• Energy Research
• PL close
• AT close
• Energies close
• Transport Research close

In accordance with the policies of European Union countries, including Poland, users of combustion vehicles are and will be encouraged in the coming years to purchase BEVs (battery electric vehicles) through various restrictions, burdens, and incentives. In this context, it is important to choose a BEV that meets consumer needs. The practical aim of the article was to analyse BEVs used in households and to select a car with the highest utility for such a household located in a specific city in Poland. The scientific goal was to analyse the impact of subjective criteria and the imprecision of judgements on the results of the multi-criteria assessment of BEVs. The research used the PVM-VSI (Preference Vector Method—Vector Space of Increments) method, which allows for examining the impact of subjective criteria on the assessment results. Moreover, by examining the deviations of the assessments, the PVM-VSI method also allows for measuring the imprecision of subjective judgements. The study showed that including subjective criteria in the decision-making model may have a decisive impact on the obtained ranking of alternatives. In the study using objective and subjective criteria, Nissan Ariya ranked first. However, in the ranking based solely on objective criteria, Kia Niro EV won, and the winner of the first ranking took the last place. In the study, the imprecision of judgements did not have a significant impact on the order of vehicles in the ranking. Based on utilities and standard deviations, it was found that only if the decision-maker was prone to gambling would there be a slight switch between alternatives.

In accordance with the policies of European Union countries, including Poland, users of combustion vehicles are and will be encouraged in the coming years to purchase BEVs (battery electric vehicles) through various restrictions, burdens, and incentives. In this context, it is important to choose a BEV that meets consumer needs. The practical aim of the article was to analyse BEVs used in households and to select a car with the highest utility for such a household located in a specific city in Poland. The scientific goal was to analyse the impact of subjective criteria and the imprecision of judgements on the results of the multi-criteria assessment of BEVs. The research used the PVM-VSI (Preference Vector Method—Vector Space of Increments) method, which allows for examining the impact of subjective criteria on the assessment results. Moreover, by examining the deviations of the assessments, the PVM-VSI method also allows for measuring the imprecision of subjective judgements. The study showed that including subjective criteria in the decision-making model may have a decisive impact on the obtained ranking of alternatives. In the study using objective and subjective criteria, Nissan Ariya ranked first. However, in the ranking based solely on objective criteria, Kia Niro EV won, and the winner of the first ranking took the last place. In the study, the imprecision of judgements did not have a significant impact on the order of vehicles in the ranking. Based on utilities and standard deviations, it was found that only if the decision-maker was prone to gambling would there be a slight switch between alternatives.

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities.

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities.

The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs’ performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters’ effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 °C, the number of points served by EVs is 40–64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions.

The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs’ performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters’ effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 °C, the number of points served by EVs is 40–64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions.

The introduction of electromobility contributes to an increase in energy efficiency and lower air pollution. European countries have not been among the world’s leading countries in this statistic. In addition, there have been different paces in the implementation of electromobility in individual countries. The main purpose of this paper is to determine the directions of change and the degrees of concentration in electromobility in European Union (EU) countries, especially after the economic closure as a result of the COVID-19 pandemic. The specific objectives are to indicate the degree of concentration of electromobility in the EU and changes in this area, especially during the COVID-19 pandemic; to determine the dynamics of changes in the number of electric cars in individual EU countries, showing the variability in this aspect, while also taking into account the crisis caused by COVID-19; to establish the association between the number of electric cars and the parameters of the economy. All EU countries were selected for study by the use of the purposeful selection procedure, as of December 31, 2020. The analyzed period covered the years 2011–2020. It was found that in the longer term, the development of electromobility in the EU, measured by the number of electric cars, is closely related to the economic situation in this area. The crisis caused by the COVID-19 pandemic has influenced the economic situation in all EU countries, but has not slowed down the pace of introducing electromobility, and may have even accelerated it. In all EU countries, in the first year of the COVID-19 pandemic, the dynamics of introducing electric cars into use increased. The growth rate in the entire EU in 2020 was 86%, while in 2019 it was 48%. The reason was a change in social behavior related to mobility under conditions of risk of infection. COVID-19 has become a positive catalyst for change. The prospects for the development of this type of transport are very good because activities related to the development of the electromobility sector perfectly match the needs related to the reduction of pollution to the environment.

The introduction of electromobility contributes to an increase in energy efficiency and lower air pollution. European countries have not been among the world’s leading countries in this statistic. In addition, there have been different paces in the implementation of electromobility in individual countries. The main purpose of this paper is to determine the directions of change and the degrees of concentration in electromobility in European Union (EU) countries, especially after the economic closure as a result of the COVID-19 pandemic. The specific objectives are to indicate the degree of concentration of electromobility in the EU and changes in this area, especially during the COVID-19 pandemic; to determine the dynamics of changes in the number of electric cars in individual EU countries, showing the variability in this aspect, while also taking into account the crisis caused by COVID-19; to establish the association between the number of electric cars and the parameters of the economy. All EU countries were selected for study by the use of the purposeful selection procedure, as of December 31, 2020. The analyzed period covered the years 2011–2020. It was found that in the longer term, the development of electromobility in the EU, measured by the number of electric cars, is closely related to the economic situation in this area. The crisis caused by the COVID-19 pandemic has influenced the economic situation in all EU countries, but has not slowed down the pace of introducing electromobility, and may have even accelerated it. In all EU countries, in the first year of the COVID-19 pandemic, the dynamics of introducing electric cars into use increased. The growth rate in the entire EU in 2020 was 86%, while in 2019 it was 48%. The reason was a change in social behavior related to mobility under conditions of risk of infection. COVID-19 has become a positive catalyst for change. The prospects for the development of this type of transport are very good because activities related to the development of the electromobility sector perfectly match the needs related to the reduction of pollution to the environment.