Evaluation, Transformation, and Extraction of Driving Cycles and Vehicle Operations

Open Access

Abstract

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.