The transport industry is in the middle of a conceptual shift driven by delivering the targets set by the Paris Agreement. Proactive heavy-duty vehicle companies seek to further gather knowledge in a structured way on environmental impacts of its products and services. The method to be implemented is Life Cycle Assessment (LCA). For implementation of LCA certain organisational and operational factors pre-requirements need to be addressed. The study takes key factors of Life Cycle Management (LCM) as a framework for assessing the readiness of Scania CV AB to implement LCA. Said key factors of LCM are analysed through company-based case study observations and literature review. The results indicate the company is in the process of introducing majority of the key factors of LCM. The case study tested the possibilities of the company for LCA, and attempted second phase of LCA, Life Cycle Inventory (LCI). The greatest challenge to LCA is low availability and format of data for LCA. However, the case study deeply tested the data limits and offers good insight in actions to be taken.

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.

In the field of heavy-duty vehicles, fuel efficiency and environmental protection are factors that need to be focused on, while the aerodynamic drag generated during vehicle travelling is one of the most influential aspects. This thesis delves into the aerodynamic simulation of Scania trucks using the open-source Computational Fluid Dynamics (CFD) tool, OpenFOAM v2206. This study rigorously investigates the aerodynamics of two Scania truck models under different operating conditions, including scenarios with different crosswind environments at high speeds.The core of this study is to compare and analyse the computational results of OpenFOAM v2206 and its predecessor OpenFOAM v3.0+ in a number of aspects, in order to elucidate the evolution and improvement of CFD techniques and their practical impact on vehicle simulation performance. In order to save computational resources, the RANS method was used for the steady-state simulations. Preliminary comparisons were also made with results from PowerFLOW, another CFD software widely used within the Scania group.Another important part of this thesis is the exploration of an alternative meshing method (ANSA Hextreme Mesh) in CFD simulations. As a widely used pre-processing software in the Scania group today, analysing and comparing the advantages and disadvantages of ANSA and OpenFOAM in terms of meshing, such as the ease of meshing and the accuracy of aerodynamic predictions, can help to provide valuable guidance for the application of truck shape design and aerodynamic simulation.The results indicate that OpenFOAM v2206 excels in predicting aerodynamics and has utility in optimising truck design. Compared to OpenFOAM v3.0+, OpenFOAM v2206 shows smaller discrepancies in results with PowerFLOW. Further exploration is required regarding transient simulations using OpenFOAM. In terms of meshing methods, a simplified model (Allan Body) was investigated, and there is further research to be done on meshing the complete truck.In conclusion, this thesis presents a comprehensive and in-depth exploration of truck aerodynamics using advanced CFD tools. The results not only deepen the understanding of airflow dynamics around heavy vehicles, but also pave the way for the development of more aerodynamically efficient and environmentally friendly truck designs.

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.  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.  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.  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. / QC 20101119

The construction industry is one of the largest emitters of greenhouse gases and health-related pollutants. Monitoring and benchmarking emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. This research focuses on real-time measurement of emissions from non-road construction equipment and development of a monitoring-benchmarking tool for comparison of expected vs. actual emissions. First, exhaust emissions were measured using a Portable Emission Measurement System (PEMS) during the operation of 18 pieces of construction equipment at actual job sites. Second-by-second emission rates and emission factors for carbon dioxide, carbon monoxide, nitrogen oxides, and hydrocarbons were calculated for all equipment. Results were compared to those of other commonly used emission estimation models. Significant differences in emission factors associated with different activities were not observed, except for idling and hauling. Moreover, emission rates were up to 200 times lower than the values estimated using EPA and California Air Resources Board (CARB) guidelines. Second, the resulting database of emissions was used in an automated, real-time environmental assessment system. Based on videos of actual construction activities, this system enabled real-time action recognition of construction operations. From the resulting time-series of activities, emissions were estimated for each piece of equipment and differed by only 2% from those estimated by manual action recognition. Third, the actual emissions were compared to estimated ones using discrete event simulation, a computational model of construction activities. Actual emissions were 28% to 144% of those estimated by manual action recognition. Results of this research will aid practitioners in implementing strategies to measure, monitor, benchmark, and possibly reduce air pollutant emissions stemming from construction. / Master of Science

<p dir="ltr">The automotive industry has incorporated controls since the 1970s, starting with the pioneering application of an air-to-fuel ratio feedback control carburetor. Over time, significant advancements have been made in control strategies to meet industry standards for reduced fuel consumption, exhaust emissions, and enhanced safety. This thesis focuses on the implementation of advanced control strategies in heavy-duty diesel powertrains and their advantages over traditional control methods commonly employed in the automotive industry.</p><p dir="ltr">The initial part of the thesis demonstrates the utilization of model predictive control (MPC) to generate an optimized velocity profile for class 8 trucks. These velocity profiles are designed to minimize fuel consumption along a given route with known grade conditions, while adhering to the time constraints comparable to those of standard commercial cruise controllers. This methodology is further expanded to include the platooning of two trucks, with the rear truck following a desired gap (variable or fixed), resulting in additional fuel savings throughout the designated route. Through collaborative efforts involving Cummins, Peloton Technology, and Purdue University, these control strategies were implemented and validated through simulation, hardware-in-the-loop testing, and ultimately, in demonstration vehicles.</p><p dir="ltr">MPC is highly effective for vehicle-level controls due to the accurate plant model used for optimization. However, when it comes to engine controls, the plant model becomes highly nonlinear and loses accuracy when linearized [20]. To address this issue, robust control techniques are introduced to account for the inherent inaccuracies in the plant model, which can be represented as uncertainties.</p><p dir="ltr">The second study showcases the application of robust controllers in diesel engine operations, focusing on a 4.5L John Deere diesel engine equipped with an electrified intake boosting system. The intake boosting system is selectively activated during transient operations to mitigate drops in the air-to-fuel ratio (AFR), which can result in smoke emissions. Initially, a two-degree-of-freedom robustsingle-input single-output (SISO) eBooster controller is synthesized to control the eBooster during load transients. Although the robust SISO controller yields improvements, the eBooster alone does not encompass all factors affecting the gas exchange process. Other actuators, such as the exhaust throttle and EGR valve, need to be considered to enhance the air handling system. To achieve this, a robust model-basedmultiple-input multiple-output (MIMO) controller is developed to regulate the desired AFR, engine speed, and diluent air ratio (DAR) using various air handling actuators and fueling strategies. The robust MIMO controller is synthesized based on a physics-based mean value engine model, which has been calibrated to accurately reflect high-fidelity engine simulation software. The robust SISO and MIMO controllers are implemented in simulation using the high-fidelity engine simulation software. Following the simulation, the controllers are validated through experimental testing conducted in an engine dynamometer at University of Wisconsin. Results from these controllers are compared against a non-eBoosted engine, which serves as the baseline. While both the SISO and MIMO controllers show improvements in AFR (Air-Fuel Ratio), DAR (Diluent Air Ratio), and engine speed recovery during the load transients, the robust MIMO controller outperforms them by demonstrating the best overall engine performance. This superiority is attributed to its comprehensive understanding of the coupling between each actuator input and the model output. When the MIMO controller operates alongside the electrified intake boosting system, the engine exhibits remarkable enhancements. Not only does it recover back to a steady state 70% faster than the baseline, but it also reduces engine speed droop by 45%. Consequently, the engine's ability to accept load torque increases significantly.</p><p dir="ltr">As a result, a single robust MIMO controller can efficiently perform the same task instead of employing multiple PIDs or look-up tables for each actuator.</p>

Automated gear-shifts are critical procedures for the driveline as they are demanded to work as fast and accurate as possible. The torque control of a driveline is especially important for the driver’s feeling of driveability. In the case of gear-shifts and torque control in general, the boost pressure is key to achieve good response and thereby a fast gear-shift. An experimental study is carried out to investigate the phenomena of boost pressure drop during gear-shift and gather data for the modelling work. Results confirm the stated fact on the influence of boost pressure drop on gear-shift completion time and also indicate a clear linear dependence between initial boost pressure and the following pressure drop. A dynamic predictive model of the engine is developed with focus on implementation in a heavy duty truck, considering limitations computational complexity and calibration need between truck configurations. The resulting approach is based on a mean value modelling scheme that uses engine control system parameters and functions when possible. To be able to be predictive, a model for demanded torque and engine speed during the gear-shift is developed as reference inputs to the simulation. The simulation is based on a filling and emptying process throughout the engine dynamics, and yields final values of several engine variables such as boost pressure. The model is validated and later evaluated in comparison to measurements gathered in test vehicle experiments and in terms of robustness to input and model deviations. Computer simulations yield estimations of the boost pressure drop within acceptable limits. Consid- ering estimations used prior to this thesis the performance is good. Input deviations and modelling inaccuracies are found to inflict significant but not devastating deviations to the model output, possibly more over time with ageing of hardware taken into account. Final implementation in a heavy duty truck ecu is carried out with results indicating that the current implementation of the module is relatively computationally heavy. At the time of ending the thesis it is not possible to analyse its performance further, and it is suggested that the module is optimized in terms of computational efficiency.

The ability to control engine valve timing has the potential to alter the engine performance over the entire operating range. The outcome of valve timing technology enables the possibility to increase efficiency, lowering emissions, increase engine torque, etc. One of the simplest ways to obtain a variable valve timing is to use cam phasers. The dynamics of a hydraulic cam phaser has been studied, three concepts with the purpose to control such an element has been developed using simulation driven product development. Focus have been on robustness, simplicity and implementation. A final concept using on/off solenoids to control a torque driven cam phaser has been designed and simulated in GT-SUITE which validated its performance and functionality. A dynamic model was built in Simulink which simulated the behaviour of the cam phaser and provided tools for optimizing the rotor design. By combining the knowledge of mechanical- and control engineering at Scania, the development process of such machine elements was effective. The outcome of this thesis has given a new perspective in understanding these components and their potentials.

Path following and lateral stability are crucial issues for autonomous vehicles. Moreover, these problems increase in complexity when handling heavy-duty vehicles due to their poor manoeuvrability, large sizes and mass variation. In addition, uncertainties on mass may have the potential to significantly decrease the performance of the system, even to the point of destabilising it. These parametric variations must be taken into account during the design of the controller. However, robust control techniques usually require offline adjustment of auxiliary tuning parameters, which is not practical and leads to sub-optimal operation. Hence, this work presents an approach to path-following and lateral control for autonomous heavy-duty vehicles subject to parametric uncertainties by using a robust recursive regulator. The main advantage of the proposed controller is that it does not depend on the offline adjustment of tuning parameters. Parametric uncertainties were assumed to be on the payload, and an H&#8734; controller was used for performance comparison in simulations. The performance of both controllers is evaluated in a double lane-change manoeuvre. Simulation results showed that the proposed method had better performance in terms of robustness, lateral stability, driving smoothness and safety, which demonstrates that it is a very promising control technique for practical applications. Ultimately, experiment tests in a rigid heavy-duty truck validate what was found in simulation results. / O seguimento de caminho e a estabilidade lateral são questões cruciais para veículos autônomos. Além disso, devido à baixa capacidade de manobra, tamanho e grande variação de massa, estes problemas se tornam mais complexos quando se trata de veículos pesados. Adicionalmente, as incertezas na massa têm o potencial de diminuir significativamente o desempenho do sistema, chegando ao ponto de desestabilizá-lo, assim, essas variações paramétricas devem ser consideradas durante o projeto do controlador. No entanto, as técnicas de controle robusto geralmente exigem o ajuste off-line de parâmetros auxiliares do controlador, o que não é prático e lava a uma operação sub-ótima. Assim, este trabalho apresenta uma abordagem de controle de seguimento de caminho e controle lateral para veículos pesados autônomos sujeitos a incertezas paramétricas usando um regulador robusto recursivo. A principal vantagem deste controlador é que ele não depende do ajuste off-line de parâmetros. Assumiu-se que as incertezas paramétricas estavam na carga do veículo, e um controlador H&#8734; foi usado para comparar o desempenho em simulação. O desempenho de ambos os controladores é avaliado em uma manobra de mudança de faixa. Os resultados de simulação mostraram que o método proposto apresentou melhor desempenho em termos de robustez, estabilidade lateral, suavidade na condução e segurança, o que o demonstra como uma técnica de controle bastante promissora para aplicações práticas. Por fim, testes experimentais em um caminhão rígido reforçam os resultados obtidos em simulação.

<div>Plug-in hybrid electric vehicles (PHEVs) are being considered as an alternative to conventional medium-duty (MD) and heavy-duty (HD) commercial vehicles to reduce fuel consumption and tailpipe emissions. Lithium ion batteries, which are used in PHEVs due to their high energy density, are expensive. The battery contributes significantly towards the life-cycle cost of MD/HD PHEVs, as these vehicles, due to high mass and aggressive battery usage, require multiple battery replacements over their lifetime. Smaller batteries increase the fuel consumption and need more replacements, while bigger batteries increase the initial system cost. Powertrain design from a life-cycle cost perspective is required to explore this trade-off and maximize the economic gains obtained from PHEVs. </div><div><br></div><div>Powertrain design entails component sizing, control strategy selection as well as architecture selection. Different powertrain designs yield different lifetime economic gains. A variety of applications exist for MD/HD vehicles, which differ in their ways of powertrain usage, due to variations in required acceleration, available braking, and average and maximum speeds. Therefore, different powertrain designs are needed depending on the application and usage scenario. The powertrain design space needs to be explored, and solutions that maximize the economic gains within the specified constraints need to be chosen.</div><div><br></div><div>This dissertation compares the economic viability of two PHEV applications (MD Truck and HD Transit Bus), with options of series and parallel hybrid architectures, over multiple drivecycles, for four economic scenarios (years 2015, 2020, 2025 and 2030). It is shown that hybridizing the transit bus achieves payback sooner than hybridizing the truck. Further, the results for the transit bus application, over the Manhattan drivecycle, show that implementation of the parallel architecture is economically viable in the 2015(present) scenario, while the series architecture becomes viable in 2020, due to significantly lower initial costs involved in the parallel architecture.</div><div><br></div><div>A methodology to select a solution out of the explored design space that maximizes the economic gains is demonstrated. Variations in the economic and vehicle usage conditions for which this solution is designed, can be expected. It is therefore necessary to check the robustness of this solution to change in external factors such as vehicle mass, annual vehicle miles travelled (AVMT), component and fuel costs. It is shown that the economic gains are affected by the battery cost, fuel cost, AVMT and vehicle mass, while the number of battery replacements are affected by AVMT and vehicle mass. </div><div><br></div><div>A probability-based approach is demonstrated to obtain confidence in the economic and battery life predictions. Specifically, probability-based variations are provided to variables such as miles traveled between recharge, recharge C-rate and battery temperature. It is shown that battery life is affected the most by battery temperature.</div><div><br></div><div>A battery heating/cooling system is required to maintain constant battery temperature of operation during all seasons, but these systems incur additional fuel costs. A framework that utilizes just the Coefficient of Performance (COP) of the heating/cooling system to calculate the excess fuel cost is proposed and demonstrated. An increase of 0.9-1.8\% in fuel consumption is shown, depending on the drivecycle and ambient temperature.</div><div><br></div><div>Further, the well-to-wheel (WTW) fuel-cycle emissions from conventional and PHEV transit buses operating in Indiana and California are assessed using the ``Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation'' (GREET) Model 2017, developed by Argonne National Labs. It is shown that 59% and 63% greenhouse gas (GHG) reductions can be achieved in Indiana and California respectively, along with reduction in carbon monoxide (CO), nitrogen oxides NOx, particulate matter with diameter less than 2.5 microns PM2.5 and volatile organic compounds (VOC) emissions for both the states. However, an increase in sulfur oxides SOx emissions for both the states, and particulate matter with diameter less than 10 microns PM10 increase for Indiana, are observed. </div><div><br></div>