Original Article Observations of train control performance on a camshaft-operated DC electrical multiple unit Robert Ellis 1 , Paul Weston 1 , Edward Stewart 1 , Stuart Hillmansen 1 , Pietro Tricoli 1 , Clive Roberts 1 and Ian Jones 2 Abstract In order to reduce energy consumption on DC railways where regenerative braking is not available, train control strategy or ‘driver style’ is a practical alternative. In 2011, instrumentation to monitor energy consumption on the Merseyrail network was fitted to a British Rail Class 508 DC camshaft-operated electrical multiple unit. In this paper, seven services from Hunts Cross to Southport are highlighted to demonstrate a number of driver styles and their correlation with energy consumption. The differences in energy consumption were observed to be related to driver aggression in both the acceleration and deceleration phases. Keywords Driver style, DC network, energy monitoring, performance analytics, railway Date received: 28 August 2014; accepted: 16 April 2015 Introduction Rail networks are highly efficient compared with other land-based transport systems. 1 Thus, the devel- opment of methods to improve the inherent energy efficiency of rail transport will allow it to compete with other land-based transport modes. 2 Broadly speaking, there are two ways to conserve energy during rail traction: capture the energy with a regen- erative braking system, which can then be dispersed among other rail vehicles or returned to the national electrical grid; or not use the energy in the first place. The latter approach can be achieved by using the trac- tion system in a more efficient manner. Much of the literature concerning driving efficiency is based on models, commonly this is used in the development of a driver advisory system (DAS). 3 Although the use of a DAS will undoubtedly become widespread due to potential energy savings of 15–25% 4,5 , there are problems with effective implementation. Mitchel 6 shows an example of the difference in energy con- sumption between driver trajectories and a simulated optimal trajectory. In this case, 90% of drivers could not follow the optimized trajectory, resulting in energy consumption increases of over 25% in most cases. There are a number of reasons that may be responsible for this difference: they range from the complexity of the trajectory to driver priorities. Examples in Yang et al. 4 describe optimized trajectory implementation on a mining route in northern Scandinavia. Two drivers, a ‘good’ one and a ‘bad’ one are compared as they attempt to follow a target trajectory. Although the ‘good’ driver achieved a sig- nificant reduction in energy consumption, the drivers were not able to closely follow the complex DAS tra- jectory due to the difficult requirements placed on them. The bad driver frequently exceeded the sug- gested speed limit and was forced to aggressively brake a number of times, resulting in an energy con- sumption increase of 1500 kWh. In Albrecht et al. 5 and Rahn et al. 7 a more simplified DAS approach is presented. In this case, an optimal trajectory is described by well-defined driver regimes: (i) full power; (ii) cruise and coast; and (iii) braking. The trajectory profile is relayed to the driver one regime at a time to reduce information processing and work- load. Providing a simple and non-distracting DAS has the potential to reduce energy consumption while maintaining driver competency and safety. Often, 1 The University of Birmingham, Birmingham, UK 2 Merseyrail, Liverpool, UK Corresponding author: Robert Ellis, BCRRE, University of Birmingham, Birmingham, B12 2SA, UK. Email: rxe297@bham.ac.uk Proc IMechE Part F: J Rail and Rapid Transit 0(0) 1–18 ! IMechE 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954409715589618 pif.sagepub.com at University of Birmingham on November 1, 2015 pif.sagepub.com Downloaded from