Quantification of Variations in Real-World Drive Cycles for Hybrid Bus Optimisation

Many different control strategies have been developed to optimize a hybrid electric (HE) power train. Recently these have included the use of adaptive approaches. Such adaptive strategies attempt to alter the behavior of the HE power train based on the journey being undertaken.

Unlike the journeys undertaken in personal vehicles, passenger buses often traverse the same unchanged route several times a day. If such a journey could be suitably modeled the model may then form part of an optimized control strategy.

This paper describes the collection and analysis of a large number of bus journeys over a period of 10 months. Journey data was collected from a real route using a GPS receiver in conjunction with a handheld computer running bespoke logging software. A method has been developed to reduce each journey into a simple statistical representation based on power/energy use.

This statistical model can then be used to demonstrate the variability between journeys on the same route for different conditions. These variations may then be used to adapt an existing ‘normal’ drive cycle of the journey to optimize the HE power train for the current conditions. Additionally the statistical model may be iterated with new data collected on route.