High-Efficiency Driving Cycle Generation Using a Markov Chain Evolution Algorithm

Zhang, Man; Shi, Shuming; Nie, Lin; Yue, Baohua

doi:10.1109/tvt.2018.2887063

Cited by 23 publications

(13 citation statements)

References 23 publications

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“…The Markov chain evolution (MCE) framework [18] is used in this study, which combines random simulation sampling with genetic evolution to effectively improve design efficiency. In this study, emphasis should be placed on the improvement of the objective function and genetic operators; thus, the application of the MCE framework is expanded, as shown in Fig.…”

Section: Design Of Four-parameter Driving Cycles Based On An Extmentioning

confidence: 99%

“…The variable D was the set of states in the one-dimensional space, and Nd was the number of states in a one-dimensional space. The four-parameters state transition matrix is used to design the genetic operators, which can refer to [18]. Specifically, the two generated driving cycles were required to satisfy the four-parameter state transition relationship in the crossover operator.…”

Section: A Design Of Genetic Operators Meeting the Four-parameter Stmentioning

confidence: 99%

“…Using normalization, equation 14can be transformed into (16). Using the auxiliary function shown in (17), Equation (16) was transformed into (18). Thus, complex…”

Section: B Design Of Objective Function Considering Driving Gear Andmentioning

confidence: 99%

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Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

2020

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The driving gear affects engine transient cycles. Current methods of designing engine transient cycles need to establish the shift model of the transmission system, and the differences from the actual driving shift law result in the quantitatively low consistency of engine transient test cycles. Then the representativeness and accuracy of the designed engine transient and steady-state test cycles will be worse. By expanding the Markov chain evolution (MCE) framework, in this study, four-parameter driving cycles with gear for heavy-duty vehicles are designed, which can represent the consistency of the engine cycle distribution. The Markov chain model-based multi-parameter state transition with gear information is constructed to be used as constraints to design the genetic operators; engine characteristic model-based parameters are calculated as the constraints for designing the objective function. Multi-parameter vehicle driving cycles are thus generated by the expanded MCE framework and then transformed into engine transient test cycles. The designed driving cycles were verified and analyzed using the data collected from a heavy-duty vehicle. The results showed that the driving parameters and fuel consumption per 100 km between the designed driving cycles and the collected database met the threshold deviation; the correlation coefficients of the distributions related to gear utilization, vehicle specified power (VSP), and engine cycles reached as high as 90%; and multiple results had the same effect as mentioned above. Compared with a conversion method based on the economical shift rule, the fuel consumption rate distribution in this study can be closer to the actual engine running conditions. INDEX TERMS Driving gear, engine distribution, heavy-duty vehicle, vehicle driving cycles, fuel consumption NOMENCLATURE MCE Markov chain evolution VSP Vehicle specified power/ (Kw/ton) WHSC World harmonized steady-state cycle TPM Transition probability matrix PKE Positive kinetic energy per distance/ (m/s 2) OD-C Collected database of congested condition DC-C Designed cycle of congested condition CC-C Contrast cycle of congested condition OD-F Collected database of free-flow condition DC-F Designed cycle of free-flow condition CC-F Contrast cycle of free-flow condition OD-H Collected database of highspeed condition DC-H Designed cycle of highspeed condition CC-H Contrast cycle of highspeed condition 2D-NT Two-dimensional engine speed and torque distribution 3D-NT Three-dimensional engine speed and torque distribution

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Section: Design Of Four-parameter Driving Cycles Based On An Extmentioning

confidence: 99%

Section: A Design Of Genetic Operators Meeting the Four-parameter Stmentioning

confidence: 99%

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Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

2020

Self Cite

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“…In addition, despite the obvious advantage of the Markov chain-based method in driving cycle synthesis, the accuracy and the time efficiency are usually conflicting [7]. Reducing the interval and increasing the number of states can improve the accuracy of the synthesized driving cycle; however, it leads to a computational burden [42]. To overcome this drawback and facilitate the introduction of the passenger load information, we employ a new station-based method to construct a representative driving cycle considering the velocity and acceleration, for a bus route.…”

Section: Introductionmentioning

confidence: 99%

Study on Driving Cycle Synthesis Method for City Buses considering Random Passenger Load

Liu

Zhao

et al. 2020

Journal of Advanced Transportation

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This paper proposes a novel driving cycle construction method in consideration of velocity, road slope, and passenger load, based on a real-world bus route with a plug-in hybrid electric bus (PHEB). The main purpose is to address the disadvantage that an inaccurate reflection of the real-world driving characteristics for city buses will be caused when ignoring the passenger load in the course of a driving cycle synthesis. Two contributions are supplemented to distinguish from the previous research. Firstly, a novel station-based method is proposed aiming at developing a driving cycle with high accuracy. The kinematic segments are partitioned according to the distance of adjacent bus stops, while a two-dimensional Markov chain Monte Carlo method is employed to synthesize driving cycle between each interval of adjacent bus stops. Secondly, the random passenger load for different bus stops is treated as a discrete Markov chain model, according to the correlation analysis of the measured passenger data which are distinguished for off-peak and peak hours. Meanwhile, Monte Carlo simulation and maximum likelihood estimation are utilized to determine the most likely number of passengers for each bus stop. At last, the fuel consumption of the PHEB is simulated with the best-synthesized driving cycle and contrasted to the mean fuel consumption of the later measured data which is composed of the velocity, road slope, and the passenger load. The results demonstrate that the synthesized driving cycle has a higher accuracy on fuel consumption estimation.

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“…To improve the efficiency in simulation, a three-parameter driving cycle generation method based on Markov chain was also proposed [16]. However, the majority of the current construction methods of driving cycle are primarily aimed at conventional vehicles [17][18][19][20][21]; hybrid electric vehicles (HEV) are quite different from these vehicles in the driving process due to their special powertrain structure and energy consumption modes [22,23].…”

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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High-Efficiency Driving Cycle Generation Using a Markov Chain Evolution Algorithm

Cited by 23 publications

References 23 publications

Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

Study on Driving Cycle Synthesis Method for City Buses considering Random Passenger Load

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

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