Delivery Vans Research Articles

Onboard power systems based on hot water energy storage for green vehicles as an alternative to battery electric vehicles

This paper introduces the concept of onboard hot-water-storage-based power systems for green vehicles. The hot water at a moderately high temperature is stored onboard vehicles and its thermal energy is used to produce wheelwork through a heat engine to drive vehicles without combustion. The hot water’s wheelwork storage density matches or exceeds that of battery packs of electric vehicles (EVs). When hot water is produced by solar energy, its overall solar utilization efficiency is more than 1.5 times higher than that of battery EVs with a 60% land-use reduction. The design and integration of hot-water storage modules for semi-trucks, delivery vans, and SUVs are demonstrated with detailed technical calculations. The results show that hot-water vehicles (HWV) can outperform EVs in terms of driving ranges. The HWV could be a truly renewable and clean vehicle and a direction to achieve sustainability with positive impacts on the earth and transportation industries.

Operational performance of light electric freight vehicles in the last mile: two Nordic case studies

PurposeTo assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.Design/methodology/approachCase studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.FindingsThe results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.Research limitations/implicationsInherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.Practical implicationsAdding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.Originality/valueOne novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

Beyond noise levels: vehicle classification using psychoacoustic indicators from pass-by road traffic noise and their correlations with speed and temperature

Environmental noise control regulations typically employ noise level descriptors to set limits for noise exposure. However, other characteristics of noise, such as frequency content, temporal patterns and masking, have been proven to influence the perception of acoustic environments. In this sense, psychoacoustic indicators offer an objective means of establishing relationships between physical characteristics of noise and the human auditory sensation phenomena. This study explored psychoacoustic indicators of pass-by vehicle noise across different vehicle categories, driving speeds, and temperatures. Moreover, the indicators were exploited as features to train a classification algorithm to predict vehicle category. Over 2000 vehicle noise samples were collected using the Statistical Pass-By (SPB) method, categorized into three classes according to ISO 11819-1, besides an additional class for delivery vans. Correction coefficients were obtained for temperature and speed to noise levels, loudness, roughness, sharpness and fluctuation strength. The differences in these indicators based on vehicle category were then discussed. A vehicle-category predictive model using the three vehicle categories defined in ISO 11819-1 yielded 84% accuracy. Including vans as an extra vehicle category dropped accuracy to 72% due to their misclassification with passenger cars. Combining these two categories increased overall accuracy to 86%. These findings could enable a less visual-dependent vehicle categorization so that vehicle fleets worldwide are more consistently classified in terms of noise. Additionally, psychoacoustic indicators appear to be valuable features for vehicle classification systems aimed to resemble the human auditory experience.

Safely Controlling Commercial Traffic

Luxembourg’s road traffic authority has opted for Kistler’s WIM technology, which weighs trucks and delivery vans without interrupting motorway traffic, to help it meet new EU commercial vehicle enforcement regulations

Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise

A set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology (ISO 11819-1:2022). Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of the three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72.5%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. Pooling these two categories increased accuracy to 82%. With more descriptors from road traffic pass-by noise than uniquely its maximum noise level, limiting definitions of vehicle categories may be overcome. As a result, measurements such as the SPB can become broader and vehicle fleets worldwide more consistently represented in terms of noise perception.

Comparison of spatial interpolation techniques for innovative air quality monitoring systems

Different integration methods were tested to integrate data from a dynamic road network (ROM) in which pollution measurement sensors were mounted over delivery vans. Two methods were purposely developed, the isoelliptical expansion - ISOE - method accounting for the wind convective transport of pollutants and the modified isoelliptical expansion - MISOE - method in which, furthermore, local specific deviation of the pollution are estimated from historical sequences of pollution levels. The results obtained by these methods were compared with the well-known inverse distance weighted - IDW - method, which is only based on the distance from the interpolation sources. The comparison of the errors between the estimated values and the available measures reveals that the MISOE model provides more accurate estimated values with a low associated error. The ISOE model is more complicate than the IDW but provides better estimations in windy days. The maps of the local adjusting coefficients estimated month by months are able to identify critical areas to address in local environmental policy decisions.

Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection.

Performance Comparison of Three Rival AI-Powered Intelligent Trajectory Tracking Controllers for an Autonomous Delivery Van

Due to the advent of E-commerce businesses, delivery vehicle traffic has grown by 71% over the last two decades. Hence, the automation of vans has received special attention to improve road safety. This study compares the performance of a trajectory tracking controller powered by three rival AI-based techniques namely, Particle Swarm, Genetic Algorithm and Ant Colony. Through scenario-based testing, all three techniques are compared from the tracking performance, algorithm complexity, accuracy and precision, and simplicity in finding the optimal weighting factors of the lateral position error and heading angle error which define the desired lateral controller output. Taken individual performance, ant colony powered fuzzy tracking controller was superior in terms of computational time, accuracy, and precision whereas, poor in ease of programming and explainability.

Stan Is THE Van: Mobile Makerspace a Hit in Ohio

Stan the Tech Van is an experience on wheels belonging to Madison (OH) Public Library (MPL). It’s not just a 2022 Ram Promaster 2500 cargo van; it can hold an FDM 3D printer along with other tech on carts in the back. We chose a high roof delivery van to allow any staff member the ability to drive Stan for a program or event since it does not require a commercial driver’s license. Stan is outfitted with delivery shelving, a roof rack, and an awning package as well as a powered Gramps Aluminum Gated Commercial Van Ramp System. It’s more than a vehicle that holds enough LEGO Robotics that a staff member doesn’t have to clean out his or her car to get a classroom set out to a school. It’s a mobile makerspace that holds all types of machines that anyone can use to create all sorts of projects.

High Volume Fuel Cell Stack Manufacturing

Plug Power is a world leader in fuel cell stack and fuel cell systems manufacturing, with over 50,000 fuel cells in the field for materials handling and stationary power applications. Plug Power is using this success to develop new markets in range-extenders for delivery vans, heavy duty vehicles, and aerospace. The rapid growth of Plug Power’s fuel cell technology has brought new challenges with high volume manufacturing. An individual cell needs to be made in less than 5 seconds, and then sealed and stacked rapidly and reproducibly.This talk will review some of the general approaches that Plug Power is taking toward high-volume stack manufacturing. We will also review two programs funded by the U.S. Department of Energy on high volume manufacturing, and novels ways toward low cost, high volume manufacturing.

Measurement and analysis of low‐acceleration and long‐duration longitudinal events using delivery van

This study investigates longitudinal acceleration events during freight transportation characterized as low‐acceleration and long‐duration using delivery van type vehicles. In the past several decades, there has been an increase in shipments requiring only single or small pallet load quantities and mixed palletized unit loads comprised of different goods. These loads are often transported in delivery vans without load securing devices, increasing the risk of product loss and damage due to load failures resulting from unit loads shifting. A field data acquisition system was used to observe and record the random acceleration events from five vehicles for 5 days, explicitly targeting the vehicles' braking and acceleration manoeuvres. The study aimed to understand the physical phenomenon and provide new information that can be used during preshipment tests to prevent damage to goods and ensure unit load integrity is maintained throughout the supply chain. The events were statistically analysed to understand their probability of occurrence, severity level, and quantify critical parameters such as event rise and hold times. For the braking manoeuvre, the statistical mean of average deceleration was 0.25 g with a corresponding rise and hold duration of 0.83 and 1.27 s, respectively. During the vehicle's acceleration manoeuvre, the statistical mean of average acceleration was 0.29 g with a rise and hold time of 1.29 and 1.39 s, respectively. Utilizing the field data, composite profiles were developed, and these profiles were compared to the currently available test procedures and previous results of other studies.

Exploring the Potential Impact of Crowdshipping Using Public Transport in Singapore

Because of increased urbanization and a rise in e-commerce activities, the demand for freight transport in cities is growing. E-retailers and logistics companies face the challenge of making timely, cost-effective, and reliable deliveries. To address this problem, the concept of crowdshipping has gained interest. Crowdshipping enlists members of the public to pick up and drop off parcels, ideally en route to their pre-committed destination. Individuals, such as commuters using public transport, can be engaged to fulfill deliveries as “crowdshippers.” This research explores the potential impact of crowdshipping using public transport in a congested city, focusing on Singapore as a case study. First, an online survey was conducted to investigate the willingness of transit passengers to act as crowdshippers. Then, a parcel allocation model was developed to match parcels with public bus journeys. In the case of there not being enough crowdshippers available, the remaining parcels will be delivered using the carrier’s delivery vans, which are subject to a vehicle routing problem. Using real-world data, we compared the performance of an e-commerce carrier with and without crowdshipping. The results show that crowdshipping can reduce vehicle kilometers traveled and associated air emissions by up to 17%. The carrier’s delivery cost can be reduced by up to 29% per parcel. These savings can be realized even if crowdshipping is limited to off-peak travel hours only. In a congested city with an extensive transit system, crowdshipping using public transport has the potential to be a sustainable means of moving goods in urban areas.

Land Efficient Mobility: Evaluation of Autonomous Last Mile Delivery Concepts in London.

Land efficient last mile delivery concepts are key to reducing the traffic in cities and to minimising its environmental impact. This paper proposes a decision support method that evaluates the autonomous delivery concept and applies it to one year’s worth of real parcel delivery data in London. Deliveries to modular and fixed lockers with autonomous delivery vans and road-based autonomous lockers (RAL) and sidewalk autonomous delivery robots (SADRs) have been simulated. Various types of autonomous delivery van fleets, depot locations, customer modes of transport, parcel demand levels, parcel locker network densities and adjustment frequencies of modular lockers are considered. A routing and scheduling algorithm is used to optimise delivery tours and vehicle choice. The optimisation algorithm finds both the optimal number of collection and delivery points (CDPs) and the delivery concept (e.g., modular lockers, sidewalk autonomous delivery robot) depending on the customer mode chosen. The results show that modular lockers which are adjusted weekly are the best option for the current or higher parcel demand levels and road-autonomous parcel lockers (RAL-R) are the best option at the lowest parcel demand level.

Liquid pump-enabled hydrogen refueling system for medium and heavy duty fuel cell vehicles: Station design and technoeconomic assessment

Recent progress in submerged liquid hydrogen (LH2) cryopump technology development offers improved hydrogen fueling performance at a reduced cost in medium- and heavy-duty (MDV and HDV) fuel cell vehicle refueling applications at 35 MPa pressure, compared to fueling via gas compression. In this paper, we evaluate the fueling cost associated with cryopump-based refueling stations for different MDV and HDV hydrogen demand profiles. We adapt the Heavy Duty Refueling Station Analysis Model (HDRSAM) tool to analyze the submerged cryopump case, and compare the estimated fuel dispensing costs of stations supplied with LH2 for fueling Class 4 delivery van (MDV), public transit bus (HDV), and Class 8 truck (HDV) fleets using cryopumps relative to station designs. A sensitivity analysis around upstream costs illustrates the trade-offs associated with H2 production from onsite electrolysis versus central LH2 production and delivery. Our results indicate that LH2 cryopump-based stations become more economically attractive as the total station capacity (kg dispensed per day) and hourly demand (vehicles per hour) increase. Depending on the use case, savings relative to next best options range from about 5% up to 44% in dispensed costs, with more favorable economics at larger stations with high utilization.

CES 2022: Less is More

AbstractScaled‐down show still delivers top‐notch exhibits and innovation aplenty.

Packaging choice and coordinated distribution logistics to reduce the environmental footprint of small-scale beer value chains

This study assesses the extent to which packaging and distribution impacts can be mitigated as environmental hotspots in the life cycle of micro-brewed beer. We conduct life cycle assessment (LCA) of seven breweries and compare their existing packaging and distribution practises with three mitigation options; use of aluminium cans or reusable glass bottles instead of single use glass bottles or use of polyethylene terephthalate (PET) kegs instead of steel kegs. Findings show that all participating breweries can achieve reductions across multiple impact categories if single use glass bottles are changed to aluminium cans or reusable glass, and further reductions are possible if mode of transport is changed from small delivery vans to lorries for distribution to retailers. The use of PET keg as an alternative to reusable steel keg is a less environmentally sustainable option when beer is delivered short distances, but some savings are possible in long distance scenarios using vans. Carbon footprints per litre beer range from 727 to 1336 g CO2 eq. across the case study breweries, with reductions of 6–27% or 3–27% by changing to aluminium can or reusable glass bottle, respectively, when beer is delivered by van. The optimal combination of reusable glass bottle delivered by lorry reduces carbon footprints by between 45 and 55% but will require significant investment and coordination across the wider food and drink sector to implement. Identifying the best packaging material requires a holistic approach that considers interactions and burdens across packaging manufacturing, distribution, use and end-of-life stages.

Impact of climatic conditions on prospects for integrated photovoltaics in electric vehicles

Integrated photovoltaics are an emerging technology which can extend the range of electric vehicles. However, up to now there is a lack of a consensus method that would provide consumers with an estimate of the fraction of annual driving which could be covered by solar power generated onboard in different usage scenarios and locations. To address this, we assess the energy implications of vehicle integrated photovoltaics for a commuter car and light delivery van for six climatic regions and for typical daily usage profiles over a ten-year period. The analysis captures the energy needs for driving and cabin temperature control to an unprecedented level of detail. Our results reveal that the grid power needed to drive such vehicles on identical routes can vary by more than 44% between climate regions. In the best case the solar power generated can cover up to 35% of the driving range per year. This contribution can vary by a factor of 2.5 between different climates, from 1800 to 5100 km annually, considerably mitigating the effect of ambient conditions on electric range. We propose developing consumer labels for solar electric vehicles that would consider this variation according to climatic conditions. The method described in this paper could help to frame initial discussions for such labels. We identify further requirements for research and development, standardisation, and policy needs.

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher. To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively long period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.

A framework for determining energy use in rural food delivery services: capturing system interdependencies through an agent-based discrete-event approach

Abstract Food e-commerce has seen significant growth over the past decade that accelerated after the onset of the COVID-19 pandemic. Last-mile transportation and logistics are widely considered the most expensive and least efficient portion of the supply chain and have multiple important energy trade-offs such as cargo capacity and consumer density. Last-mile transportation energy use in rural areas is underrepresented in the literature. This study proposes a hybrid agent-based and discrete event model framework for evaluating the last-mile transportation energy use of van- and car-based food delivery services in a rural community, based on meal-kit and grocery delivery operations, respectively. This framework quantifies last-mile energy use in rural areas, and is demonstrated here using a neighborhood outside of Austin, TX as an analytical testbed. The study focuses on the effects of consumer density, cargo limitations, and vehicle speed. For the conditions examined with this framework, diesel delivery vans use more total energy than passenger cars for the same trip, though a van delivering four orders uses less energy per-order than a car delivering one order. However, there are trade-offs between vehicle type and mileage, cargo capacity, route density, and speed that are particularly important for delivery services operating in rural areas. This framework can be used by service providers to assess route-specific trade-offs for each vehicle and gauge which is preferable for given operating conditions or to evaluate the energy, and thus also cost, impact of expanding their services to rural areas.