Sage Journals: Discover world-class research

Abstract

The optimization algorithm is crucial for improving the precision and efficiency of driving cycle design. However, when combined with the Markov chain-based driving cycle design method, the effectiveness of the widely used genetic algorithm (GA) may be limited because of constraints in the state transition search space. In this study, a forward and inverse models-based Markov chain evolution (FI-MCE) method is proposed to efficiently and accurately design driving cycles, overcoming the limitations of the GA. Initially, the theoretical relationship between forward and inverse Markov chain models is demonstrated. By incorporating both models, any length of driving cycle can start and end in an idle state, enhancing the efficiency of constructing feasible solution sets. Then, the GA is enhanced with novel mutation and crossover strategies based on these models to simplify operations and overcome limitations. Furthermore, representative driving cycles across different lengths and different geographical regions are designed using FI-MCE to validate its effectiveness. Ultimately, comparing FI-MCE with existing methods demonstrates its superior accuracy and efficiency. Under identical circumstances, FI-MCE consistently outperforms existing methods by swiftly designing more precise driving cycles while ensuring high consistency with database feature distribution. This remarkable capability holds immense potential in overcoming the limitations associated with conventional methodologies.

Keywords

driving cycle Markov chain forward and inverse models evolution method consistency of distribution

Get full access to this article

View all access options for this article.

References

Tian

Huang

Fang

Driving Range Parametric Analysis of Electric Vehicles Driven by Interior Permanent Magnet Motors Considering Driving Cycles. CES Transactions on Electrical Machines and Systems, Vol. 3, No. 4, 2019, pp. 377–381.

Wang

Zhang

Zhou

Fan

Zeng

Genetic Algorithm-Based Fuzzy Optimization of Energy Management Strategy for Fuel Cell Vehicles Considering Driving Cycles Recognition. Energy, Vol. 263, 2023. https://doi.org/10.1016/j.energy.2022.126112.

Tutuianu

Bonnel

Ciuffo

Haniu

Ichikawa

Marotta

Pavlovic

Steven

Development of the World-wide harmonized Light duty Test Cycle (WLTC) and a Possible Pathway for Its Introduction in the European Legislation. Transportation Research Part D: Transport and Environment, Vol. 40, 2015, pp. 61–75.

State Administration for Market Regulation and Standardization Administration of the PRC. China Automotive Test Cycle - Part 1: Light-Duty Vehicles. GB/T 38146.1-2019. Beijing, China. 2019.

State Administration for Market Regulation and Standardization Administration of the PRC. China Automotive Test Cycle - Part 2: Heavy-duty commercial vehicles. GB/T 38146.2-2019. Beijing, China. 2019.

Zahid

State of Charge Estimation for Electric Vehicle Power Battery Using Advanced Machine Learning Algorithm Under Diversified Drive Cycles. Energy, Vol. 162, 2018, pp. 871–882.

Kharrazi

Almén

Frisk

Nielsen

Extending Behavioral Models to Generate Mission-Based Driving Cycles for Data-Driven Vehicle Development. IEEE Transactions on Vehicular Technology, Vol. 68, No. 2, 2019, pp. 1222–1230.

André

Hickman

A. J.

Hassel

Joumard

Driving Cycles for Emission Measurements Under European Conditions. SAE Transactions, Vol. 104, 1995, pp. 562–574.

André

The ARTEMIS European Driving Cycles for Measuring Car Pollutant Emissions. Science of the Total Environment, Vol. 334–335, 2004, pp. 73–84.

10.

Mayakuntla

S. K.

Verma

A Novel Methodology for Construction of Driving Cycles for Indian Cities. Transportation Research Part D: Transport and Environment, Vol. 65, 2018, pp. 725–735.

11.

Shen

Zhao

Zhan

Development of a Typical Driving Cycle for an Intra-City Hybrid Electric Bus with a Fixed Route. Transportation Research Part D: Transport and Environment, Vol. 59, 2018, pp. 346–360.

12.

Perhinschi

M. G.

Marlowe

Tamayo

Wayne

W. S.

Evolutionary Algorithm for Vehicle Driving Cycle Generation. Journal of the Air & Waste Management Association, Vol. 61, No. 9, 2011, pp. 923–931.

13.

Qiu

Cui

Wang

Feng

A Clustering-Based Optimization Method for the Driving Cycle Construction: A Case Study in Fuzhou and Putian, China. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 10, 2022, pp. 18681–18694.

14.

Lin

Niemeier

D. A.

An Exploratory Analysis Comparing a Stochastic Driving Cycle to California’s Regulatory Cycle. Atmospheric Environment, Vol. 36, 2002, pp. 5759–5770.

15.

Gong

Midlam-Mohler

Marano

Rizzoni

An Iterative Markov Chain Approach for Generating Vehicle Driving Cycles. SAE Internatoional, Vol. 4, No. 1, 2011, pp. 1035–1045.

16.

Ashtari

Bibeau

Shahidinejad

Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle. Transportation Science, Vol. 48, No. 2, 2014, pp. 170–183.

17.

Tong

H. Y.

Development of a Driving Cycle for a Supercapacitor Electric Bus Route in Hong Kong. Sustainable Cities and Society, Vol. 48, 2019. https://doi.org/10.1016/j.scs.2019.101588.

18.

Bhatti

A. H. U.

Kazmi

S. A. A.

Tariq

Ali

Development and Analysis of Electric Vehicle Driving Cycle for Hilly Urban Areas. Transportation Research Part D: Transport and Environment, Vol. 99, 2021, pp. 1–17.

19.

Zhao

Development of a Representative Urban Driving Cycle Construction Methodology for Electric Vehicles: A Case Study in Xi’an. Transportation Research Part D: Transport and Environment, Vol. 81, 2020, pp. 1–22.

20.

Shi

Lin

Zhang

Cheng

Huang

Liu

Research on Markov Property Analysis of Driving Cycles and Its Application. Transportation Research Part D: Transport and Environment, Vol. 47, 2016, pp. 171–181.

21.

Nyberg

Frisk

Nielsen

Using Real-World Driving Databases to Generate Driving Cycles with Equivalence Properties. IEEE Transactions on Vehicular Technology, Vol. 65, No. 6, 2016, pp. 4095–4105.

22.

Souffran

Miègeville

Guérin

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing. IEEE Transactions on Vehicular Technology, Vol. 61, No. 8, 2012, pp. 3454–3465.

23.

Cui

Zou

Chen

Gong

Optimization Based Method to Develop Representative Driving Cycle for Real-World Fuel Consumption Estimation. Energy, Vol. 235, 2021, pp. 1–18.

24.

Cui

Zou

Chen

Gong

A Novel Optimization-Based Method to Develop Representative Driving Cycle in Various Driving Conditions. Energy, Vol. 247, 2022, pp. 1–17. https://doi.org/10.1016/j.energy.2022.123455.

25.

Liu

Rodgers

M. O.

Guensler

Impact of Road Grade on Vehicle Speed-Acceleration Distribution, Emissions and Dispersion Modeling on Freeways. Transportation Research Part D: Transport and Environment, Vol. 69, 2019, pp. 107–122.

26.

Al-Wreikat

Serrano

Sodré

J. R.

Driving Behaviour and Trip Condition Effects on the Energy Consumption of an Electric Vehicle Under Real-World Driving. Applied Energy, Vol. 297, 2021. https://doi.org/10.1016/j.apenergy.2021.117096.

27.

Jia

Wang

Ouyang

Constructing Representative Driving Cycle for Heavy Duty Vehicle Based on Markov Chain Method Considering Road Slope. Energy and AI, Vol. 6, 2021. https://doi.org/10.1016/j.egyai.2021.100115.

28.

Zhang

Shi

Lin

Yue

High-Efficiency Driving Cycle Generation Using a Markov Chain Evolution Algorithm. IEEE Transactions on Vehicular Technology, Vol. 68, No. 2, 2019, pp. 1288–1301.

29.

Zhang

Shi

Cheng

Shen

Cao

Using the AMCE Algorithm to High-Efficiently Develop Vehicle Driving Cycles with Road Grade. IEEE Access, Vol. 7, 2019, pp. 160449–160458.

30.

Dabčević

Škugor

Topić

Deur

Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology. Energies, Vol. 15, No. 11, 2022, p. 4108.

31.

Shi

Zhang

Lin

Yue

Low-Cost Reconstruction of Typical Driving Cycles Based on Empirical Information and Low-Frequency Speed Data. IEEE Transactions on Vehicular Technology, Vol. 69, No. 8, 2020, pp. 8221–8231.

32.

Sericola

Markov Chains Theory, Algorithms and Applications. John Wiley & Sons, Inc., Hoboken, NJ; ISTE, London, 2013.

33.

Pan

Stochastic Process. Hunan University Press, Changsha, China, 1988.

34.

Yue

Shi

Lin

Guo

Zhang

Study on the Design Method of Driving Cycle with Road Grade Based on Markov Chain Model. Presented at 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), Montreal, QC, Canada, 2015.

35.

Shi

Wang

Zhang

Statistical Inference-Based Research on Sampling Time of Vehicle Driving Cycle Experiments. Transportation Research Part D: Transport and Environment, Vol. 54, 2017, pp. 114–141.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.62 MB

Forward and Inverse Models-Based Optimization Method of the Markov Chain to Accurately Design Driving Cycles

Abstract

Keywords

Get full access to this article

References

Supplementary Material