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.

The freight transport industry faces big challenges as the demand for transport and fuel prices are steadily increasing, whereas the environmental impact needs to be significantly reduced. Heavy-duty vehicle (HDV) platooning is a promising technology for a sustainable transportation system. By semi-autonomously governing each platooning vehicle at small inter-vehicle spacing, we can effectively reduce fuel consumption, emissions, and congestion, and relieve driver tension. Yet, it is not evident how to synthesise such a platoon control system and how constraints imposed by the road topography affect the safety or fuel-saving potential in practice. This thesis presents contributions to a framework for the design, implementation, and evaluation of HDV platooning. The focus lies mainly on establishing fuel-efficient platooning control and evaluating the fuel-saving potential in practice. A vehicle platoon model is developed together with a system architecture that divides the control problem into manageable subsystems. Presented results show that a significant fuel reduction potential exists for HDV platooning and it is favorable to operate the vehicles at a small inter-vehicle spacing. We address the problem of finding the minimum distance between HDVs in a platoon without compromising safety, by setting up the problem in a game theoretical framework. Thereby, we determine criteria for which collisions can be avoided in a worst-case scenario and establish the minimum safe distance to a vehicle ahead. A systematic design methodology for decentralized inter-vehicle distance control based on linear quadratic regulators is presented. It takes dynamic coupling and engine response delays into consideration, and the structure of the controller feedback matrix can be tailored to the locally available state information. The results show that a decentralized controller gives good tracking performance and attenuates disturbances downstream in the platoon for dynamic scenarios that commonly occur on highways. We also consider the problem of finding a fuel-efficient controller for HDV platooning based on road grade preview information under road and vehicle parameter uncertainties. We present two model predictive control policies and derive their fuel-saving potential. The thesis finally evaluates the fuel savings in practice. Experimental results show that a fuel reduction of 3.9–6.5 % can be obtained on average for a heterogenous platoon of HDVs on a Swedish highway. It is demonstrated how the savings depend on the vehicle position in the platoon, the behavior of the preceding vehicles, and the road topography. With the results obtained in this thesis, it is argued that a significant fuel reduction potential exists for HDV platooning. / <p>QC 20140527</p>

This article describes the first development phase of a centrifugal compressor with variable geometry which is designed to match the needs of future heavy-duty engines. Requirements of truck engines are analyzed, and their impact on the properties of the compressor map is evaluated in order to identify the most suitable kind of variable geometry. Our approach utilizes the transformation of engine data into pressure ratio and mass flow coordinates that can be displayed and interpreted using compressor maps. One-dimensional and three-dimensional computational fluid dynamics fluid flow calculations are used to identify loss mechanisms and constraints of fixed geometry compressors. Linking engine goals and aerodynamic objectives yields specific recommendations on the implementation of the variable geometry compressor.

A rear loading refuse truck was simulated with a conventional and hydraulic hybrid configuration. Models for the hydraulic hybrid components were developed to simulate the system. A control algorithm was developed using a stochastic dynamic programming approach. The results did not match those that are advertised by the commercially available systems, but reasons for this deviation are discussed. The predicted improvement in fuel economy ranged from 1% to 15% depending on variance in drive cycle and vehicle weight. A brief analysis of the cost of the hybrid system was also conducted based on an estimated drive cycle. This analysis showed that, at current fuel prices of about $4.00/gallon, the system may not make financial sense for a 10 year period of ownership. / text

A heavy-duty vehicle can benefit from the height control of the chassis that anair suspension provides. For example, to retain a pitch angle parallel to the road,regardless of what load it carries. For the purpose of developing a controller,a model of the air suspension provides evaluation and testing opportunities aswell as it gives the option for more advanced model based controller algorithms.Furthermore, a model can provide with an accurate axle weight estimation. Inthis thesis, both physical and statistical models are developed and parametersare estimated by solving minimization problems. They are then evaluated usingdata collected from a Scania truck, comparing normalized mean-root error valuesas well as residual analysis of each model.

<p>This thesis presents a new method for iterative road grade estimation based on sensors that are commonplace in modern heavy duty vehicles. Estimates from multiple passes of the same road segment are merged together to form a road grade map, that is improved each time the vehicle revisits an already traveled route. The estimation algorithm is discussed in detail together with its implementation and experimental evaluation on real vehicles.</p><p> </p><p>An increasing need for goods and passenger transportation drives continuing worldwide growth in road transportation while environmental concerns, traffic safety issues, and cost efficiency are becoming 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 benefit from preview road grade information. Using global navigation satellite systems an exact vehicle position can be obtained. This enables stored maps to be used as a source of preview road grade information. The task of creating such maps is addressed herein by the proposal of a method where the vehicle itself estimates the road grade each time it travels along a road and stores the information for later use.</p><p> </p><p>The presented road grade estimation method uses data from sensors that are standard equipment in heavy duty vehicles equipped with map-based advanced driver assistance systems. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver in a Kalman filter, to form a road grade estimate based on a system model. The noise covariance parameters of the filter are adjusted during gear shifts, braking and poor satellite coverage. The estimated error covariance of the road grade estimate is then used together with its absolute position to update a stored road grade map, which is based on all previous times the vehicle has passed the same location.</p><p> </p><p>Highway driving trials detailed in the thesis demonstrate that the proposed method is capable of accurately estimating the road grade based on few road traversals. The performance of the estimator under conditions such as braking, gear shifting, and loss of satellite coverage is presented. The experimental results indicate that road grade estimates from the proposed method are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort of heavy duty vehicles.</p>

Several modern marine engines use multiple injectors for lower heat losses and higher efficiency. However, the heavy-duty vehicles still apply a single injector per cylinder. This work investigates how multiple injectors can be operated in compression ignition heavy-duty engines along with potential benefits from such concepts. The studies aimed to avoid high boundary gas temperatures by having two injectors at the rim of the bowl, in addition to the standard injector. A longer injector-wall distance reduces the amount of hot gases at the boundaries for reduced convective heat losses. Additional degrees of freedom also follows from an increased number of injectors to simplify combustion control. The thesis included CFD simulations, metal engine experiments, and optical engine diagnostics to investigate the efficiency –and emission benefits for two –and three-injector concepts compared to the single-injector approach. The CFD simulations aimed to set beneficial spray angles and chamber geometries for reduced heat losses and reasonable emission levels with and without swirl at different load conditions. A flat bowl with two injectors reduced the heat losses by 4.2 %-points resulting in a direct efficiency increase of 1.9 %-points at middle-load conditions. Metal engine studies confirmed the simulation results by testing two -and three-injector concepts. The higher three-injector flow rate raised efficiency and diminished heat losses while providing low nitric oxide levels. Thus, three injectors lessen the typical trade-off between efficiency and nitric oxides. The thesis further performed single-injector optical engine experiments to investigate combustion control limitations. The results concluded that high soot levels occur from the multiple injections used to achieve isobaric combustion. These high soot levels followed by injecting into fuel-rich zones, which can be avoided by using multiple injectors. Finally, the thesis provides a multiple injector design suitable for heavy-duty production engines.

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.

Long haulage of heavy-duty trucks weighing over 15-ton stands for nearly 50% of the fuelconsumption among trucks, making them the most fuel consuming category. This indicatesthe potential benefits in improving the fuel efficiency for said category. Hybridization is onepossible solution.Hybrid vehicles are vehicles with two or more power sources in the powertrain. Differentpowertrain configurations, hybridization levels and hybrid concepts are best suitedfor different applications. With prices for fossil fuels constantly rising hybridization is animportant technology to improve fuel efficiency.Different variations of configurations and concepts enables many choices when decidingon a hybrid driveline. A simulation tool for efficiently comparing various hybrid drivelineswould be a great asset when deciding on a configuration for a certain vehicle application. Forthis thesis the application in focus is the previously mentioned category, a heavy duty longhauler weighing 36-ton.The modeling approach used for the simulation tool is called quasistatic modeling or"backward modeling". This name comes from, based on a chosen drive cycle, the resistingforces which act on the vehicle can statically be calculated at each step from the velocityprofile. The required power to drive along the drive cycle can then be calculated backwardswithin the powertrain resulting in a fuel consumption for the combustion engine. For thisthe free QSS-toolbox for Matlab Simulink has been used as a base and modified when needed.The configuration chosen to be implemented is a parallel electric hybrid and was chosenfor its good characteristics for the type of driving highways provide. For this configurationtwo types of controllers are used, one being an Equivalent Consumption Minimization Strategycontroller and the other a simple, rule based heuristic controller.The results for both controllers show small benefits with hybridization of the longhauler compared with the conventional which in the long run would make bigger differencebecause of the large consumption in whole. A sensitivity analysis was also done showingthat improving conventional vehicle parameters can be as beneficial as hybridization.

<p>Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor.</p><p>A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter.</p><p>The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful.</p><p>The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity.</p><p>All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.</p>