Fleet Operations Research Articles

Swarms of Pirates: Red Team Exercises Using Autonomous High-Speed Maneuvering Surface Vessels

We present a final overview of the efforts by the Naval Air Warfare Center Weapons Division (NAWCWD) and the Jet Propulsion Laboratory to automate the operation of the largest fleet of autonomous maritime vehicles. The vehicles are intended for large-scale demonstrations and tests of US Navy systems or tactics. This review covers a preexisting distributed architecture for human-in-the-loop control of several autonomous high-speed boats with a main focus on the planning, formation assignment, and formation switching pipeline. Algorithm capabilities are described and validated in simulation and field tests using real-world vehicles. Theoretical lower bounds on the time required to change formations are also derived and used to bound our experimental performance. We are able to present data from the 2019 “final exam” that pitted this architecture in a head-to-head competition, which was won after a perfect run with no hazardous maneuvers recorded under autonomous control. The effort concluded successfully on December 31, 2020, with the delivery of code and documentation after successful integration and testing exercises in 2019 and 2020. We conclude the paper with discussions of limitations, extensions, and suggestions for future work.

Robust charging strategies for electric bus fleets under energy consumption uncertainty

Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. A deterministic model and a robust model with a probability-free uncertainty set are proposed and compared. The power is optimized via rational allocation of charging resources, where the uncertainty of energy consumption is addressed to achieve the dual goals of reducing charging expenses and improving system robustness. A column generation algorithm is designed to solve the optimization issue. The experimental results show that the obtained robust charging strategies can achieve up to 97.88% utilization of charging resources at low electricity prices. Moreover, the robust model can effectively prevent low electric quantity and delayed departure issues for EBs caused by the uncertainty of energy consumption.

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.

Electric truck deployment in Chinese cities: Promotion policies and implications for future policymaking

AbstractThe electrification of road freight vehicles is an important approach to reduce air pollution and carbon emissions from the trucking industry. In its current stage, electric truck adoption in China is concentrated in a few cities that provide strong support for the technology. This article reviews the promotion policies adopted by ten major Chinese cities pioneering electric logistics vehicle (ELV) adoption. These policies include operational subsidies, road access privileges, subsidies for charger installation and operation, and other incentives for ELV charging and parking. As local purchasing subsidies for ELVs have been phased down, the operational subsidies and road access privileges provide the main impetus for urban ELV adoption. In addition, a holistic regional assessment identifying high‐potential provinces for future electric truck deployment in the country is described. The assessment takes into account eight indicators under three relevant categories of considerations (i.e., the unique attributes of the technology, where it is most needed, and where it has the most support) and shows that provinces favorable for electric truck deployments are concentrated in the east and south of China. Fleet operators, policymakers, and other industry stakeholders should consider prioritizing electric truck deployments in these regions. Some of these eight indicators are difficult or impossible to change, and policymakers looking to encourage more logistics vehicle electrification must therefore focus on those policy factors that can be changed. Policy recommendations are then formulated in light of the city‐level policy analysis as well as the regional assessment, which is expected to help inform future policymaking and speed ELV adoption in cities across the high‐potential regions and even nationwide.This article is categorized under: Cities and Transportation > Electric Mobility Policy and Economics > Regional and International Strategies

Процессные модели развития триады подсистем лечебно-профилактических учреждений: управление, ресурсы, потенциал. Ресурсы

In modern conditions, an integrated approach is needed to the priority development of the material and technical base, namely in the aspect of the acquisition and supply of medical equipment. Being a part of organizational technology, the efficient operation of the medical equipment fleet contributes greatly to improving the quality of medical care.

Predicting the relative effectiveness of different management scenarios at reducing seabird interactions in a demersal trawl fishery

The incidental mortality in trawl fisheries is considered a conservation threat for many seabirds. We simulate management scenarios, combining bird scaring lines (BSL; zero, one and two) and variables describing discards (occurrence, levels, type, and mode), to predict the total seabird collisions with warp cables potentially produced by a demersal trawl fleet operating in a region and season of high seabird abundance. A total of 2067 collisions, including 439 heavy collisions and 53 fatal collisions, were recorded on five trips aboard the Uruguayan trawl fleet. One BSL reduced collisions and heavy collisions by 89%, and the associated mortality by 94%. Best management scenarios in terms of reducing collisions were those without discards, where the models with BSL outperformed the scenarios without BSL. Scenarios with two BSL presented slight improvements, likely caused by the small sample size. Under a scenario without discards, the mortality caused by the entire fleet is likely to be negligible. Given the inability to eliminate discards in most situations, we simulated scenarios of discharge produced in batches with BSL. This could drive mortality to negligible levels. We provide six recommended scenarios (S) ordered by expected reduction in seabird mortality. Fisheries implementing BSL as a single mitigation measure should present a strong reduction in seabird mortality. However, the ability to hold discards on board, at least partially, can drive mortality to negligible levels. This should occur without discards and BSL use (S1), without discards and without BSL (S2), and with batch discharge and BSL use (S3).

Modern authentication solutions for fleet management

Protect people, data and vehicles and optimize costs and processes while keeping an eye on regulatory compliance: vehicle fleet operators face a number of requirements. Modern authentication solutions based on radio frequency identification (RFID) and mobile technologies for driver identification and access control help to meet these challenges. They can significantly improve the utility of telematics and other fleet management solutions. To make the implementation a success, there are important aspects to consider.

Optimization and Analysis of Electric Vehicle Operation with Fast-Charging Technologies

This work presents three demos, which include Electric Buses (EBs) from four various brands with lengths of 12 m and 18 m and an Electric Truck (E-truck) for refuse collection. The technical operation of these EVs were analyzed to implement further operational cost optimization on the demo vehicles. The Electric Vehicles (EVs) were tested against superfast-charging solutions based on Pantograph (Type A & Type B) on the route lines (and depots) and based on Combined Charging System Type 2 (CCS2, Combo2) from various brands to validate the interoperability among several vendors and support further EV integration with more affordable solutions. The optimization includes the calculation of the EBs’ consumption at various seasons and under various operating conditions in order to use optimum battery system design, heating system, optimum EB fleet operation and size and to find the charging solutions properly. The results showed that the EB consumption increases in some cases by 64.5% in wintertime due to heating systems, and the consumption in urban areas is more than that on the route lines outside cities. In the E-truck demo, where the electric heater was replaced with a heat-pump to optimize the energy consumption, it was found that the consumption of the heat-pump is about half of the electric heater under certain operating conditions. Under strict EB schedule, Pantograph charging solutions with power ratings of 300–600 kW have been adopted to charge the batteries of the EBs within 4–10 min. In order to minimize the cumulative costs of energy, (pantograph) charging infrastructure depreciation and battery degradation, as well as depot charging (at the bus operator’s depot), was adopted with a power level of 50–350 kW based on CCS2 and pantograph.

A predictive analytics method for maritime traffic flow complexity estimation in inland waterways

Maritime traffic flow complexity is the factor that presents in most existing maritime safety analysis methods. It is considered as one of the main influencing factors affecting maritime safety. It can be estimated quantitatively through the analysis of traffic data. To explore maritime ship traffic in more detail, a predictive analytics method utilizing the Lempel-Ziv algorithm and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for traffic safety management is proposed. The Lempel-Ziv algorithm quantifies the entropy for the evaluation of the irregularity and unpredictability of ship travel time sequences, and TOPSIS ranks complexity. The results presented utilize Automatic Identification System (AIS) data corresponding to complex inland waterway traffic scenarios encountered in the Yangtze River. They show that high complexity implies ship travel time sequences are neither periodic nor stochastic, but dependent on the evolution patterns of traffic encounters. A correlation analysis of traffic flow complexity with the number of maritime accidents is carried out, showing that higher traffic flow complexity may result in more unwanted events. It is therefore concluded that the proposed method may help to (1) differentiate traffic flow complexity accurately, indicating that the higher the complexity value is, the higher the irregularity and unpredictability of maritime traffic flow are, (2) provide helpful reference for optimizing traffic management over the life cycle of fleet operations as well as maritime safety management of high traffic flow complexity areas.

Data analytics in fleet operations: A systematic literature review and workflow proposal

Fleet operations have been one of the main concerns of a wide range of industries, particularly those industries in which transportation plays a pivotal role for operational continuity, such as logistics service providers and public transportation. In addition, accentuated by the social and economic globalization, new scenarios and higher requirements were introduced to the transportation sector and, therefore, the need for advanced techniques, such as optimization, traffic modeling, and machine learning, to improve fleet operations, is growing. As for machine learning, although an increasing number of data-driven methods are observed in manufacturing and mobility solutions, there is still a gap in terms of literature reviews related to machine learning applications in fleet operations. Based on this gap, a systematic literature review was conducted, and several applications related to machine learning in fleet operations were identified, most of them encompassing aspects related to predictive maintenance of mechanical systems and components. It was also identified that most of the studies were based on a single data source and did not approach aspects related to the deployment of the machine learning models and data analysis workflows. Therefore, based on the findings from this literature review, the present paper aims to present an overview of the main machine learning applications in fleet operations and propose a data analysis workflow for the connectivity monitoring of heavy vehicles based on multiple data sources.

Joint Routing and Charging Problem of Electric Vehicles with Incentive-Aware Customers Considering Spatio-Temporal Charging Prices

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator.

Factors influencing alternative fuel adoption decisions in heavy-duty vehicle fleets

Understanding heavy-duty vehicle (HDV) fleet operator behavior with respect to adoption of alternative fuel vehicles (AFVs) is critically important for accelerating diffusion of these technologies, and for achieving societal benefits through reduced emissions and improved public health. However, fleet operator perspectives have thus far received limited attention, leaving a key knowledge gap. This study aims to fill this gap by exploring HDV fleet operator decisions about alternative fuel adoption using both existing literature and new empirical data. To this end, we first develop an initial theoretical framework of AFV fleet adoption behavior in organizations based upon existing theories and literature. We then empirically improve the framework by investigating 20 organizations in California via in-depth qualitative interviews and project reports. A total of 29 adoption and 42 non-adoption cases were probed across various alternative fuel technologies, including natural gas, propane, electricity, hydrogen, biodiesel, and renewable diesel options. Content analysis of the qualitative data yielded 38 motivators or barriers related to AFV adoption, encompassing perceived technological characteristics, organization characteristics, and external environmental influences. The study results contribute theoretically and empirically to a better understanding of the demand-side aspects of AFV adoption by HDV fleet operators, particularly in California and in the other US states that follow California’s environmental policies.

A Data-driven Integrated Safety Risk Warning Model based on Deep Learning for Civil Aircraft

With the extensive application of sensor technology, airlines accumulate a lot of flight data during fleet operations. Quick Access Recorder (QAR) is an important basis for aircraft state estimation and safety assessment, it records the operating status of various aircraft systems. However, the current application of QAR is mainly reflected in the investigation and verification of accidents, but it fails to apply such continuous real-time data to safety monitoring, risk prediction, and early warning. Therefore, this paper proposes a data-driven integrated safety risk warning model based on deep learning. Combined with the characteristics of QAR data and aircraft system failures, the failure mode and impact analysis (FMEA), cause chain analysis (CCA), long and short-term memory (LSTM), and other methods are used to describe the development of risks caused by system failures and predict the trend of flight parameters of each system, improve the ability to predict the risk and risk severity of the aviation system. Taking the Aircraft Refrigeration System (ARS) as the case object, the safety risk warning model is validated to calculate the risk level may face and find out the hidden hazard sources of civil aircraft operation in advance, to improve the safety level of civil aircraft operation

Методика нормирования материальных ресурсов для автобусов

Abstract. The industry standards for the consumption of material resources currently in force in road transport are advisory in nature and are used by carriers in their current work for production planning. Industry standards, regulated, for example, by order of the Ministry of Transport of the Russian Federation No. 158 dated May 30, 2019, establish resource consumption on an average for all road carriers of the Russian Federation. Taking into account the individual operating conditions of the rolling stock and the organization of the transport process is possible only by a correction factor to the fuel consumption rate, the calculation method of which is not regulated. The result is low convergence of planned and actual traffic figures. An effective solution to this problem is the use of resource consumption rates calculated on the basis of statistical data on the consumption of material resources obtained using the means of objective control and data storage of ERP systems. This will make it possible to fully take into account the complex of significant factors that determine the volume of consumption of material resources, reduce the amount of inventory, increase the turnover of funds, and increase the accuracy of rationing and pricing processes for any trucking company. However, the substantiation of such resource consumption norms requires an assessment of the influence of significant factors, the establishment of a relationship between them and the development of a mathematical apparatus for normalization. Therefore, the purpose of this study, which is to increase the efficiency of passenger transportation by buses based on ensuring the reliability of material resource consumption rates, taking into account individual operating conditions, is aimed at solving an urgent scientific and practical problem. Theoretical studies are carried out on the basis of the analysis of scientific and regulatory and technical literature, the legal framework, systemic, statistical, factorial and technical and economic analysis, economic and mathematical modeling of the transport process, and expert evaluation. Experimental studies were carried out in the conditions of operating passenger transport enterprises using the methods of mathematical statistics, computer modeling, field observations. The main results of scientific novelty are: a set of qualitative characteristics and quantitative indicators of factors that determine the consumption of material resources in the operation of the bus fleet; mathematical models for calculating the consumption rates of fuels and lubricants, spare parts and car tires, as well as the corresponding standard operating costs; dependence of indicators of operating consumption of diesel fuel, resource mileage of automobile tires on the magnitude of significant factors established for domestically produced buses of various capacities. Further research is supposed to be carried out in the direction of the development of resource saving methods in the motor transport complex.

Calibration and Confidence in Snowplow Fleet Operations and Fleet Telematics

The Indiana Department of Transportation (INDOT) spends approximately $30 to $60 million a year on deicing salt and operates a fleet of 1,000 winter operations trucks distributed among 140 locations. The entire fleet is now instrumented with location telematics, and all new trucks have integrated dash cameras, salt spreader application rate and plow up/plow down integrated into the telematics link. When winter storms occur, they have varying regional impacts and INDOT monitors several data sources including National Weather Service (NWS) live doppler, National Severe Storms Laboratory’s (NSSL) Multi-Radar Multi-Sensor (MRMS) products, road weather monitoring stations, and connected vehicles (CV) that provide roadway segment operating speeds. This paper discusses how telematics has been integrated to provide a comprehensive view of conditions, truck asset locations, and material distribution maps. The telematics identified widely varying salt spreader rates for the same calibration settings and equipment in preliminary analysis. A calibration box is developed to allow offload calibration to occur within 10 minutes without weighing or transporting the fleet vehicle. The method is deployed across six districts at INDOT for over 1000 snowplows. A sampling of eight trucks in the fleet found the proposed calibration method reduced salt application on average of 45%. This paper describes a series of telematics dashboards for managing winter operations and details the methods developed for 140 geographically distributed truck units to conduct simple, fast, and effective calibration.

Capturing curtailed renewable energy in electric power distribution networks via mobile battery storage fleet

In this paper, a new scheduling model is proposed for the daily operation of a truck-mounted Mobile Battery Energy Storage Systems (MBES) fleet employed in a distribution network. The distribution network is installed with various wind and PV distributed resources wherein a portion of the renewable-based generation capacity is curtailed due to various technical reasons. The MBES fleet scheduling model aims at minimizing curtailed renewable energy by absorbing and releasing excess energy when and where needed. Accordingly, the variable spatial and temporal renewable generation curtailment is recovered by optimal spatio-temporal and power-energy scheduling of the MBES fleet. The required transportation time for the MBES unit transportation comprising detachment, movement, and attachment is considered efficiently. Besides, a detailed breakdown of the MBES transportation cost is presented and modelled via a new formulation. The proposed MBES fleet operation model can be easily integrated into the available commercial distribution optimal power flow packages. Considering linearity, the model can handle very large-scale real-life networks without convergence problems by achieving global optima. The model is numerically tested, and simulation results demonstrate its effectiveness for recovering a considerable share of the curtailed renewable energy irrespective of the resources type, generation time period, or installation location.

When Shared Autonomous Electric Vehicles Meet Microgrids: Citywide Energy-Mobility Orchestration

Problem definition: We develop a crossdisciplinary analytics framework to understand citywide mobility-energy synergy. In particular, we investigate the potential of shared autonomous electric vehicles (SAEVs) for improving the self-sufficiency and resilience of solar-powered urban microgrids. Academic/practical relevance: Our work is motivated by the ever-increasing interconnection of energy and mobility service systems at the urban scale. We propose models and analytics to characterize the dynamics of the SAEV-microgrid service systems, which were largely overlooked by the literature on service operations and vehicle-grid integration (VGI) analysis. Methodology: We develop a space-time-energy network representation of SAEVs. Then, we formulate linear program models to incorporate an array of major operational decisions interconnecting the mobility and energy systems. To preventatively ensure microgrid resilience, we also propose an “N − 1” resilience-constrained fleet dispatch problem to cope with microgrid outages. Results: Combining eight data sources of New York City, our results show that 80,000 SAEVs in place of the current ride-sharing mobility assets can improve the microgrid self-sufficiency by 1.45% (benchmarked against the case without grid support) mainly via the spatial transfer of electricity, which complements conventional VGI. Scaling up the SAEV fleet size to 500,000 increases the microgrid self-sufficiency by 8.85% mainly through temporal energy transfer, which substitutes conventional VGI. We also quantify the potential and trade-offs of SAEVs for peak electricity import reduction and ramping mitigation. In addition, microgrid resilience can be enhanced by SAEVs, but the actual resilience level varies by microgrids and by the hour when grid contingency occurs. The SAEV fleet operator can further maintain the resilience of pivotal microgrid areas at their maximum achievable level with no more than a 1% increase in the fleet repositioning trip length. Managerial implications: Our models and findings demonstrate the potential in deepening the integration of urban mobility and energy service systems toward a smart-city future.

Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error.

Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets

The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool’s performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs.

MAN trolleybuses in Ukraine (1939–1951): a history, technical characteristics, features of operation

The growth of the vehicle assets and bus services in Ukrainian cities increases the level of environmental pollution. During the environmental crisis, electric transport (e-transport) is becoming a matter for scientific inquiry, a subject of discussion in politics and among public figures. In the program for developing the municipal services of Ukraine, priorities are given to the development of the infrastructure of ecological transport: trolleybuses, electric buses, electric cars. The increased attention to e-transport on the part of the scientific community, politicians, and the public actualizes the study of its history, development, features of operation, etc. The historiographic analysis carried out by the authors allows us to say about insufficient coverage by Ukrainian researchers of a number of aspects and periods in the history of e-transport. A small number of special works on the history of the operation of foreign-made trolleybuses in Ukrainian cities in the first half of the 20th century and an analysis of their technical characteristics determine the relevance and scientific novelty of this study. When writing the work, Ukrainian and foreign scientific reference publications, monographs, papers, mainly from foreign electronic resources, have been used. The authors have used both general scientific (analysis, synthesis, deduction, induction) and historical research methods, in particular, problem-chronological, comparative-historical, retrospective methods, etc. The aim of the study is to highlight little-known facts of the history of production and operation of MAN trolleybuses in Ukrainian cities, as well as to introduce their technical characteristics into scientific circulation. The etymology of the model names of German trolleybuses, which usually consisted of the names of the manufacturers of chassis, body, and electrical equipment, has been clarified. The types, specific design solutions of the first MAN trolleybus generation and the prerequisites for their appearance in Chernivtsi have been determined. Particular attention has been paid to trolleybuses that were in operation in Germany and other Western European countries from the first half of the 1930s to the early 1950s. In the mid-1930s, the MAN plant in Nuremberg began production of trolleybuses; its models had the most modern constructive solutions at that time, a characteristic design and a state-of-the-art heating system. Depending on the length, German manufacturers divided the trolleybus models into four types. As a result of problems with the operation of the bus fleet in Chernivtsi, the city authorities have decided to build a trolleybus line in the city; four trolleybuses manufactured by the MAN plant were purchased. The paper traces the stages of operation of the MAN trolleybuses in Chernivtsi, where they worked during 1939–1944 and after the end of the Second World War, they were transferred to Kyiv. After two years of operation in the Ukrainian capital, the trolleybuses entered the routes in Dnipropetrovsk during 1947–1951. The technical characteristics of the first MAN trolleybus generation, which were operated in Ukrainian cities, have been presented and analyzed. It was determined that in all the main indicators and operational parameters, they were as close as possible to similar models of German trolleybuses. The proposed methodology and the structure of the study can later be used to write papers on the history of science and technology, in particular, of an e-transport.