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Electric coolant pumps for IC engines are under development by a number of suppliers. They offer packaging and flexibility benefits to vehicle manufacturers. Their full potential will not be realised, however, unless an integrated approach is taken to the entire cooling system. The paper describes such a system comprising an advanced electric pump with the necessary flow controls and a supervisory strategy running on an automotive microprocessor. The hardware and control strategy are described together with the simulation developed to allow its calibration and validation before fitting in a B/C class European passenger car. Simulation results are presented which show the system to be controllable and responsive to deliver optimum fuel consumption, emissions and driver comfort.

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.

Driver behaviour can strongly affect fuel consumption, and driver training in eco-driving techniques has been shown to reduce fuel consumption by 10% on average. However the effects of this training can be short-lived, so there is an apparent need for continuous monitoring of driver behaviour. This study presents a driver advisory tool which encourages eco-driving, and its evaluation in the field. The system, developed by Ashwoods Automotive Ltd (UK) and the University of Bath (UK), is aimed at fleet operators of light commercial vehicles, where the driver is typically a company employee. A significant strength of the system is that it has been designed for easy integration with the vehicle CAN-bus, reducing complexity and cost. By considering the Inertial Power Surrogate (speed times acceleration) the core algorithm is able to identify behaviour which is likely to increase fuel consumption.