Only about 38% of the UK network is currently electrified, however. The rate of progress threatens to delay arrival at 100%, with investment held back by high costs.
In the meantime, diesel trains continue to carry many thousands of passengers each day. At Northern, the second largest train operator in the UK, the annual fuel bill is about £50m – a prime target for cost reductions.
“With only 25% of our network electrified, diesel trains remain integral to our operation and, as such, we want to make sure we operate them in the most fuel-efficient way,” said Northern’s strategic development director Rob Warnes recently, as the company announced plans that it hopes could save thousands of litres of fuel a year.
The operator hopes to do so using a data model, developed in partnership with rail technology firm Chrome Angel Solutions and neuroinclusive data specialist Auticon, which calculates the optimal train speed and braking patterns for routes on its network. Output from the model can be used to coach drivers on how to approach routes, cutting fuel use while avoiding delays.
In an early test between Middlesbrough and Whitby in North Yorkshire, the model identified potential fuel savings of up to 7%, with an equal reduction in carbon dioxide (CO2) emissions. Now, Northern plans to test the system more widely – if successful, it hopes the model could provide major savings over its 2,500 daily services.
Getting fuel savings on track
The project started by “building up from first principles”, said Chrome Angel director Anisa Mamaniyat to Professional Engineering: “Looking at the theory of how trains operate, understanding all of the different variables that can impact driver behaviour – things like looking at the train parameters, so the weight of the train, the resistance of the train, looking at the network characteristics, and looking at line speeds, gradients, acceleration curves, those sorts of things.”
Public domain data was combined with drivers’ experience and expertise in the modelling tool, which the company aims to augment with operational data from trains in future, to improve accuracy.
Improved fuel efficiency is better for train engines in the long term, Mamaniyat said, further reducing costs.
The approach is particularly useful for legacy trains built from the 1980s to early 2000s, said Alistair Rutter, Northern head of operational science, to Professional Engineering. “When you look at modern rolling stock technology, everything's got a sensor on it. You can measure exactly how much fuel is used by an engine and so on,” he said.
“When you go back to our more legacy fleets, like the ones we're looking at here… we know how much fuel range it's got, because we know a 156 will do 1,600 miles.
“How it uses that fuel throughout that journey, we don't really know. So therefore, if we want to get to that fuel usage reduction, if we can understand how the fuel is being used, then we can look at that, how that delivers against the timetable.”
The test deployment between Middlesbrough and Whitby – selected because of the number of bends and speed restrictions over the roughly two-hour journey – used fuel pumps with fitted sensors to measure the flow of fuel throughout, which could then be fed back into the algorithm to make the engines as efficient as possible.
“If we understand how the train is using fuel to the second, to the metre hopefully, then you can understand and make a plot of the network,” said Rutter. “If we know we’ve got to get between A and B within a certain amount of time, actually, what is the most fuel efficient way of doing it?”
The model’s outputs are route-specific, so while the Whitby line had fuel savings of up to 7%, that figure will vary on other lines. “What we get in the real world is going to be slightly different, because you might find there's a headwind. If we've got 200 people on the train, or we've got no people on the train, the results will vary,” said Rutter.
Data-driven innovation
“Diesel trains will still be around for another couple of decades or so. And although there are other things that industry is doing, those other initiatives can be expensive,” said Mamaniyat.
“The rail industry is becoming more aligned with the digital world… so I think there is a much bigger appetite for data-driven innovations.”
While Northern hopes the model could enable swift reductions in diesel use, it could also help target energy efficiency as operators introduce more electric trains, as well as new technologies such as hydrogen fuel cells or batteries. Angel Trains – which owns more than a third of the UK’s rolling stock – was also involved in the project, raising the prospect of widescale deployment.
“In theory, if you can model other driving modes, like electric or battery-operated trains, then absolutely you can be better informed about the optimal driving style,” said Mamaniyat. “Ultimately, the savings are made by implementing those changes operationally, but what we can do is help provide a tool that can help facilitate those discussions and decisions.”
Northern recently announced plans to test the model on six routes in the North East: Bishop Auckland to Darlington, both in County Durham; Darlington to Saltburn in North Yorkshire; Newcastle to Hexham in Northumberland; Newcastle to Whitby; Nunthorpe to Kildale, both in North Yorkshire; and Seaham in County Durham to Middlesbrough.
The project has been funded by the Northern Innovation Fund, the Department for Transport and the Connected Places Catapult.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.