Driving cycles are acting as the basis of the evaluation of the vehicle performance from air quality point of view, such as fuel consumption or pollutant emission, especially in emission modeling and emission estimation. The original definition of the driving cycle, or drive schedule, given by U.S. Environmental Protection Agency (EPA), is basically a speed-time trajectory which is able to describe the general driving characteristics and driving patterns. Therefore, the development of drive cycles requires a large amount of real data to realize such “generalization”. Then, with such the eligible data collected, it leads to the development of modeling, from traffic modeling to emission modeling, especially for those pollutant emissions which have the public concern. In this study, focused on heavy duty diesel vehicles (HDDVs), the estimations of the common emissions are being made based on the Texas specific drive cycles, in second-by-second form, collected and generated from five local metropolitan areas, including Houston, Austin, San Antonio, Dallas-Fort Worth and El Paso. First of all, the accurate Global Positioning System (GPS) logging technique is applied for data collection in order to collect not only the moving data but also the relevant geographical information, such as location and roadway, for further analysis. Then, during the progress of data cleaning and data processing, some modifications are made subjectively to improve the deficits of the general methodologies developed by EPA. Afterwards, the specific drive cycles are presented in the format of operating mode distributions, which are also the main part of the input during the emission estimation in Motor Vehicle Emission Simulator (MOVES). Along with all the Texas specific inputs prepared, both the rates and amount of studied emissions are estimated through MOVES. A further comparison is made between the emission rates of default analysis and local analysis to verify the accuracy of MOVES at project level. It is found that the default estimation made by MOVES is accurate for mid-speed cases, at magnitude level. Significant differences happened in low-speed cases and high-speed cases, in which it shows the importance to develop the local drive cycles when estimating the emission rates regionally.

Indiana University-Purdue University Indianapolis (IUPUI) / Understanding the operating profile of different heavy-duty vehicles is needed by parts manufacturers for improved configuration and better future design of the parts. This study investigates the use of a tournament classification approach for both vocation and fleet identi- fication. The proposed approach is implemented using four different classification techniques, namely, K-Means, Expectation Maximization, Particle Swarm Optimization, and Support Vector Machines. Vocations classifiers are developed and tested for six different vocations ranging from coach buses to rail inspection vehicles. Operational field data are obtained from a number of vehicles for each vocation and aggregated over a pre-set distance that varies according to the data collection rate. In addition, fleet classifiers are implemented for five fleets from the coach bus vocation using a similar approach. The results indicate that both vocation and fleet identification are possible with a high level of accuracy. The macro average precision and recall of the SVM vocation classifier are approximately 85%. This result was achieved despite the fact that each vocation consisted of multiple fleets. The macro average precision and recall of the coach bus fleet classifier are approximately 77% even though some fleets had similar operating profiles. These results suggest that the proposed classifier can help support vocation and fleet identification in practice.

An increasing need for goods and passenger transportation drives continued worldwide growth in traffic. As traffic increases environmental concerns, traffic safety, and cost efficiency become ever more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control, and hybrid vehicle state-of-charge control decrease the energy consumption of vehicles and increase the safety. These control systems can benefit significantly from preview road grade information. This information is currently obtained using specialized survey vehicles, and is not widely available. This thesis proposes new methods to obtain road grade information using on-board sensors. The task of creating road grade maps is addressed by the proposal of a framework where vehicles using a road network collect the necessary data for estimating the road grade. The estimation can then be carried out locally in the vehicle, or in the presence of a communication link to the infrastructure, centrally. In either case the accuracy of the map increases over time, and costly road surveys can be avoided. This thesis presents a new distributed method for creating accurate road grade maps for vehicle control applications. Standard heavy duty vehicles in normal operation are used to collect measurements. Estimates from multiple passes along a road segment are merged to form a road grade map, which improves each time a vehicle retraces a route. The design and implementation of the road grade estimator are described, and the performance is experimentally evaluated using real vehicles. Three different grade estimation methods, based on different assumption on the road grade signal, are proposed and compared. They all use data from sensors that are standard equipment in heavy duty vehicles. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver, using vehicle and road models. The operation of the estimators is adjusted during gearshifts, braking, and poor satellite coverage, to account for variations in sensor and model reliability. The estimated error covariances of the road grade estimates are used together with their absolute positions to update a stored road grade map. Highway driving trials show that the proposed estimators produce accurate road grade data. The estimation performance improves as the number of road segment traces increases. A vehicle equipped with the proposed system will rapidly develop a road grade map for its area of operation. Simulations show that collaborative generation of the third dimension for a pre-existing large area two-dimensional map is feasible. The experimental results indicate that road grade estimates from the proposed methods are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort in heavy duty vehicles. The grade estimators may also be used for on-line validation of road grade information from other sources. This is important in on-board applications, since the envisioned control applications can degrade vehicle performance if inaccurate data are used. / QC 20110408

<p>The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods.</p> <p> </p> <p>These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset.</p> <p> </p> <p>The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.</p>

Increasing demands for lower environmental impact from vehicles, including heavy-duty vehicles, have driven several vehicle manufacturers to consider adding hybrid electrical vehicles (HEV’s) to the product portfolio. Present research on batteries for HEV’s is mainly focused on lithium-ion battery chemistries, since lithium-ion batteries has the most promising technical potential compared to other types of batteries. However, the uncertainty regarding battery lifetime combined with a high battery cost can have a negative impact on large scale commercialisation of heavy-duty hybrid vehicles in the near future. A large part of present lithium-ion battery research is focused on new materials, but there is also research focusing on ageing of already established lithium-ion battery chemistries. Cycle ageing of batteries often includes complete charging and discharging of batteries or the use of standardized test cycles. Battery cycling in real HEV applications is however quite different compared to this kind of laboratory testing, and real life testing on vehicles is a way of verifying the soundness of laboratory ageing. The aim of this study was to develop a test method suitable for real life testing of lithium-ion batteries for heavy-duty HEV-usage, with the purpose of investigating the correlation of battery ageing and usage in real life applications. This concept study includes both cell level battery cycling and performance testing on board vehicles. The performance tests consist of discharge capacity measurements and hybrid pulse power characterization (HPPC) tests. The main feature of this test equipment is that it is designed to be used on conventional vehicles, emulating an HEV environment for the tested battery. The functionality of the equipment was verified on a heavy-duty HEV with satisfying results. Results from real life testing of 8 batteries using the developed test equipment on four conventional heavy-duty trucks shows that the concept of comparing battery ageing with battery usage has a most promising potential to be used as a tool when optimizing battery usage vs. lifetime. Initial results from this real life study shows significant differences in state of charge (SOC) and power distributions between cycled batteries, but so far only small differences in ageing. Lithium-ion batteries of the type lithium manganese spinel/lithium titanate (LMO/LTO) were used in this study. / Ökande krav på minskad miljöpåverkan från fordon, inklusive tunga fordon, har drivit flera fordonstillverkare till att addera hybridiserade fordon till produktportföljen. Forskning på hybridfordonsbatterier är idag huvudsakligen inriktad på litiumjonbatterikemier, vilken har den mest lovande tekniska potentialen jämfört med andra typer av batterikemier. Det finns idag en risk att osäkerheten kring litiumjonbatteriers livslängd i kombination med en hög batterikostnad kan ha en negativ inverkan på en storskalig kommersialisering av tunga hybridfordon inom den närmsta framtiden. En stor del av batteriforskningen är inriktad på nya material, men det finns även forskning som fokuserar på åldring av redan etablerade litiumjonbatterikemier. Vid åldringsprov används ofta standardiserade testcykler eller cykler där batterierna blir fullständigt laddade och urladdade. Cykling av batterier i verkliga förhållanden skiljer sig dock från den typen av laboratorietester och provning på fordon är därför ett sätt att kontrollera att laboratorieprovning ger relevanta resultat gällande åldring. Syftet med denna studie var att utveckla en testmetodik lämplig för provning av litiumjonbatterier för tunga hybridfordon i verklig drift, med syfte att undersöka kopplingen mellan batteriers åldrande och hur det används. Detta koncept inkluderar battericykling på cellnivå och möjligheten att utföra batteriprestandatester på fordon, där prestandatesterna består av kapacitetsprov och pulsprov. Den viktigaste egenskapen hos den utvecklade testmetodiken är att provning sker på konventionella fordon genom att emulera en hybridmiljö för det testade batteriet. Funktionaliteten hos den utvecklade testutrustningen verifierades på en tung hybridlastbil med goda resultat. Resultaten från en fältstudie av 8 batterier på 4 lastbilar där den utvecklade testutrustningen användes påvisar att testmetodiken har en lovande potential att kunna användas som ett verktyg vid optimering av utnyttjandegrad och livslängd för HEV-batterier. De initiala resultaten från denna fältstudie påvisar skillnader i laddningsgradsfördelning och batterieffektfördelning mellan cyklade batterier, men ännu bara små skillnader i åldring. Litiumjonbatterier av typen litiummanganspinel/litiumtitanat (LMO/LTO) användes i denna studie. / QC 20111205

Autonomous vehicles is a rapidly expanding field, and promise to play an important role in society. In more isolated environments, vehicle automation can bring significant efficiency and production benefits and it eliminates repetitive jobs that can lead to inattention and accidents. The thesis addresses the problem of lateral and longitudinal dynamics control of autonomous ground vehicles with the purpose of accurate and smooth path following. Clothoids are used in the design of optimal predictive controllers aimed at minimizing the lateral forces and jerks in the vehicle. First, a clothoid-based path sparsification algorithm is proposed to efficiently describe the reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path. Second, a clothoid-based model predictive controller (MPCC) is proposed. This controller aims at producing a smooth driving by taking advantage of the clothoid properties.  Third, we formulate the problem as an economic model predictive controller (EMPC). In EMPC the objective function contains an economic cost (here represented by comfort or smoothness), which is described by the second and first derivatives of the curvature.  Fourth, the generation of feasible speed profiles, and the longitudinal vehicle control for following these, is studied. The speed profile generation is formulated as an optimization problem with two contradictory objectives: to drive as fast as possible while accelerating as little as possible. The longitudinal controller is formulated in a similar way, but in a receding horizon fashion. The experimental evaluation with the EMPC demonstrates its good performance, since the deviation from the path never exceeds 30 cm and in average is 6 cm. In simulation, the EMPC and the MPCC are compared with a pure-pursuit controller (PPC) and a standard MPC. The EMPC clearly outperforms the PPC in terms of path accuracy and the standard MPC in terms of driving smoothness. / <p>QC 20160503</p> / iQMatic

Indiana University-Purdue University Indianapolis (IUPUI) / The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods. These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset. The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.

Background: Climate changes and global warming call for behaviour changes from mankind and for new business models to introduce sustainable innovations. Financial analysis plays an important role in guiding the choice of these business models. However, assumptions and uncertainties pose challenges to the use of financial analysis in business modelling. Purpose: The purpose of this study is to develop a proactive systematic approach of financial analysis in business modelling. Accounting for the important role of assumptions and uncertainty factors, the approach should guide the choices of capital structure, revenue model, and strategic partnerships in the business model. Methodology: The developed approach combines the use of different methods to assess different business models for a public fast-charging stations for electric heavy-duty vehicles in Sweden. The used techniques are DCF analysis, What-If analysis, Tornado diagram, Monte-Carlo simulation, and multiple linear regression analysis. Results and analysis: Applied to the case of a public fast-charging station for electric heavy-duty vehicles, the approach leads to the identification of potential viable business models. Under the condition of using financial leverage through debt, additional revenue sources such as per-charge event user fee and advertising as well as partnership with the public sector in the form of grants, it is possible to achieve a viable business model. Conclusions: A systematic proactive approach of the use of financial analysis in business modelling was successfully developed and applied to the case of fast-charging stations for electric heavy-duty vehicles. The identified viable business models rely on financial leverage through debt, additional revenue sources and partnership with the public sector in the form of grants. Recommendations for future research: Simulations with more input parameters as well as combinations with observational studies of existing business models can be further investigated.

Wading is the phenomenon where a vehicle drives through water with a relatively deep water level. Sincea large portion of the vehicle is submerged in water it can affect the driveability and function of individualcomponents. Wading is therefore an important phenomenon to be aware of especially today where society moves towards alternative energy sources. This includes water sensitive components when contact with water can generate major consequences. Previous knowledge and experience of wading has been from performing physical tests, but using Computational Fluid Dynamics (CFD) to examine the phenomenon can accelerate the iterative design process. In this thesis, numerical method of wading simulations on complete heavy-duty vehicles using the software STAR-CCM+ are developed. Furthermore, the results from the numerical methods are validated against results from physical tests performed at Scania’s test facility in Södertälje. The numerical methods are divided into a simplified model of a Battery Electric Vehicle (BEV) and a detailed geometry of a gas-driven vehicle from Scania. Beside dividing the wading scenario into the geometries, two different methods are developed, Wave and Wading. The Wave-method includes the vehicle standing still while a water wave is fed in through the inlet of the domain, i.e. allowed to flush over the vehicle, with a velocity of 3.6 km/h and 8 km/h. This method is implemented for both a generic simplified BEV truck and a detailed real-life Scania truck. For the Wading-method, motion is applied to the vehicle where itis driving with a velocity of 3.6 km/h through a digital twin of the water trench available at the test facility. This method is further divided into two cases, Zero Gap and Floating, where the difference is the distance between the tires of the vehicle and ground of the domain. The Floating-case includes a 10 cm distance and the Zero Gap-case has no gap between the tires and ground. The Wading-method is only implemented for the simplified geometry due to the computational cost and complexity. All methods use the Volume of Fluid (VOF) method for multiphase modelling and the Zero Gap-case uses Overset Mesh for modelling motion. The validation of the simulations focuses on the water behaviour such as water surface topology and water flowing inside the vehicle while wading. The results for the Wave-method with both the simplified and detailed truck at 8 km/h shows similarities in the water surface topology between the numerical model and the physical test. The simulations of the Wading-method is not visualising any similarities since the visible wave pattern are few and unclear in the numerical model. An isosurface is used to visualise the surface of the water which generated a smooth topology since no other options, such as vector fields, are added. It is found that the water movement inside the vehicle will affect water sensitive areas, e.g. on the battery packs. It is concluded that the derived methods are a first draft and should be directed towards future development in optimising the methods to lower the computational cost, but also to improve the capturing of the interface between the two phases. Due to instability and computational cost the detailed geometry is not implemented in the Wading-method. The methods are adapted to use different vehicle types since the simplified and detailed geometry are a BEV and a gas-driven truck respectively.

Road transport is responsible for a significant share of the global GHG emissions. In order to address the increasing trend of road vehicle emissions, due to its heavy reliance on oil, Nordic countries have set ambitious goals and policies for the reduction of road transport GHG emissions. Despite the fact that the latest developments in the passenger car segment are leading towards the progressive electrification of the fleet, the decarbonization of heavy-duty vehicle segment presents significant challenges that are yet to be overcome. This study focuses, on the first part, on the regulatory framework of fuel economy standards of road vehicles, highlighting the absence of a European regulation on fuel efficiency for the heavy-duty sector. Energy efficiency technologies can be grouped mainly in vehicle technologies, driveline and powertrain technologies, and alternative fuels. The fuel efficiency of HDVs can be positively improved at different vehicle levels, but the technology benefit and its economic feasibility are heavily dependent on the vehicle type and the operational cycle considered. The electrification pathway has the potential of reducing the carbon emission to a great extent, but the current battery technologies have proven to be not cost efficient for the heavy vehicles, because of the high purchase price and the low range, related to the battery cost and inferior energy density compared to conventional liquid fuels.   A scenario development model has been created in order to estimate and quantify the impact of future developments and emission reduction measures in Finland, Sweden and Norway for the timeframe 2016-2050, with a focus on 2030 results. Two scenarios concerning the powertrain developments of heavy-duty vehicles and buses have been created, a conservative scenario and electric scenario, as well as vehicle efficiency improvements and fuel consumption scenarios. Additional sets of parameters have been estimated as input for the model, such as national transport need and load assumptions. The results highlight the challenges of achieving the national GHG emission reduction targets with the current measures in all three countries. The slow fleet renewal rates and the high forecasted increase of transport need limit the benefits of alternative and more efficient powertrains introduced in the fleet by new vehicles. The heavy-duty transport is expected to maintain its heavy reliance on diesel fuel and hinder the improvements of the light-duty segments. A holistic approach is needed to reduce the GHG emissions from road transport, including more efficient powertrains, higher biofuel shares and progressive electrification.