In the European Union the legislation governing fleet CO2 emissions is already in place with a fleet average limit of 130g/km currently being imposed on all vehicle manufacturers. With the target for this legislation falling to 95g/km by 2020 and hefty fines for noncompliance automotive engineers are working a pace to develop new technologies that lower the CO2 emissions and hence fuel consumption of new to market vehicles. As average new vehicle CO2 emissions continue to decline the task of measuring these emissions with high precision becomes increasingly challenging. With the introduction of real world emissions legislation planned for 2017 there is a development driven need to precisely assess the vehicle CO2 emissions on chassis dynamometers over a wide operating range. Furthermore since all type approval and certification testing is completed on chassis dynamometers, any new technology must be proven against these test techniques. Typical technology improvements nowadays require repeatability limits which were unprecedented 5-10 years ago and the challenge now is how to deliver this level of precision. Detailed studies are conducted into the four key areas that cause significant noise to the CO2 emissions results from chassis dynamometer tests. These are the vehicle electrical system, driver behaviour, procedural factors and the chassis dynamometer itself. In each of these areas, the existing contribution of imprecision is quantified, methods are proposed then demonstrated for improving the precision and the improved case is quantified. It was found that the electrical system can be controlled by charging the vehicle battery, not using auxiliary devices and installing current measurement devices on the vehicle. Simply charging the vehicle battery prior to each test was found to cause a change to the CO2 emissions of 2.2% at 95% confidence. Whilst auxiliary devices were found to cause changes to the CO2 emissions of up to 43% for even a relatively basic vehicle. The driver behaviour can be controlled by firstly removing the tolerances from the driver’s aid which it was found improved the precision of the CO2 emissions by 43.5% and secondly by recording the throttle pedal movements to enable the validation of test results. Procedural factors, such as tyre pressures can be easily controlled by resisting the temptation to over check and by installing pressure sensing equipment. Using a modern chassis dynamometer with low parasitic losses will make the job of controlling the dynamometer easier, but all dynamometers can be controlled by following the industry standard quality assurance procedures and implementing statistical process control tools to check the key results. The implementation of statistical process control alone improved the precision of unloaded dynamometer coastdown checks by reducing the coefficient of variation from 6.6 to 4.0%. Using the dynamometer to accelerate the vehicle before coastdown checks was found to approximately halve the variability in coastdown times. It was also demonstrated that verification of the dynamometer inertia simulation and response time are both critically important, as the industry standard coastdown test is insufficient, in isolation, to validate the loading on a vehicle. Six sigma and statistical process control techniques have shown that for complex multiple input single output systems, such as chassis dynamometer fuel economy tests, it is insufficient to improve only one input to the system to achieve a change to the output. As a result, suggested improvements in each noise factor often have to be validated against an input metric rather than the output CO2 emissions. Despite this, the overall level of precision of the CO2 emissions and fuel consumption seen at the start of the research, measured by the coefficient of variation of approximately 2.6%, has been improved by over six times through the simultaneous implementation of the findings from this research with the demonstration of coefficient of variation as low as 0.4%. Through this research three major contributions have been made to the state of the art. Firstly, from the work on driver behaviour an extension is proposed to the Society of Automotive Engineers J2951 drive quality metric standard to include the a newly developed Cumulative Absolute Speed Error metric and to suggest that metrics are reviewed across the duration of a test to identify differences in driving behaviours during a test that do not cause a change to the end of test result. Secondly, the need to instrument the vehicle and test cell to record variability in the key noise factors has been demonstrated. Thirdly, a universal method has been developed and published from this research, to use response modelling techniques for the validation of test repeatability and the correction of CO2 emissions. The impact of these contributions is that the precision of chassis dynamometer emissions tests can be improved by a factor of 6.5 and this is of critical importance as the new real world driving and world light-duty harmonised emissions legislation comes into force over the next two to five years. This legislation will require an unprecedented level of precision for the effective testing of full vehicle system interactions over a larger operating range but within a controlled laboratory environment. If this level of precision is not met then opportunities to reduce vehicle fuel consumption through technology that only has a small improvement on fuel consumption, which is likely given the large advances that have be achieved over the last few decades, will be missed.

The transportation sector is the dominant source of U.S. fuel consumption and emissions. Specifically, highway travel accounts for nearly 75 percent of total transportation energy use and slightly more than 33 percent of national emissions of EPA's six Criteria pollutants. Enactment of the Clean Air Act Amendment of 1990 (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have changed the ways that most states and local governments deal with transportation problems. Transportation planning is geared to improve air quality as well as mobility. It is required that each transportation activity be analyzed in advance using the most recent mobile emission estimate model to ensure not to violate the Conformity Regulation. Several types of energy and emission models have been developed to capture the impact of a number of factors on vehicle fuel consumption and emissions. Specifically, the current state-of-practice in emission modeling (i.e. Mobile5 and EMFAC7) uses the average speed as a single explanatory variable. However, up to date there has not been a systematic attempt to quantify the impact of various travel and driver-related factors on vehicle fuel consumption and emissions. This thesis first systematically quantifies the impact of various travel-related and driver-related factors on vehicle fuel consumption and emissions. The analysis indicates that vehicle fuel consumption and emission rates increase considerably as the number of vehicle stops increases especially at high cruise speed. However, vehicle fuel consumption is more sensitive to the cruise speed level than to vehicle stops. The aggressiveness of a vehicle stop, which represents a vehicle's acceleration and deceleration level, does have an impact on vehicle fuel consumption and emissions. Specifically, the HC and CO emission rates are highly sensitive to the level of acceleration when compared to cruise speed in the range of 0 to 120 km/h. The impact of the deceleration level on all MOEs is relatively small. At high speeds the introduction of vehicle stops that involve extremely mild acceleration levels can actually reduce vehicle emission rates. Consequently, the thesis demonstrated that the use of average speed as a sole explanatory variable is inadequate for estimating vehicle fuel consumption and emissions, and the addition of speed variability as an explanatory variable results in better models. Second, the thesis identifies a number of critical variables as potential explanatory variables for estimating vehicle fuel consumption and emission rates. These explanatory variables include the average speed, the speed variance, the number of vehicle stops, the acceleration noise associated with positive acceleration and negative acceleration noise, the kinetic energy, and the power exerted. Statistical models are developed using these critical variables. The statistical models predict the vehicle fuel consumption rate and emission rates of HC, CO, and NOx (per unit of distance) within an accuracy of 88%-96% when compared to instantaneous microscopic models (Ahn and Rakha, 1999), and predict emission rates of HC, CO, and NOx within 95 percentile confidence limits of chassis dynamometer tests conducted by EPA. Comparing with the current state-of-practice, the proposed statistical models provide better estimates for vehicle fuel consumption and emissions because speed variances about the average speed along a trip are considered in these models. On the other hand, the statistical models only require several aggregate trip variables as input while generating reasonable estimates that are consistent with microscopic model estimates. Therefore, these models could be used with transportation planning models for conformity analysis. / Master of Science

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.

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