Generation of Equivalent Driving Cycles Using Markov Chains and Mean Tractive Force Components

Abstract In the automotive industry driving cycles have been used to evaluate vehicles in different perspectives. If a vehicle manufacturer focuses only on a fixed driving cycle there is a risk that controllers of the vehicle are optimized for a certain driving cycle and hence are sub-optimal solutions to real-world driving. To deal with this issue, it is beneficial to have a method for generating more driving cycles that in some sense are equivalent but not identical. The idea here is that these generated driving cycles have the same vehicle excitation in the mean tractive force, MTF. Using the individual force components of the MTF in the generation of driving cycles with Markov chains makes it possible to generate equivalent driving cycles that have the same vehicle excitation from real-world driving data. This is motivated since the fuel consumption estimation is more accurate when the MTF components are considered. The result is a new method that combines the generation of driving cycles using real-world driving cycles with the concept of equivalent driving cycles, and the results are promising.