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Driving cycles play a fundamental role in the design of components, in the optimization of control strategies for drivetrain topologies, and in the identification of vehicle properties. The focus on a single or a few test cycles results in a risk of non-optimal or even poor design regarding the real usage profiles. Ideally, multiple different driving cycles that are representative of the real and scattering operating conditions are used. Therefore, tools for the stochastic generation of representative driving cycles are required, and many works have addressed this issue with different approaches. Until now, the stochastic generation of representative testing cycles has been limited to low dimensionality, and only a few works have studied higher dimensionality using Markov chain theory. However, it is mandatory to create tools that can stochastically generate multidimensional cycles incorporating all relevant operating conditions and maintaining signal dependency at the same time.

As the transmission design directly impacts drive unite operation and power flow to the driveline, the transmission power loss is a critical target in the drivetrain development. The demand of more precise and more efficient power loss prediction has therefore increased significantly, which highlights the need of new methodologies in order to optimize the power loss model for vehicle transmissions. The possible power losses that exist in the power flow path, are gear mesh losses, gear churning losses, gear windage losses, bearing losses, synchronizer losses and sealing losses. Thanks to the decades of research, analytical models are available for the prediction of these component losses, which could deliver power loss distributions and overall efficiency maps of complex transmissions. The aim of this paper is to introduce a methodology to improve the accuracy of a chosen power loss model on a system level.