Study on the combined design method of transient driving cycles for passenger car in Changchun

Liu, Li; Huang, Chaosheng; Liu, Minghui; Shi, Shuming

doi:10.1109/vppc.2008.4677594

Cited by 11 publications

(1 citation statement)

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“…Two examples of the systematic route choice are presented in Tamsanya et al (17), in which the selection is based on the speed and flow distribution on the network, and research by Hung et al (11), who used annual average daily traffic data to identify relevant routes. In another study, Liu et al took the road grade into consideration, besides the speed and volume (18).…”

Section: Onboard Instrumentation Techniquementioning

confidence: 99%

Evaluating Microtrip Definitions for Developing Driving Cycles

Nouri

Morency

2017

Transportation Research Record

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Climate change has become one of the most critical environmental concerns of the past decades, with greenhouse gas (GHG) emissions being identified as the main culprit. Globally, policy makers have been trying to reduce GHG emissions through various policies and strategies. Given that in North America transportation accounts for 30% of total emissions, it has become the focus of attention for GHG reduction initiatives. The use of emissions models is necessary to assess the potential impact of those initiatives. The main component for emissions measurement and estimation is the driving cycle, which can be summed up as the speed profile that represents driving behaviors. The accuracy of estimations of emissions strongly depends on the accuracy of the driving cycles used; using inaccurate driving cycles would not be representative of real-world driving patterns and could provide erroneous results, even if the model used were the most reliable possible. Driving-cycle development has different steps, one being to divide the speed profiles into smaller sections called microtrips. There are several methods for establishing the parameters of the microtrips created; in this study, such methods, as well as a new one based on distance, were compared to determine which method could result in the most accurate driving cycle. The results show that microtrips based on spatial characteristics provide more representative driving cycles, whereas among spatial characteristics, distance-based approaches resulted in the most accurate driving cycle.

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Section: Onboard Instrumentation Techniquementioning

confidence: 99%

Evaluating Microtrip Definitions for Developing Driving Cycles

Nouri

Morency

2017

Transportation Research Record

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Using the AMCE Algorithm to High-Efficiently Develop Vehicle Driving Cycles With Road Grade

et al. 2019

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Road grade greatly affects energy consumption and pollutant emission of heavy-duty vehicles. It is therefore necessary to develop representative three-parameter driving cycles while taking road grade into account. However, the low running efficiency of existing methods for developing driving cycles is a serious problem. To improve efficiency, inspired by the idea that intelligent algorithms with self-adaptivity can accelerate convergence, an adaptive Markov chain evolution (AMCE) method is proposed in this study. Based on the characteristics of the evolution strategies of a Markov chain evolution (MCE) satisfying the Markov property, a strategy boundary variable is defined to classify the MCE evolution strategies into two categories, one with the global and one with local search capability. Then, an adaptive probability equation is used to adjust the proportion of the evolution strategies, thus the evolution strategies can satisfy not only the Markov property of driving cycle but is also self-adaptive. By collecting driving data including the elevation information from heavy-duty vehicles driving on a highway, a three-parameter driving cycle with road grade was generated using the proposed method. In comparison with the MCE method, the proposed method can obtain the three-parameter representative driving cycles on the highway, and running efficiency was increased by 43.08% under the given conditions. Additionally, the rationality and necessity of the proposed method are fully verified.INDEX TERMS Vehicle driving cycle, road grade, self-adaptivity, Markov chain evolution, running efficiency.

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

et al. 2022

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When dealing with customers, original equipment manufacturers (OEMs) classify vehicular usage by resorting to simplified, often colloquial, descriptions that allow for a rough understanding of the operating conditions and the user's needs. In this way, the information retrieved from the customers is exploited to guide their choices in terms of vehicle design and configuration, based on the characteristics of the transport application, labeled using intuitive metrics. However, a common problem in this context is the absence of any formal connection to lower levels of representation that might effectively be used to assess vehicular energy performance in simulation, or for design optimization using mathematical algorithms. Indeed, both processes require more accurate modeling of the surroundings, including exhaustive information about the local road, weather, and traffic conditions. Therefore, starting with a detailed statistical description of the environment, this paper proposes a method for mathematical classification of transport missions and applications within the theoretical framework of the operating cycle (OC). The approach discussed in the paper combines a collection of statistical models structured hierarchically, called a stochastic operating cycle (sOC), with a bird's-eye view description of the operating environment. The latter postulates the existence of different classes, which are representative of the usage and whose definition is based on simple metrics and thresholds expressed mathematically in terms of statistical measures. Algebraic expressions, called operating classes in the paper, are derived analytically for all the stochastic models presented. This establishes a connection between the two levels of representation, enabling to simulate longitudinal vehicle dynamics in virtual environments generated based on the characteristics of the intended application, using log data collected from vehicles and/or information provided by customers. Additionally, the relationships between the two descriptions are formalized using elementary probability operators, allowing for an intuitive characterization of a transport operation. An example is adduced to illustrate a possible application of the proposed method, using six sOCs parametrized from log data collected during real-world missions. The proposed approach may facilitate the interaction between OEMs, customers, and road operators, allowing for planning of maintenance, and optimization of transport missions, components, and configurations using standard procedures and routines.INDEX TERMS Autoregressive models, mission classification, operating cycle, road transport mission, stochastic modeling, stochastic operating cycle.The associate editor coordinating the review of this manuscript and approving it for publication was Chih-Yu Hsu .

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Study on the combined design method of transient driving cycles for passenger car in Changchun

Cited by 11 publications

References 1 publication

Evaluating Microtrip Definitions for Developing Driving Cycles

Evaluating Microtrip Definitions for Developing Driving Cycles

Using the AMCE Algorithm to High-Efficiently Develop Vehicle Driving Cycles With Road Grade

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

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