Large Vehicle Fleets Research Articles

Verification of the HDM-4 fuel consumption model using a Big data approach: A UK case study

This paper presents an assessment of the accuracy of the HDM-4 fuel consumption model calibrated for the United Kingdom and evaluates the need for further calibration of the model. The study focuses on HGVs and compares estimates made by HDM-4 to measurements from a large fleet of vehicles driving on motorways in England. The data was obtained from the telematic database of truck fleet managers (SAE J1939) and includes three types of HGVs: light, medium and heavy trucks. Some 19,991 records from 1645 trucks are available in total. These represent records of trucks driving at constant speed along part of the M1 and the M18, two motorways in England.These conditions have been simulated in HDM-4 by computing fuel consumption for each truck type driving at a constant speed of 85 km/h on a flat and straight road segment in good condition.Estimates are compared to real measurements under two separate sets of assumptions. First, the HDM-4 model calibrated for the UK has been used. Then, the model was updated to take into account vehicle weight and frontal area specific to the considered vehicles.The paper shows that the current calibration of HDM-4 for the United Kingdom already requires recalibration. The quality of the model estimates can be improved significantly by updating vehicle weight and frontal area in HDM-4. The use of HGV fleet and network condition data as described in this paper provides an opportunity to verify HDM-4 continuously.

Development of Robust Fault Signatures for Battery and Starter Failure Prognosis

Battery and starter are crucial vehicle components, whose failures may cause customers to be stranded. To enhance customer satisfaction and improve dealership serviceability, the failure prognosis and fault isolation for battery or starter are very important. In order to develop a robust diagnostic and prognostic solution, in this work, the feature extraction algorithms are developed to extract two fault signatures, namely battery charging resistance equivalent and battery cranking resistance ratio. The algorithms are based on the equivalent circuit model for the battery and starter system, the battery empirical model, and the field knowledge about the driver’s behavior and battery management system. The proposed solution is a passive approach, and does not require any additional sensors for GM vehicles, or expensive computing hardware. Therefore, it is suitable for both onboard and off-board implementation. The solution has been validated with large fleet of vehicles under different scenarios, and implemented for selected GM vehicles through the OnStar™ Proactive Alerts service.

ASSESSING TEMPORAL BEHAVIOR IN LIDAR POINT CLOUDS OF URBAN ENVIRONMENTS

Abstract. Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

EXTRACTING LANE GEOMETRY AND TOPOLOGY INFORMATION FROM VEHICLE FLEET TRAJECTORIES IN COMPLEX URBAN SCENARIOS USING A REVERSIBLE JUMP MCMC METHOD

Abstract. Highly automated driving (HAD) requires maps not only of high spatial precision but also of yet unprecedented actuality. Traditionally small highly specialized fleets of measurement vehicles are used to generate such maps. Nevertheless, for achieving city-wide or even nation-wide coverage, automated map update mechanisms based on very large vehicle fleet data gain importance since highly frequent measurements are only to be obtained using such an approach. Furthermore, the processing of imprecise mass data in contrast to few dedicated highly accurate measurements calls for a high degree of automation. We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today’s connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.

Computing Feasible Vehicle Platooning Opportunities for Transport Assignments**This work was supported by the COMPANION EU project, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council.

Abstract: Vehicle platooning facilitates the partial automation of vehicles and can significantly reduce fuel consumption. Mobile communication infrastructure makes it possible to dynamically coordinate the formation of platoons en route. We consider a centralized system that provides trucks with routes and speed profiles allowing them to dynamically form platoons during their journeys. For this to work, all possible pairs of vehicles that can platoon based on their location, destination, and other constraints have to be identified. The presented approach scales well to large vehicle fleets and realistic road networks by extracting features from the transport assignments of the vehicles and rules out a majority of possible pairs based on these features only. Merely a small number of remaining pairs are considered in depth by a complete and computationally expensive algorithm. This algorithm conclusively decides if platooning is possible for a pair based on the complete data associated with the two vehicles. We derive appropriate features for the problem and demonstrate the effectiveness of the approach in a simulation example.

Vehicle-Originating-Signals for Real-Time Charging Control of Electric Vehicle Fleets

We propose the vehicle-originating-signals (VOS) approach for charging control of a fleet of electric vehicles (EVs) in an electricity distribution network (DN). The goal of the approach is to manage the EV load such that the aggregate power consumption, including inflexible demand and solar generation, closely follows a given target profile. The VOS approach enables EVs in a fleet to compute signals reflecting their need for charge and willingness to supply power. An aggregator collects these signals and implements the control with minor computational effort. We evaluate the VOS approach on a scenario for Munich, Germany, based on a mobility survey and real electricity demand and solar generation data. First, we compare our approach against a centralized optimization in terms of objective fulfillment and solving time. We show that it achieves a competitive performance, especially for large vehicle fleets. Second, we present a statistical method to evaluate the performance and limitations of our approach. We identify a recommended range of the vehicle fleet size for a given load magnitude and show that the target profiles can be met with a defined certainty. Finally, we present a method for further reducing the communication overhead with minor effect on performance.

In This Issue

In This Issue

Electric vehicles as flexible loads – A simulation approach using empirical mobility data

Due to the rapid increase of wind and photovoltaic generation, flexible storage applications become more important. Electric vehicles are supposed as one option to fill the gap between a fixed energy demand and a stochastic feed in from fluctuating energy sources. But the charging loads will also affect the grid load, since the transport sector contributes considerably to the total energy consumption today. This study examines the conflicting relationship between user mobility and grid support and introduces an approach to simulate large vehicle fleets on the basis of individual driving profiles.9744 driving profiles from the German mobility panel were used within this examination. 958 were classified as potential early adopters for electric vehicles. Those vehicles could provide grid support in 81% of the time, when charging spots are available at home and at work. We simulated the charging loads under the restrictions of the individual mobility for the scenario 2030. Uncoordinated charging will increase the load fluctuations, whereas coordinated charging loads allow load shifting without limiting the mobility. The additional electricity demand is moderate over the next two decades.

The Brazilian Experience with Biofuels (Innovations Case Narrative)

biofuels in the internal combustion engines on our roads. In the late 1800s, Henry Ford used ethanol to drive automobiles and Rudolf Diesel used biodiesel from peanuts to drive trucks. But these fuels were replaced in the early 1900s by gasoline and diesel oil distilled from petroleum; they became cheap and seemingly inexhaustible in the United States and a few other countries with easy access to oil. Meanwhile biofuels, particularly ethanol (also an alcoholic beverage), were expensive and produced in minor amounts compared to the huge quantities needed for large vehicle fleets. Oil-poor countries, including Brazil, had a different experience. Since 1920, Brazilians have conducted technical studies on ethanol-run vehicles, including racing cars; because ethanol works so well as a fuel it makes a good alternative to imported gasoline. In 1931, a Brazilian law required that all gasoline include 5 percent bioethanol from sugarcane and the government regulated prices to make it possible. Over the years the blend remained almost constant and was slowly raised to 7.5 percent. Such blends did not require changes in the engines. In the early 1970s, Brazil imported all of its gasoline and petroleum from abroad, at an annual cost of U.S. $600 million. In 1973, with the first oil shock, imports rose to more than $4 billion annually, contributing greatly to the deficit in hard currency and badly damaging the economy.

Solving the open vehicle routeing problem via a single parameter metaheuristic algorithm

In this paper, we consider the open vehicle routeing problem (OVRP), in which routes are not sequences of locations starting and ending at the depot but open paths. The problem is of particular importance for planning fleets of hired vehicles, a common practice in the distribution and service industry. In such cases, the travelling cost is a function of the vehicle open paths. To solve the problem, we employ a single-parameter metaheuristic method that exploits a list of threshold values to guide intelligently an advanced local search. Computational results on a set of benchmark problems show that the proposed method consistently outperforms previous approaches for the OVRP. A real-world example demonstrates the applicability of the method in practice, demonstrating that the approach can be used to solve actual problems of routing large vehicle fleets.

Routing Optimization for Waste Management

Waste Management (WM) obtains one third of its revenue from landfill disposals and two-thirds from waste-collection services. As most of the revenue comes from collecting trash, improving efficiency in operating the fleet improves the bottom line. After a flurry of acquisitions and a merger with USA Waste, WM found itself with a large fleet of vehicles whose routing, dispatching, maintenance, and management were decentralized. WM recognized that it could reduce operating costs by improving its use of assets. It contracted with the Institute of Information Technology to develop WasteRoute, a comprehensive route-management system that took into account WM's specific routing concerns and provided broad benefits. Initially, the target audience of the system was the dispatchers and indirectly the drivers. Sales and customer service also benefited because WasteRoute integrated the sales, customer service, and operations departments. The system reduced operating costs, provided better customer service, and determined appropriate prices. WM deployed WasteRoute across the nation beginning in March 2003. By the end of 2003, WM had 984 fewer routes, saving $18 million. It estimated that its savings for 2004 due to the reduction will be $44 million. As it extends the system to additional areas, it expects additional route reductions.

Corrosion Experience with a Large Vehicle Fleet

Corrosion Experience with a Large Vehicle Fleet