challenges and opportunities of EV deployment in the future are then discussed comprehensively. The government regulation for EVs is still a major non-technical challenge, whereas the charging time and battery performance are the challenges for the technical aspect.