Data-Driven Identification of Characteristic Real-Driving Cycles Based on k-Means Clustering and Mixed-Integer Optimization

Virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies. Since the vehicle-usage behavior has a strong impact on component loads, realistic computation results require accurate assumptions about these boundaries. In this context, driving cycles (DCs) are used to represent the system boundaries in vehicle operation. The aim of this article is to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios. Vehicle measurements are segmented and consumption-relevant features are extracted from each segment. These features are then used to apply clustering and classification techniques to identify characteristic groups that are consequently assigned to different driving environments and driving styles. In order to obtain even more realistic driving scenarios, a data-fusion approach is used to incorporate a road slope signal from a NASA digital elevation model for each segment. Lastly, a genetic mixed-integer optimization algorithm is proposed to efficiently generate representative DCs for each characteristic group of driving segments. The main contribution of this article is a data-driven identification of the parameter space of real-driving scenarios from extensive vehicle measurements including the implementation of road slope information. The scenarios are represented via a constrained number of compact CDCs which enables comprehensive investigations of new powertrain technologies under average as well as extreme real-driving conditions to develop efficiency-robust powertrain systems.

I. Introduction

The development of efficient, clean and cost-effective powertrain systems is a major concern of original equipment manufacturers (OEM) and their technology suppliers to establish a competitive market position within the automotive industry. Main drivers for this trend are market-specific limits for exhaust-gas emissions and fuel consumption that are introduced by national legislations as a response to local air pollution in metropolitan areas and the CO greenhouse effects. In order to comply with these limits, several measures such as usage of light-weight materials, exhaust-gas after-treatment, downsizing and electrification have been subsequently implemented to reduce emissions and fuel consumption of conventional vehicles. Today it is even more challenging to further increase the overall vehicle's efficiency. On the one hand, faster innovation processes reduce the desired time-to-market for new products and on the other hand the introduction of new technologies like hybrid or electric powertrains increase the complexity of product development.