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Abstract



Evaluation, Generation, and Transformation of Driving Cycles


Driving cycles are important components for evaluation and design of vehicles. They determine the focus of vehicle manufacturers, and indirectly they affect the environmental impact of vehicles since the vehicle control system is usually tuned to one or several driving cycles. Thus, the driving cycle affects the design of the vehicle since cost, fuel consumption, and emissions all depend on the driving cycle used for design. Since the existing standard driving cycles cannot keep up with the changing road infrastructure, the changing vehicle fleet composition, and the growing number of vehicles on the road, which do all cause changes in the driver behavior, the need to get new and representative driving cycles are increasing. A research question is how to generate these new driving cycles so that they are both representative and at the same time have certain equivalence properties, to make fair comparisons of the performance results. Besides generation, another possibility to get more driving cycles is to transform the existing ones into new, different, driving cycles considering equivalence constraints. With the development of new powertrain concepts the need for evaluation will increase, and an interesting question is how to utilize new developments in dynamometer technology together with new possibilities for connecting equipment. Here a pedal robot is developed to be used in a vehicle mounted in a chassis dynamometer and the setup is used for co-simulation together with a moving base simulator that is connected with a communication line. The results show that the co-simulation can become a realistic driving experience and a viable option for dangerous tests and a complement to tests on a dedicated track or on-road tests, if improvements on the braking and the vehicle feedback to the driver are implemented. The problem of generating representative driving cycles, with specified excitation at the wheels, is approached with a combined two-step method. A Markov chain approach is used to generate candidate driving cycles that are then transformed to equivalent driving cycles with respect to the mean tractive force components, which are the used measures. Using an optimization methodology the transformation of driving cycles is formulated as a nonlinear program with constraints and a cost function to minimize. The nonlinear program formulation can handle a wide range of constraints, e.g., the mean tractive force components, different power measures, or available energy for recuperation, and using the vehicle jerk as cost function gives good drivability. In conclusion, methods for driving cycle design have been proposed where new driving cycles can either be generated from databases, or given driving cycles can be transformed to fulfill certain equivalence constraints, approaching the important problem of similar but not the same. The combination of these approaches yields a stochastic and general method to generate driving cycles with equivalence properties that can be used at several instances during the product development process of vehicles. Thus, a powerful and effective engineering tool has been developed.

Peter Nyberg

2015

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