A driving cycle is a representation of how vehicles are driven and  is usually represented by a set of data points of vehicle speed  versus time.  Driving cycles have been used to evaluate vehicles for  a long time. A traditional usage of driving cycles have been in  certification test procedures where the exhaust gas emissions from  the vehicles need to comply with legislation. Driving cycles are now  also used in product development for example to size components or  to evaluate different technologies.  Driving cycles can be just a  repetition of measured data, be synthetically designed from  engineering standpoints, be a statistically equivalent  transformation of either of the two previous, or be obtained as an  inverse problem e.g. obtaining driving/operation patterns.  New  methods that generate driving cycles and extract typical behavior  from large amounts of operational data have recently been proposed.  Other methods can be used for comparison of driving cycles, or to  get realistic operations from measured data. 

This work addresses evaluation, transformation and extraction of  driving cycles and vehicle operations.  To be able to test a vehicle  in a controlled environment, a chassis dynamometer is an  option. When the vehicle is mounted, the chassis dynamometer  simulates the road forces that the vehicle would experience if it  would be driven on a real road. A moving base simulator is a  well-established technique to evaluate driver perception of e.g. the  powertrain in a vehicle, and by connecting these two simulators the  fidelity can be enhanced in the moving base simulator and at the  same time the mounted vehicle in the chassis dynamometer is  experiencing more realistic loads. This is due to the driver's  perception in the moving base simulator is close to reality. 

If only a driving cycle is considered in the optimization of a  controller there is a risk that the controllers of vehicles are  tailored to perform well in that specific driving cycle and not  during real-world driving. To avoid the sub-optimization issues, the  operating regions of the engine need to be excited differently. This  can be attained by using a novel algorithm, which is proposed in  this thesis, that alters the driving cycle while maintaining that  the driving cycle tests vehicles in a similar way. This is achieved  by keeping the mean tractive force constant during the process. 

From a manufacturers standpoint it is vital to understand how your  vehicles are being used by the customers. Knowledge about the usage  can be used for design of driving cycles, component sizing and  configuration, during the product development process, and in  control algorithms.  To get a clearer picture of the usage of wheel  loaders, a novel algorithm that automatically, using existing  sensors only, extracts information of the customers usage, is  suggested. The approach is found to be robust when evaluated on  measured data from wheel loaders loading gravel and shot rock.