Study on the design method of time-variant driving cycles for EV based on Markov Process

Liu, Li; Huang, Chaosheng; Lu, Biao; Shi, Shuming; Zhang, Yan; Cheng, James

doi:10.1109/vppc.2012.6422709

Cited by 9 publications

(5 citation statements)

References 3 publications

(2 reference statements)

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“…Step IV: decoding predicted states into real physical values. Detailed information of general procedure of Markov chain prediction can be found in [45]. For practical applications, future probability distributions of vehicles' velocity, acceleration and drivers' power request can be predicted by Markov model.…”

Section: Markov Based Methodsmentioning

confidence: 99%

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

189

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Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mispredictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

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Section: Markov Based Methodsmentioning

confidence: 99%

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

189

View full text Add to dashboard Cite

show abstract

“…Calculate the average nominal velocity of each fragment, then distribute all velocity fragments into 7 state clusters according to the calculated average values, which are (−∞, 5], (5,15], (15,25), (25,35), (45, 55), and (55, +∞). Note that the nominal velocity cannot be directly applied to the driving cycle construction, and it only works in the fragment state cluster distribution where the road slope information is added into the traditional velocity-time trace and helps to make the state cluster distribution more reasonable.…”

Section: State Cluster Distributionmentioning

confidence: 99%

“…In the process of fragment selection, the Markov analysis method replaces the traditional random selection with transfer matrix estimation, which substantially improves the construction accuracy of driving cycles [13,14]. Liu et al used the Markov process to design time-variant driving cycles [15].…”

Section: Introductionmentioning

confidence: 99%

Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

et al. 2020

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A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

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“…One method type is based on traditional Markov chains (MC), and the other type is a kind of intelligent method derived from the MC method. The MC method [9]- [11] is typically used in the field of developing vehicle driving cycles, and presents great advantages in developing three-parameter driving cycles. For instance, when developing a three-parameter driving cycle that considers road grade, in contrast to traditional micro-trips combination optimization methods [12]- [14], this method avoids discontinuous road grade sequences [15].…”

Section: Introductionmentioning

confidence: 99%

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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Study on the design method of time-variant driving cycles for EV based on Markov Process

Cited by 9 publications

References 3 publications

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

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

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