Evaluation, Generation, and Transformation of Driving Cycles

Abstract

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 prod...

A proposal for an operating cycle description format for road transport missions

“…The high-level description offered by the Global Transport Application (see Edlund and Fryk [5]) is one example, where the mission is divided into different classes based on statistical measures. Similarly, there are many approaches to Markov-based methods, for example: Johannesson et al [20], Nyberg [1] and Speckert et al [21], where the mission is described in terms of parameters belonging to one or several stochastic models. These types of high-level descriptions cannot excite a vehicle model by themselves, but instead use an intermediary step: often by generating data to a conventional driving cycle.…”

“…Simulations require detailed models of the vehicle and its control systems, and a great deal of effort has been put into this, both by original equipment manufacturers and governmental agencies. However, regardless of how detailed the vehicle model is, the prediction will be misleading unless it is excited in the right way by the right physical entities [1]. Naturally, the driver and environment are the cause of the excitations, and the focal point of this study is the question of how to describe the environment.…”

“…In traditional and simple simulation modelling environments, the vehicle moves forward along a 'straight' line. Among the many examples in this category is VECTO *Correspondence: parp@chalmers.se 1 Chalmers University of Technology, Göteborg, Sweden Full list of author information is available at the end of the article [2]. It is becoming increasingly evident that many of the important control systems on modern vehicles rely on more accurate descriptions of the vehicle motion to be able to show good correlation between simulation and measurements.…”

A Classification Method of Road Transport Missions and Applications Using the Operating Cycle Format

“…Specifically, many methods proposed by researchers rely on simplified models for longitudinal vehicle dynamics in combination with reference speed profiles (called driving cycles) to simulate representative operating conditions. Starting from log data, various techniques may be employed to build driving cycles, using an assortment of measures like acceleration, mean speed and torque, cruising time, road grade, et cetera [18], [28]- [31], [31]- [53]. In particular, a first distinction may be made between rule-based methods [54], [55] and statistical ones [18], [28]- [37].…”

Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

“…While ensuring comparability of test results, their deterministic nature and lack of real-life aspects such as road grade limit the meaningfulness of the results. The drive cycles' representativeness for actual driving conditions is relevant to ensure robustness and prevent sub-optimality of vehicle designs [120]; while they are commonly used for design and evaluation, adaption to changing driving conditions is needed to maintain representativeness. The impact of using different drive cycles in the design process is illustrated in various optimization experiments.…”