Edward Chappell

The control of chassis dynamometer fuel consumption testing noise factors and the use of response modelling for validation of test repeatability

The research described in this paper was aimed at demonstrating the implementation of statistically derived tolerances to so-called ‘noise’ factors that cause imprecision in the vehicle fuel consumption during chassis dynamometer testing. These tolerances were derived from previous work carried out by one of the authors co-workers and were set to achieve a repeatability target of 0.5% coefficient of variation in the vehicle fuel consumption. This target was successfully achieved during a test programme to determine the fuel consumption benefit of two candidate engine oils over production engine oil using a 1.0 l gasoline passenger test vehicle. Regression response modelling was used to determine whether the recorded variability was correlated with the variability in the vehicle fuel consumption and it was found that all the measured test noise factors were adequately controlled. A universal methodology is proposed for the use of the response modelling technique to verify adequate control of known noise factors and to allow for corrections to the vehicle fuel consumption to be performed where factors have not been adequately controlled, without the need to complete additional testing.

Experimental analysis of the V-Charge variable drive supercharger system on a 1.0 L GTDI engine

A compound charging system that pairs a turbocharger with a supercharger seems to be a potential trend for future passenger car gasoline engines, as the strength of both could be enhanced and the deficiencies of each could be offset. The use of a fixed-ratio positive-displacement supercharger system on a downsized turbocharged gasoline engine has already appeared on the market. Although such systems can achieve enhanced low-end torque and improved transient response, several challenges still exist. An alternative solution to the fixed-ratio positive-displacement supercharger is the V-Charge variable ratio centrifugal supercharger. This technology utilizes a Torotrak continuously variable transmission (CVT) coupled to a centrifugal compressor for near silent boosting. With a wide ratio spread of 10:1 and rapid rate of ratio change, the compressor speed can be set independently of the engine speed to provide an exact boost pressure for the required operating points, without the need to recirculate the air through a bypass valve. A clutch and an active bypass valve can also be eliminated, due to the CVT capability to down-speed, thus improving the noise vibration and harshness performance. This paper will, for the first time, present and discuss the V-Charge technology optimization and experimental validation on a 1.0 L GTDI engine to achieve a better brake specific fuel consumption and transient response over the turbo-only and the fixed-ratio positive-displacement supercharger solution. The potential for the V-Charge system to increase the low-end torque and enable a down-speeding strategy is also discussed.

Driving Style Modelling with Adaptive Neuro-Fuzzy Inference System and Real Driving Data

With different cognitive abilities and driving style preferences, car-following behaviors can vary significantly among human drivers. To facilitate the replications of human driving behaviors on chassis dynamometer using a robot driver, this paper proposes a novel fuzzy logic driver model that attempts to perform humanized driving behaviors in the car-following regimes. An adaptive neuro-fuzzy inference system was developed to tune the fuzzy model using real driving data collected from an instrumented vehicle. Driver’s cognition parameters, such as headway distance, vehicle speed and pedal positions, were modelled as system inputs. Meanwhile, driver’s action parameters, such as pedal movements and gear selection, were selected as system outputs. Three models that possess different driving styles were calibrated using the system. Afterwards, in order to evaluate its performance of emulating human behaviors, the established fuzzy models were examined in a simulation scenario that is anchored to standard WLTC drive cycle tests.

Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter

The major contribution of this paper is to propose a low-cost accurate distance estimation approach. It can potentially be used in driver modelling, accident avoidance and autonomous driving. Based on MATLAB and Python, sensory data from a Continental radar and a monocular dashcam were fused using a Kalman filter. Both sensors were mounted on a Volkswagen Sharan, performing repeated driving on a same route. The established system consists of three components, radar data processing, camera data processing and data fusion using Kalman filter. For radar data processing, raw radar measurements were directly collected from a data logger and analyzed using a Python program. Valid data were extracted and time stamped for further use. Meanwhile, a Nextbase monocular dashcam was used to record corresponding traffic scenarios. In order to measure headway distance from these videos, object depicting the leading vehicle was first located in each frame. Afterwards, the corresponding vanishing point was also detected and used to automatically compute the camera posture, which is to minimize the interference caused by camera vibration. The headway distance can hence be obtained by assuming the leading and host vehicles were in the same ground plane. After both sensory data were obtained, they were synthesized and fused using Kalman filter, to generate a better estimation of headway distance. The performances of both sensors were assessed individually and the correlation between their measurements was evaluated by replotting radar measurements on the video stream. The results of individual sensors and Kalman filter were compared to investigate the optimization performance of the data fusion approach.This is a general guidance of headway distance estimation with a low cost radar and a monocular camera. With described general procedures, this paper can allow researchers to easily fuse radar and camera measurements to obtain optimized headway distance estimation. This paper can facilitate the development of a more realistic robotic driver that can mimic human driver behaviors.