Starting this thesis with the intention to inspire and to be inspired, I have tried to zoom out and look on designing a car from a new perspective. By telling a holistic design story rather than solving a specific problem I want people to imagine a future where one can live a modern and connected life in rural communities, all made possible after a green energy revolution. Design research, brand analysis, sketching, form verification using clay and digital modeling and advanced visualization were the main activities performed during the project. They helped me to explore, understand and successfully propose a complete story of vehicle and context. The result is BMW iMPULSE, a shared and fully autonomous vehicle that is wirelessly powered by clean energy and is always ready to support the spontaneous lifestyle

The city landscape of China is undoubtedly experiencing rapid transformation. With the fast rate of urbanization it seems plausible that people will live in a vertical city served by new kinds of transportation infrastructure around the year 2050. This project studied the values of the upper middle class of migrants living in a future Shanghai. Ideation and form development resulted in an aesthetic sculpture informed by the western brand and the eastern culture. This sculpture then inspired the development of a vehicle concept that can act as mobility solution as well as an icon of quality living in the future Chinese home.

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The goal was to extract knowledge from data that is logged by the electronic system of every Volvo truck. This allowed the evaluation of large populations of trucks without requiring additional measuring devices and facilities. An evaluation cycle, similar to the knowledge discovery from databases model, was developed and applied to extract knowledge from data. The focus was on extracting information in the logged data that is related to the class labels of different populations, but also supported knowledge extraction inherent from the given classes. The methods used come from the field of unsupervised learning, a sub-field of machine learning and include the methods self-organizing maps, multi-dimensional scaling and fuzzy c-means clustering. The developed evaluation cycle was exemplied by the evaluation of three data-sets. Two data-sets were arranged from populations of trucks differing by their operating environment regarding road condition or gross combination weight. The results showed that there is relevant information in the logged data that describes these differences in the operating environment. A third data-set consisted of populations with different engine configurations, causing the two groups of trucks being unequally powerful. Using the knowledge extracted in this task, engines that were sold in one of the two configurations and were modified later, could be detected. Information in the logged data that describes the vehicle's operating environment, allows to detect trucks that are operated differently of their intended use. Initial experiments to find such vehicles were conducted and recommendations for an automated application were given.

Nowadays traffic congestion is a big problem all over the world. To solve this problem governments build broader roads, establish more reasonable traffic rules. Dynamic routing is a good and efficient way to reduce traffic congestion. That is why research of real time vehicle routing optimization algorithms was made. Experiment showed that best performance of shortest path algorithm was Dijkstra algorithm. Based on that, a software prototype was developed – optimized route search system. Driver can select shortest or fastest route to his destination. There was used roads length to evaluate shortest path and special coefficient to evaluate fastest route. This coefficient is calculated respectively to road load, length, speed limit, capacity. Performance of developed system is good so I conclude that the system is capable of routing vehicles in real time in complex traffic network.

The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard. Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented. With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform. To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior. A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety. Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition.

One area of vehicle handling performance that has been the focus of an OEM{'}s (Original Equipment Manufacturer) engineering effort is within the realm of vehicle straight-line performance. As the name implies, straight-line performance is determinant on the vehicle{'}s tendency to resist vehicle lateral drift when being driven straight. Vehicle lateral drift is a condition where the driver must apply a constant correctional torque to the steering wheel in order to maintain a straight line course. A full vehicle model was developed to simulate the influences of suspension parameters on vehicle drift. Adams 2010 was chosen as the multi-body dynamics (MBD) software for this research for its ability to develop a full vehicle high fidelity model without the need for physical test data. The model was created from standard Adams/Car suspension templates modified to accommodate the subject vehicle. The front suspension sub-assembly model was built upon the front MacPherson strut suspension template. Likewise, the rear suspension sub-assembly model was created from the rear multi-link suspension template. The tire model used in the full vehicle model was based on the Pacejka 2002 formulation. A model of a similar tire was generated using a custom spreadsheet based on the PAC2002, a slightly modified version of the Pacejka 2002 formulation found within Adams/Car. A virtual tire test rig and a 6/7-DoF model were created to understand and verify the behaviour of the generated tire models. The virtual tire test rig was used to compare the outputs of the PAC2002 tire model to the calculated values from a custom tire property spreadsheet. The 6/7-DoF model was used to test and verify the effect of the tire{’}s residual lateral forces. The full-vehicle model was verified using the parallel wheel travel and opposite wheel travel suspension analyses. The parallel wheel travel analysis was used to tease out binding issues within the designed travel of the suspension. The opposite wheel travel analysis was used similarly for anti-roll bar systems. Simulations based on the industry standard vehicle drift tests were run to understand the effect of certain vehicle suspension geometry on vehicle drift, namely the vehicle{’}s front and rear camber and toe angles. The full-vehicle model was also subjected to straight-line performance simulations with various road bank or crown angles. The results were compared with industry-standard vehicle drift test data gathered by the OEM on their own test track. The results indicate that the direction of vehicle pull matches with the OEM test data, but the magnitudes differ in both the positively and negatively banked road simulation results. It is likely that the difference in vehicle drift is due to the lack of steering data obtained for the full-vehicle model.

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Electric Vehicles (EVs) have gained acceptance as low or zero emission means of transportation. This thesis deals with the design of a battery cell testing platform and Lithium-Ion (Li-Ion) battery modeling for EVs. A novel regenerative cell testing platform is developed for cell cycling applications. A 300 W - 5 V cell cycler consisting of a buck and a boost converter is designed. Furthermore, a novel battery modeling approach is proposed to accurately predict the battery performance by dynamically updating the model parameters based on the battery temperature and State of Charge (SOC). The comparison between the experimental and the model simulation results of an automotive cell under real-world drive-cycle illustrates 96.5% accuracy of the model. In addition, the model can be utilized to assess the long-term impact of battery impedance on performance of EVs under real-world drive-cycles.