A vehicle has several rotating components such as a traction electric motor, the driveline, and the wheels and tires. The rotating inertia of these components is important in vehicle performance analyses. However, in many studies, the rotating inertias are typically lumped into an equivalent inertial mass to simplify the analysis, making it difficult to investigate the effect of those components and losses for vehicle energy use. In this study, a backward-tracking model from the wheels and tires to the power source (battery or fuel cell) is developed to estimate the effect of rotating inertias for each component during propulsion and regenerative braking of a vehicle. This paper presents the effect of rotating inertias on the power and energy for propulsion and regenerative braking for two-wheel drive (either front or rear) and all-wheel drive (AWD) cases. On-road driving and dynamometer tests are different since only one axle (two wheels) is rotating in the latter case, instead of two axles (four wheels). The differences between an on-road test and a dynamometer test are estimated using the developed model. The results show that the rotating inertias can contribute a significant fraction (8 -13 %) of the energy recovered during deceleration due to the relatively lower losses of rotating components compared to vehicle inertia, where a large fraction is dissipated in friction braking. In a dynamometer test, the amount of energy captured from available energy in wheel/tire assemblies is slightly less than that of the AWD case in on-road test. The total regenerative brake energy capture is significantly higher (> 70 %) for a FWD vehicle on a dynamometer compared to an on-road case. The rest of inertial energy is lost by inefficiencies in components, regenerative brake fraction, and friction braking on the un-driven axle. / Master of Science

A driving cycle is a velocity profile over time. Driving cycles can be used for environmental classification of cars and to evaluate vehicle performance. The benefit by using stochastic driving cycles instead of predefined driving cycles, i.e. the New European Driving Cycle, is for instance that the risk of cycle beating is reduced. Different methods to generate stochastic driving cycles based on real-world data have been used around the world, but the representativeness of the generated driving cycles has been difficult to ensure. The possibility to generate stochastic driving cycles that captures specific features from a set of real-world driving cycles is studied. Data from more than 500 real-world trips has been processed and categorized. The driving cycles are merged into several transition probability matrices (TPMs), where each element corresponds to a specific state defined by its velocity and acceleration. The TPMs are used with Markov chain theory to generate stochastic driving cycles. The driving cycles are validated using percentile limits on a set of characteristic variables, that are obtained from statistical analysis of real-world driving cycles. The distribution of the generated driving cycles is investigated and compared to real-world driving cycles distribution. The generated driving cycles proves to represent the original set of real-world driving cycles in terms of key variables determined through statistical analysis. Four different methods are used to determine which statistical variables that describes the features of the provided driving cycles. Two of the methods uses regression analysis. Hierarchical clustering of statistical variables is proposed as a third alternative, and the last method combines the cluster analysis with the regression analysis. The entire process is automated and a graphical user interface is developed in Matlab to facilitate the use of the software. / En körcykel är en beskriving av hur hastigheten för ett fordon ändras under en körning. Körcykler används bland annat till att miljöklassa bilar och för att utvärdera fordonsprestanda. Olika metoder för att generera stokastiska körcykler baserade på verklig data har använts runt om i världen, men det har varit svårt att efterlikna naturliga körcykler. Möjligheten att generera stokastiska körcykler som representerar en uppsättning naturliga körcykler studeras. Data från över 500 körcykler bearbetas och kategoriseras. Dessa används för att skapa överergångsmatriser där varje element motsvarar ett visst tillstånd, med hastighet och acceleration som tillståndsvariabler. Matrisen tillsammans med teorin om Markovkedjor används för att generera stokastiska körcykler. De genererade körcyklerna valideras med hjälp percentilgränser för ett antal karaktäristiska variabler som beräknats för de naturliga körcyklerna. Hastighets- och accelerationsfördelningen hos de genererade körcyklerna studeras och jämförs med de naturliga körcyklerna för att säkerställa att de är representativa. Statistiska egenskaper jämfördes och de genererade körcyklerna visade sig likna den ursprungliga uppsättningen körcykler. Fyra olika metoder används för att bestämma vilka statistiska variabler som beskriver de naturliga körcyklerna. Två av metoderna använder regressionsanalys. Hierarkisk klustring av statistiska variabler föreslås som ett tredje alternativ. Den sista metoden kombinerar klusteranalysen med regressionsanalysen. Hela processen är automatiserad och ett grafiskt användargränssnitt har utvecklats i Matlab för att underlätta användningen av programmet.

Due to the goals made by the European Union as well as the country of Sweden regarding the desired decrease of the ongoing greenhouse gas emissions, electrical alternatives have increased enormously in the industry and the automotive areas in recent years. By going from petrol-powered vehicles to electrical vehicles, the transport sector has the potential to produce zero direct emissions. To be able to develop electrical vehicles with the highest efficiency possible, it is of great importance to understand what losses occurs in the vehicle. By lowering these losses, we create a vehicle which both become cheaper and better for the climate. The aim of his thesis is to study the performance of the motors with different combinations of angular velocity and torque, analyse what losses occur with the combinations and also how the result is affected by the resolution of the drive cycles. Produced drive cycles, with a purpose to represent the velocity and acceleration of a vehicle in a realistic way, was used during the study to obtain a drive pattern. MATLAB was used during the whole thesis for analysis, simulation and plotting. Firstly, a method to obtain the probability of certain combinations of torques and angular velocities for a specific drive cycle was created. With this probability, a method to be able to adjust and choose the resolution of the torques and angular velocities was created. It was later concluded that these two methods functioned as desired. With the obtained combinations, the mechanical power as well as the most common losses in the electrical motor could be calculated. With this, an efficiency could be calculated and analysed. The energy demand of the different combinations was also obtained. One conclusion that could be made with this thesis was that the average efficiency did not depend on the resolution of the torque and angular velocity much at all. With a resolution going from 0.5 Nm to 20 Nm, the average efficiency only changed by below 0.3 percent units. At the end of the thesis, a continued work is proposed to implement different magnetic properties that are not accounted for in this thesis. Another suggestion made is to include scenarios where the battery is charged when a negative torque is applied to the motor shaft.

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Volvo Construction Equipment is one of the leading construction machinery manufacturers in the world. To stay amongst the leaders, research and development projects for new technologies are crucial. The most important path of development today is the reduction of emissions produced by these heavy duty vehicles. To tackle this challenge, several technologies are already used in industry. One example are hybrid machines that combine a conventional diesel engine with batteries, resulting in reduced engine size and pollutants. Another option are full battery-electric vehicles, which can reduce the on-site emissions to zero. The electrochemical processes within batteries are however comparable slow and result in long recharge times. A new focus of development within the industry are hybrid systems combining fuel cells and batteries. Since hydrogen can be refueled almost as fast as convenient fuel, it solves the issue of long recharge times. Additionally, the reaction is emission free, since there is no combustion process and the only byproduct that is emitted from the fuel cell is chemically clean water. This thesis aims to propose an architecture and packaging solution to replace the diesel engine in a large size wheel loader with a fuel cell power system. This also includes all respective auxiliary systems, i.e. energy storage, cooling and electric systems. Achieving the same performance as a conventional large size wheel loader as well as keeping the spatial envelope the same are the main objectives of this work. To achieve these goals, an extensive study on the most common drive cycles is carried out to understand the power demand of the machine. After the selection of an energy storage system based on a MATLAB simulation script, a cooling system is modelled and scaled to fulfill the operating requirements of the different components. Eventually, all systems are modeled and installed into the wheel loader in CATIA V5. The study showed, that the new system architecture of the vehicle can be fitted into the existing engine bay with a slight extension of the rear frame and hood. Two power optimized batteries are combined with one fuel cell. Hydrogen tanks with a filling volume of 478 [L] can be installed in the machine, covering 50% of the customer population curve without degradation of performance. This includes one refill of the wheel loader during the day. The performance parameters match the conventional machine up to a high degree, concluding that the conversion of a large size wheel loader into a fuel cell powered wheel loader is feasible.

With increasing costs of fossil fuels and intensified environmental awareness, low carbon vehicles, including hybrid electric vehicles (HEVs), are becoming more popular for car buyers due to their lower running costs. HEVs are sensitive to the driving conditions under which they are used however, and real-world driving can be very different to the legislative test cycles. On the road there are higher speeds, faster accelerations and more changes in speed, plus additional factors that are not taken into account in laboratory tests, all leading to poorer fuel economy. Future trends in the automotive industry are predicted to include a large focus on increased hybridisation of passenger cars in the coming years, so this is an important current research area. The aims of this project were to determine the energy consumption of a HEV in real-world driving, and investigate the differences in this compared to other standard drive cycles, and also compared to testing in laboratory conditions. A second generation Toyota Prius equipped with a GPS (Global Positioning System) data logging system collected driving data while in use by Loughborough University Security over a period of 9 months. The journey data was used for the development of a drive cycle, the Loughborough University Urban Drive Cycle 2 (LUUDC2), representing urban driving around the university campus and local town roads. It will also have a likeness to other similar driving routines. Vehicle testing was carried out on a chassis dynamometer on the real-world LUUDC2 and other existing drive cycles for comparison, including ECE-15, UDDS (Urban Dynamometer Driving Schedule) and Artemis Urban. Comparisons were made between real-world driving test results and chassis dynamometer real-world cycle test results. Comparison was also made with a pure electric vehicle (EV) that was tested in a similar way. To verify the test results and investigate the energy consumption inside the system, a Prius model in Autonomie vehicle simulation software was used. There were two main areas of results outcomes; the first of which was higher fuel consumption on the LUUDC2 compared to other cycles due to cycle effects, with the former having greater accelerations and a more transient speed profile. In a drive cycle acceleration effect study, for the cycle with 80% higher average acceleration than the other the difference in fuel consumption was about 32%, of which around half of this was discovered to be as a result of an increased average acceleration and deceleration rate. Compared to the standard ECE-15 urban drive cycle, fuel consumption was 20% higher on the LUUDC2. The second main area of outcomes is the factors that give greater energy consumption in real-world driving compared to in a laboratory and in simulations being determined and quantified. There was found to be a significant difference in fuel consumption for the HEV of over a third between on-road real-world driving and chassis dynamometer testing on the developed real-world cycle. Contributors to the difference were identified and explored further to quantify their impact. Firstly, validation of the drive cycle accuracy by statistical comparison to the original dataset using acceleration magnitude distributions highlighted that the cycle could be better matched. Chassis dynamometer testing of a new refined cycle showed that this had a significant impact, contributing approximately 16% of the difference to the real-world driving, bringing this gap down to 21%. This showed how important accurate cycle production from the data set is to give a representative and meaningful output. Road gradient was investigated as a possible contributor to the difference. The Prius was driven on repeated circuits of the campus to produce a simplified real-world driving cycle that could be directly linked with the corresponding gradients, which were obtained by surveying the land. This cycle was run on the chassis dynamometer and Autonomie was also used to simulate driving this cycle with and without its gradients. This study showed that gradient had a negligible contribution to fuel consumption of the HEV in the case of a circular route where returning to the start point. A main factor in the difference to real-world driving was found to be the use of climate control auxiliaries with associated ambient temperature. Investigation found this element is estimated to contribute over 15% to the difference in real-world fuel consumption, by running the heater in low temperatures and the air conditioning in high temperatures. This leaves a 6% remainder made up of a collection of other small real-world factors. Equivalent tests carried out in simulations to those carried out on the chassis dynamometer gave 20% lower fuel consumption. This is accounted for by degradation of the test vehicle at approximately 7%, and the other part by inaccuracy of the simulation model. Laboratory testing of the high voltage battery pack found it constituted around 2% of the vehicle degradation factor, plus an additional 5% due to imbalance of the battery cell voltages, on top of the 7% stated above. From this investigation it can be concluded that the driving cycle and environment have a substantial impact of the energy use of a HEV. Therefore they could be better designed by incorporating real-world driving into the development process, for example by basing control strategies on real-world drive cycles. Vehicles would also benefit from being developed for use in a particular application to improve their fuel consumption. Alternatively, factors for each of the contributing elements of real-world driving could be included in published fuel economy figures to give prospective users more representative values.

When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.

BEVs are a critical pathway towards achieving energy independence and meeting greenhouse and pollutant gas reduction goals in the current and future transportation sector [1]. Automotive manufacturers are increasingly investing in the refinement of electric vehicles as they are becoming an increasingly popular response to the global need for reduced transportation emissions. Therefore, there is a desire to extract the most fuel economy from a vehicle as possible. Some areas that manufacturers spend much effort on include minimizing the vehicle’s mass, body drag coefficient, and drag within the powertrain. When these values are defined or unchangeable, interest is driven to other areas such as investigating the control strategy of the powertrain. If two or more electric motors are present in an electric vehicle, Torque Vectoring (TV) strategies are an option to further increase the fuel economy of electric vehicles. Most of the torque vectoring strategies in literature focus exclusively on enhancing the vehicle stability and dynamics with few approaches that consider efficiency or energy consumption. The limited research on TV that addresses system efficiency have been done on a small number of vehicle architectures, such as four independent motors, and are distributing torque front/rear instead of left/right which would not induce any yaw moment. The proposed research aims to address these deficiencies in the current literature. First, by implementing an efficiency-optimized TV strategy for a rear-wheel drive, dual-motor vehicle under straight-line driving as would be experienced in during the EPA drive cycle tests. Second, by characterizing the yaw moment and implementing strategies to mitigate any undesired yaw motion. The application of the proposed research directly impacts dual-motor architectures in a way that improves overall efficiency which also drives an increase in fuel economy. Increased fuel economy increases the range of electric vehicles and reduces the energy demand from an electrical source that may be of non-renewable origin such as coal.

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