An Iterative Markov Chain Approach for Generating Vehicle Driving Cycles

Gong, Qingjie; Midlam-Mohler, Shawn; Marano, Vincenzo; Rizzoni, Giorgio

doi:10.4271/2011-01-0880

Cited by 82 publications

(42 citation statements)

References 20 publications

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“…We model the stochastic process, i.e., X i , as a discrete-time finite-state Markov chain, inspired by the work in [12] and [13]. The Markov chain, as shown in Fig.…”

Section: B Microscopic Mobility Modelmentioning

confidence: 99%

Stochastic Analysis of a Single-Hop Communication Link in Vehicular Ad Hoc Networks

Abboud

Zhuang

2014

IEEE Trans. Intell. Transport. Syst.

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A vehicular ad hoc network (VANET) is a promising addition to our future intelligent transportation systems, which supports various safety and infotainment applications. The high node mobility and frequent topology changes in VANETs impose new challenges in maintaining a long-lasting connection between network nodes. As a result, the lifetime of communication links is a crucial issue in VANET development and operation. This paper presents a probabilistic analysis of the communication link in VANETs for three vehicle density ranges. First, we present the stationary distribution of the communication link length using mesoscopic mobility models. Second, we propose a stochastic microscopic mobility model that captures time variations of intervehicle distances (distance headways). A discrete-time finitestate Markov chain with state-dependent transition probabilities is proposed to model the distance headway. Third, the proposed stochastic microscopic model and first passage time analysis are used to derive the probability distribution of the communication link lifetime. Numerical results are presented to evaluate the proposed model, which demonstrate a close agreement between analytical and simulation results.

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“…We model the stochastic process, i.e., X i , as a discrete-time finite-state Markov chain, inspired by the work in [12] and [13]. The Markov chain, as shown in Fig.…”

Section: B Microscopic Mobility Modelmentioning

confidence: 99%

Stochastic Analysis of a Single-Hop Communication Link in Vehicular Ad Hoc Networks

Abboud

Zhuang

2014

IEEE Trans. Intell. Transport. Syst.

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“…Gong et al [144] developed a method for generating driving cycles with real-world driving properties for simulation and analysis of vehicles. Their method uses measured vehicle speeds from a number of vehicles to generate a Markov chain model.…”

Section: Phevsmentioning

confidence: 99%

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

Malikopoulos

2014

IEEE Trans. Intell. Transport. Syst.

194

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The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.Index Terms-Hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), supervisory power management control algorithms.

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“…Today the vehicle manufacturers use several different driving cycles with the objective that they will cover and represent realworld driving, however, the abovementioned changing driving behavior results in a need to get more driving cycles. There are several approaches to generate new driving cycles that are representative for a certain region of interest, e.g., the Markov chain approaches in (Lee and Filipi, 2011;Gong et al, 2011;Souffran et al, 2012) that extract typical behavior from large amounts of operational data.…”

Section: Generation and Transformation Of Driving Cyclesmentioning

confidence: 99%

Evaluation, Generation, and Transformation of Driving Cycles

Nyberg

2015

Linköping Studies in Science and Technology. Dissertations

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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...

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An Iterative Markov Chain Approach for Generating Vehicle Driving Cycles

Cited by 82 publications

References 20 publications

Stochastic Analysis of a Single-Hop Communication Link in Vehicular Ad Hoc Networks

Stochastic Analysis of a Single-Hop Communication Link in Vehicular Ad Hoc Networks

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

Evaluation, Generation, and Transformation of Driving Cycles

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