Vehicle Fleet Research Articles

Empowering safe and secure autonomy: Federated learning in the era of autonomous driving

Artificial Intelligence (AI) has a significant impact on empowering autonomous driving systems to perceive and interpret the environment effectively. However, ensuring data privacy and security in autonomous driving systems is a critical challenge. To surmount these hurdles, federated learning has emerged as an effective strategy. Federated learning is a decentralized machine learning approach that facilitates the cooperative training of models across a diverse set of connected devices, enabling them to collectively learn and improve their performance, while preserving data privacy. This approach eliminates the necessity of sharing raw data and only involves sharing model updates with a central aggregator, thereby ensuring privacy and minimizing data exposure. This paper examines the implementation of federated learning in autonomous driving. It explores the principles of federated learning, including decentralized training, local model updates, model aggregation, privacy preservation, iterative learning, and heterogeneity handling. Two specific approaches, Deep Federated Learning (DFL) and End-to-End Federated Learning, are discussed, highlighting their benefits in enhancing privacy and maintaining prediction accuracy. The paper also discusses the applications of federated learning in communication and control aspects of autonomous driving. It emphasizes the scalability, adaptability, edge computing, real-time learning, federated transfer learning, and privacy-preserving data sharing as potential future prospects for federated learning in autonomous driving. Overall, federated learning offers a unique opportunity to address privacy concerns in autonomous driving systems while harnessing the collective intelligence of a fleet of vehicles. It has the potential to revolutionize the field and contribute to the development of safe and secure autonomous driving technologies.

Vehicle Abandonment: Analysis of National and International Fleets and Their Irregular Disposal on Public Roads

People's need to get around has meant that motor vehicles now dominate cities. The increase in the fleet has triggered numerous problems, including their improper disposal. In this sense, the objective was to evaluate the national and international fleets of motor vehicles and their irregular disposal on public roads, in a context of growing demand. The methodology included a systematic review of Capes journals and the evolution of the national and international fleet, as well as those already discarded, and sought an analysis of the various realities. The results showed that the unbridled increase in fleets resulted in urban mobility, with relevant environmental impacts due to the abandonment of vehicles on the roads, necessitating actions from social actors, governments, automobile companies, and society to solve the problems highlighted.

Evaluating private and system-wide impacts of freight eco-routing

Freight is crucial for economic development, but the trucks hauling this freight significantly contribute towards greenhouse gas emissions, advancing global climate change and criteria pollutant emissions that affect local community health. Hence, this work explores the opportunities and challenges associated with freight eco-routing to mitigate these freight-related externalities in Southern California. To this end, the authors establish the private impacts of a single carrier eco-routing its fleet of freight vehicles with a point-to-point routing tool. Further, the authors investigate network-wide freight eco- routing's system effects with the novel origin-based algorithm for multi-class Traffic Assignment by Paired Alternative Segments (mTAPAS). Overall, the study showcases the potential opportunities with freight eco-routing in reducing transportation-related externalities. However, it also highlights implementation challenges due to a lack of monetary incentives for carriers and regulators to account for these externalities in their decision-making.

Battery Electric Vehicles in Commercial Fleets: Use profiles, battery aging, and open-access data

Electric vehicles are key to reducing emissions in the transportation sector, and the electric vehicle market is growing strongly worldwide. In research and development in the electric vehicle sector, it is important to understand electric vehicle use profiles and their impact on battery aging. The battery is the central element of a vehicle as it determines the range as well as the price. For techno-economic analysis, battery aging is of special interest as it is a major factor in the vehicle’s lifetime. Battery aging depends on the battery use profile, but up to now, operational data is scarce, and many publications are based on simulations and assumptions. To contribute, we analyze multi-year commercial vehicle and battery data collected from onboard data loggers in a field test conducted from 2014 to 2016. We also develop and evaluate periodic non-intrusive capacity tests carried out with a chassis dynamometer to assess battery aging in terms of capacity fade. Electric vehicles in a geriatric care fleet experienced high usage and regular driving patterns with annual driving distances of 9062km to 15 308km and a mean state-of-charge between 70% and 80% while driving. Use profile regularity and state-of-charge values at the time of plug-in are major factors in the evaluation of vehicle-to-X application viability. Regarding aging, the batteries suffered a capacity fade from 3.1% to up to 13% during fleet operation of three years. Hand in hand with our analysis, we publish time series data of the vehicle and the battery in addition to mobility data for nine vehicles to fill the data gap for available electric vehicle data and allow for further analysis.

Probabilistic Models for Prognostics of the Condition of Vehicle Fleets

Probabilistic Models for Prognostics of the Condition of Vehicle Fleets

Exploring the Relationship Between CO2 Emissions and Fuel Consumption in Road Transport: Empirical Evidence from Chad

Over the period 2008-2019, this study examines the linear impact of petroleum product (PP) consumption in Chad, PP prices, the vehicle fleet and urbanisation on CO2 emissions on the one hand, and on the other, it determines the causal links that exist between the various variables studied. This study opts for the augmented Dickey-Fuller and Phillips-Perron tests to verify the stationarity of the variables. The ARDL model is then estimated and diagnostic tests are performed to confirm the validity of the model. To confirm the existence of long-term relationships, the bounds test was applied. Finally, the Toda-Yamamoto causality test was used to capture the influences present between the series under study. The main results show that, in the long term, a 1% increase in gasoline consumption would lead to a 1.03% increase in CO2 emissions and the linear impact of diesel consumption is positive and insignificant on CO2 emissions. In the short term, gasoline and diesel consumption have a positive and insignificant impact on CO2 emissions. In terms of causality, there is a unidirectional causality from gasoline consumption to CO2 emissions and a bidirectional causality between CO2 emissions and diesel consumption. This study is the first to simultaneously link CO2 emissions, PP consumption, PP prices, the vehicle fleet and urbanisation in general, and particularly in the case of Chad. It therefore adds to the literature on the simultaneous relationship between CO2 emissions, PP consumption, the car fleet and urbanisation in a global and restricted context. This study could guide Chadian oil pollution management decision-makers in adopting policies related to the effects of PP consumption in the road transport sector.

Automotive braking is a source of highly charged aerosol particles

Although the last several decades have seen a dramatic reduction in emissions from vehicular exhaust, nonexhaust emissions (e.g., brake and tire wear) represent an increasingly significant class of traffic-related particulate pollution. Aerosol particles emitted from the wear of automotive brake pads contribute roughly half of the particle mass attributed to nonexhaust sources, while their relative contribution to urban air pollution overall will almost certainly grow coinciding with vehicle fleet electrification and the transition to alternative fuels. To better understand the implications of this growing prominence, a more thorough understanding of the physicochemical properties of brake wear particles (BWPs) is needed. Here, we investigate the electrical properties of BWPs as emitted from ceramic and semi-metallic brake pads. We show that up to 80% of BWPs emitted are electrically charged and that this fraction is strongly dependent on the specific brake pad material used. A dependence of the number of charges per particle on charge polarity and particle size is also demonstrated. We find that brake wear produces both positive and negative charged particles that can hold in excess of 30 elementary charges and show evidence that more negative charges are produced than positive. Our results will provide insights into the currently limited understanding of BWPs and their charging mechanisms, which potentially have significant implications on their atmospheric lifetimes and thus their relevance to climate and air quality. In addition, our study will inform future efforts to remove BWP emissions before entering the atmosphere by taking advantage of their electric charge.

Delivery routing for a mixed fleet of conventional and electric vehicles with road restrictions

The enforcement of stringent regulations capping carbon emissions has prompted city logistics enterprises to substitute electric vehicles (EVs) for conventional vehicles (CVs). For city logistics enterprises with a mixed fleet of CVs and EVs, this study investigates the delivery routing problem with road restrictions (DRPRR) for accessible time windows and bearing weight. We formulate the DRPRR as a mixed integer programming model to minimise the total operations cost. The model consists of the set-up cost of CVs and EVs, the diesel cost of CVs, the electricity cost of EVs, the carbon tax cost, and the penalty cost of vehicles waiting for roads to be accessible. To effectively solve the model, an adaptive large neighbourhood search (ALNS) algorithm is developed that consists of tailored destroy and repair operators with two alternative solution acceptance criteria. Numerical experiments are conducted to validate the effectiveness of the proposed model and ALNS algorithm. In small-scale instances, the ALNS with the Metropolis criterion finds the best solutions for 41 of 48 instances while maintaining a deviation of less than 2.5% from CPLEX in the remaining 7 instances, and its running time is significantly shorter than CPLEX. In large-scale instances, the ALNS with the Metropolis criterion has stronger solving ability and better stability than benchmark algorithms (i.e. GA-LS, LNS, and ALNS with a threshold acceptance criterion). We also address a real-world case and conduct a sensitivity analysis to provide useful managerial insights. Specifically, implementing road restrictions on accessible time windows and the carbon tax policy simultaneously is more appropriate from the comprehensive perspective of market activity and carbon emissions.

Greenhouse gas emissions benefits of the lightweight vehicle fleet in China: A dynamic fleet perspective

Lightweighting is believed to be a silver bullet to reduce greenhouse gas (GHG) emissions related to the road transportation. However, the fleet-scale and long-term GHG emissions estimation on the light-duty passenger vehicle (LDPV) fleet in China is still underexplored. A dynamic fleet-based life cycle assessment (LCA) model is developed to predict the annual GHG emissions attributable to the lightweight LDPV fleet in China at provincial level. With the most aggressive implementation of lightweighting, the cumulative mitigation potential of lightweight LDPV fleet from 2021 to 2050 is 0.72 Gt CO2 eq and 0.55 Gt CO2 eq using high-strength steel (AHSS) and aluminium (Al), respectively. Sooner implementation brings a higher cumulative reduction in GHG emissions. Delaying the implementation by five years would sacrifice 0.16 Gt CO2 eq ∼0.2 Gt CO2 eq cumulative reduction, and the loss would increase to 0.31 Gt CO2 eq ∼0.41 Gt CO2 eq when postpone the lightweighting by ten years.

Long-term trends of black carbon and particle number concentrations and their vehicle emission factors in Stockholm

Black carbon (BC) and particle number (PN) concentrations are usually high in cities due to traffic emissions. European mitigation policies, including Euro emission standards, have been implemented to curb these emissions. We analyzed BC and PN (particle diameter Dp > 4 nm) concentrations in Stockholm spanning the years 2013–2019 (BC) and 2009–2019 (PN) measured at street canyon and rooftop sites to assess the effectiveness of the implemented policies. Combining these data with inverse dispersion modeling, we estimated BC and PN emission factors (EFBC and EFPN) for the mixed fleet, reflecting real-world driving conditions. The pollutants showed decreasing trends at both sites, but PN concentrations remained high at the canyon site considering the World Health Organization (WHO) recommendations. BC concentrations declined more rapidly than PN concentrations, showing a −9.4% and −4.9% annual decrease at the canyon and −7.2% and −0.5% at the rooftop site in the years 2013–2019. The EFBC and EFPN trends showed that the mitigation strategies for reducing particulate emissions for on-road vehicles were successful over the study period. However, the introduction of biofuels in the vehicle fleet —ethanol and later rapeseed methyl ester (RME)— increased the concentrations of particles with Dp < 10 nm before the adoption of particulate filters in the exhausts. Stricter Euro emission regulations, especially with diesel particulate filters (DPF) in Euro 5, 6, and VI vehicles, led to 66% decrease in EFBC and 55% in EFPN. Real-world EFBC surpassed HBEFA (Handbook Emission Factors for Road Transport) database values by 2.4–4.8 times; however, direct comparisons between real-world and HBEFA EFPN are difficult due to differences in lower cut-off sizes and measurement techniques. Our results underscore the necessity for revising the HBEFA database, updating laboratory testing methods and portable emission measuring systems (PEMS) measurements to account for liquid condensate contributions to PN measurements.

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is also a way to reduce the operating costs of an electric vehicle. However, to produce cheap electricity from renewable energy sources, you first need to invest in the construction of a photovoltaic system. The article presents a strategic model for charging a fleet of electric vehicles with energy from photovoltaic systems. The model is useful for sizing a planned photovoltaic system to the energy needs of a vehicle fleet. It uses the Metalog family of probability distributions to determine the probability of producing a given amount of energy needed to power electric vehicle chargers. Using the model, it is possible to determine the percentage of energy from photovoltaic systems in the total energy needed to charge a vehicle fleet. The research was carried out on real data from an operating photovoltaic system with a peak power of 50 kWp. The approach presented in the strategic model takes into account the geographical and climatic context related to the location of the photovoltaic system. The model can be used for various renewable energy sources and different sizes of vehicle fleets with different electricity demands to charge their batteries. The presented model can be used to manage the energy produced both at the design stage of the photovoltaic system and during its operation.

Te Whatu Ora EV

This research project looks at the implications of Te Whatu Ora Southern transitioning their 300 fleet vehicles to EVs, with the primary focus of this study being Wakari hospital. The key questions this study focuses on are: How would this transition to EVs affect the transport emissions by Te Whatu Ora Southern? What would the cost savings of this transition be? What time of day would be best for Wakari hospital to charge the vehicles and how much would they save by charging at specific times? As Te Whatu Ora Southern ranges from Oamaru to Invercargill and Queenstown, and with around 300 fleet vehicles, the problems to solve were tackled by first looking at Wakari hospital and using results from this hospital to scale to the whole fleet. This research was done by analyzing data supplied by Te Whatu Ora Southern and appropriate literature exploring EVs and emissions reduction. The main results of this research showed that by switching the fleet vehicles to EVs, the road travel emissions would decrease by 80-90%, and that it would be cheapest for Wakari Hospital to charge their vehicles from midnight to 8am, however more research would need to be done in order to implement a more detailed charging plan. The key impact of this research is carbon emission reduction and how Te Whatu Ora could scale these results for not just the southern region but the whole of New Zealand.&#x0D; Supervised by: Arjan Abeynaike, Craig Cliff

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

Designing corporate mobility as a service – Decision support and perspectives

Corporate Mobility as a Service (CMaaS) integrates company-exclusive fleets and public mobility services, such as car sharing, bike sharing, or taxis, into one multimodal mobility system. As a result, companies may reduce their own vehicle fleet, their total mobility costs, as well as their emissions by shifting trips to alternative mobility modes. Against this background, we present a strategic-tactical decision support tool for corporate mobility managers to design a CMaaS system. We include strategic decisions on the fleet size and composition of company-exclusive fleets. The fleet composition addresses decisions on the inclusion of various mobility modes, e.g., cars and bikes, as well as on the choice of vehicle classes, e.g., regarding drive train technologies, and on how these vehicles are provided to the company, e.g., leased or owned vehicles. Further, we include tactical decisions on the choice of price tariffs offered by public mobility providers. Herein, our decision support tool accounts for structural requirements, such as the spatio-temporal corporate mobility demand, costs, upper bounds on the number of vehicles, technical vehicle restrictions, and the use of micromobility modes, like bikes and scooters. Finally, a comprehensive case study with ten mobility services and driving profiles of 144 companies enables us to draw general insights on the current and future potentials of CMaaS. Results promise average cost savings of 33% and a shift of 21% of trips to public mobility services.

Air pollutant emissions and sources in Lao People’s Democratic Republic: a provincial scale analysis for years 2013-2019

Recent rapid economic development in Lao People’s Democratic Republic (PDR) has increased national fuel consumption, vehicle fleet, industrial output, waste generation, and agricultural production. This has contributed to national average ambient fine particulate matter (PM2.5) air pollutant levels that are four times higher than World Health Organisation guidelines. Emission inventories are a key tool in understanding the major sources to these air pollution levels, and provide a starting point to identify where mitigation action can be targeted. A national air pollutant emission inventory has not been developed in Lao PDR and, combined with a limited air quality monitoring network means there is limited capacity to develop and track the effectiveness of mitigation actions. This study describes the first air pollutant emission inventory at the national and provincial scale for Lao PDR, covering 2013–2019. Emissions of nine air pollutants, and two greenhouse gases, were quantified using national statistics and international default emission factors. In 2019, national total PM2.5, Nitrogen Oxides (NOx), Black Carbon (BC), Sulphur Dioxide (SO2), Non-Methane Volatile Organic Carbons (NMVOCs), and Ammonia (NH3) were 125, 83, 9.7, 26, 219, and 99 thousand tonnes respectively. Key source sectors include forest fires, residential cooking, agriculture, electricity generation, and transport. However, the contribution of different sources varies across provinces. Forest fires are the primary source determining the spatial trend of particulate air pollution while residential and agricultural emissions contribute more significantly to rural provinces such as Savannakhet. Key sectors in major urban provinces (Vientiane Capital and Xayaboury) are industry, transport and electricity generation. These sectors are also significant sources of greenhouse gases (CO2 and CH4), demonstrating the potential for identification, evaluation and prioritisation of actions that simultaneously improve air quality and achieve Lao PDR’s international climate change commitments.

Considerations on reducing emissions by increasing the use of electric vehicles in urban area

The pace between city development, increasing number of working places and emissions control is a challenge nowadays in urban environment. The gradual increase of electric cars ownership is essential for reducing the emissions level in urban areas. The city policy is of high importance for encouraging the use of electric cars, especially for trip purposes to work and business. The paper deals with the effects of increasing the fleet of electric vehicles on the emissions level in urban areas. The effect of considering a certain share of electric car trips to work and business is analyzed. Considering this, the concept of “green city corridors” can be introduced and considered as a priority for the transport policy in urban areas.

Towards zero pollution vehicles by advanced fuels and exhaust aftertreatment technologies

Vehicular emissions deteriorate air quality in urban areas notably. The aim of this study was to conduct an in-depth characterization of gaseous and particle emissions, and their potential to form secondary aerosol emissions, of the cars meeting the most recent emission Euro 6d standards, and to investigate the impact of fuel as well as engine and aftertreatment technologies on pollutants at warm and cold ambient temperatures. Studied vehicles were a diesel car with a diesel particulate filter (DPF), two gasoline cars (with and without a gasoline particulate filter (GPF)), and a car using compressed natural gas (CNG). The impact of fuel aromatic content was examined for the diesel car and the gasoline car without the GPF. The results showed that the utilization of exhaust particulate filter was important both in diesel and gasoline cars. The gasoline car without the GPF emitted relatively high concentrations of particles compared to the other technologies but the implementation of the GPF decreased particle emissions, and the potential to form secondary aerosols in atmospheric processes. The diesel car equipped with the DPF emitted low particle number concentrations except during the DPF regeneration events. Aromatic-free gasoline and diesel fuel efficiently reduced exhaust particles. Since the renewal of vehicle fleet is a relatively slow process, changing the fuel composition can be seen as a faster way to affect traffic emissions.

Shifting towards luxury cars: The price and environmental effects of Beijing’s vehicle lottery system and an alternative policy

This paper investigates changes in the composition of vehicle fleets after Beijing implemented a vehicle lottery system (VLS) in 2011 that randomly allocates a quota of license plates to lottery participants. Using the car registration data, we examine the consumption response at the beginning of the restriction. Our analysis reveals that VLS effectively eliminated over one million additional car registrations in two years, however it also increased purchases of cars that are heavier, have larger engines, and are less fuel-efficient. We highlight that the change was associated with a greater reduction in the retail price of luxury cars compared with economy cars post-VLS implementation. Through a difference-in-differences (DID) analysis, we show that the average retail price in Beijing dropped by an additional 2 percent (about $0.4k) for economy cars and 5.8 percent (around $4.3k) for luxury cars, compared to cities without VLS. This price change increased the attractiveness of luxury cars relatively to economy cars. Furthermore, we build a structural model to estimate the consumer demand on vehicles as well as to simulate and compare other potential policies. Given the drawback of VLS that partially offsets the policy goal of emission elimination, we propose a market-based policy to save fuel consumption while achieving the same target of controlling vehicle growth as VLS.

Optimizing location-routing and demand allocation in the household waste collection system using a branch-and-price algorithm

In catering to the rapid development of urbanization and the growing urban population, it is essential to design an efficient and effective household waste collection system for guaranteeing a favorable ecological environment in cities. This paper focuses on a location-routing and demand allocation problem (LRDAP) in the household waste collection system where the household waste is initially allocated and transported to a set of intermediate transfer stations using a category of small garbage vehicles, and subsequently a fleet of large garbage vehicles visits each transfer station and transports the collected household waste to the waste disposal center for harmless treatment. The purpose is to simultaneously determine the location of transfer stations, the demand allocation of household waste collection and the routing plan of garbage vehicles while maximizing the efficiency of household waste collection within the whole system. Then, we formulate the proposed LRDAP into a mixed integer program. To solve our LRDAP, an exact branch-and-price (B&P) algorithm is developed along with several acceleration strategies involving column generation (CG) stabilization and heuristic dynamic programming (DP). Finally, we illustrate the proposed model and solution methodology by dealing with real-world problem instances with respect to household waste collection in China. Computational results are presented that explore the performance of our proposed B&P algorithm, analyze the optimized location-routing and demand allocation plan, discuss the impact of selected parameters, and gain some managerial insights in designing an efficient and effective household waste collection system under different decision-making conditions.

Distributional impacts of fleet-wide change in light duty transportation: mortality risks of PM2.5 emissions from electric vehicles and Tier 3 conventional vehicles

Abstract Light-duty transportation continues to be a significant source of air pollutants that cause premature mortality and greenhouse gases (GHGs) that lead to climate change. We assess PM2.5 emissions and its health consequences under a large-scale shift to electric vehicles (EVs) or Tier-3 internal combustion vehicles (ICVs) across the United States, focusing on implications by states and for the fifty most populous metropolitan statistical areas (MSA). We find that both Tier-3 ICVs and EVs reduce premature mortality by 80%–93% compared to the current light-duty vehicle fleet. The health and climate mitigation benefits of electrification are larger in the West and Northeast. As the grid decarbonizes further, EVs will yield even higher benefits from reduced air pollution and GHG emissions than gasoline vehicles. EVs lead to lower health damages in almost all the 50 most populous MSA than Tier-3 ICVs. Distributional analysis suggests that relying on the current gasoline fleet or moving to Tier-3 ICVs would impact people of color more than White Americans across all states, levels of urbanization, and household income, suggesting that vehicle electrification is more suited to reduce health disparities. We also simulate EVs under a future cleaner electric grid by assuming that the 50 power plants across the nation that have the highest amount of annual SO2 emissions are retired or retrofitted with carbon capture and storage, finding that in that case, vehicle electrification becomes the best strategy for reducing health damages from air pollution across all states.