Comparative studies of Markov chain-based driving cycles for light-duty vehicles

ABSTRACT

With the massive ongoing efforts to mitigate climate change and reduce emission factors in the transportation sector, driving cycles (DCs) are becoming an essential tool for the testing and certification of distinct types of vehicles. Nevertheless, the open literature lacks careful comparative studies on the impact of different approaches in the development of Markov chain (MC)-based DCs, as well as DCs, developed specifically for the developing and highly congested urban areas in the Middle East and North Africa. Using a large dataset of 43 light-duty vehicles, driven over different areas in Greater Cairo, Egypt, this study aims to develop, compare, and benchmark 24 candidate MC-based DCs, against two clustering-based DCs, as well as four cycles used widely in the US and Europe. These 24 DCs differ in terms of the clustering algorithm (K-medoids and K-means), clustering parameters (different combinations of vehicle’s speed, acceleration, specific power, and percentage idling time), and definitions of microtrips (start-stop and fixed distance). The results show that the MC method outperforms random chaining of microtrips, with average relative root mean square errors (RRMSEs) of 15.8% and 23.6%, respectively. Clustering 350 m fixed distance-based microtrips using the vehicle’s speed, acceleration, and percentage idling time shows the least RRMSE of 8.207%. Defining microtrips based on fixed distance is also better than starts/stops for most vehicle types. The reference cycles (WLTP, NEDC, UDDS, and FTP-17) showed poor representativeness of the real-world data, with an average RRMSE of 76.8%. The same inferior performance of reference cycles, compared to the newly proposed ones, was also highlighted in the estimation of fuel consumption and emission factors. Hence, the proposed cycles and the reported comparative studies can be valuable tools for the assessment of emissions and fuel consumption in such developing metropolitan areas.

Highlights

1. 24 Markov chain driving cycles are developed and benchmarked for Cairo, Egypt.

2. Markov chain method outperforms the random chaining of microtrips.

3. The best-performing cycle has a relative root mean square error of 8.2%.

4. The best cycle clusters microtrips based on speed, acceleration, and idling time.

5. The proposed cycle is superior to WLTP, NEDC, UDDS, and FTP-17 for Cairo.

GRAPHICAL ABSTRACT

KEYWORDS:

• Driving cycle
• light-duty vehicles
• Markov chain
• clustering
• emissions
• Cairo

Acknowledgment

This research is supported by the Sustainable Transport Project for Egypt project, executed by the Transportation Programme, Development Research, and Technological Planning Centre at Cairo University, and sponsored by the Global Environment Facility (GEF) and UNDP Cairo. We also acknowledge the Egyptian Ministry of the Environment for allowing the data collection. Our appreciation is also extended to all experts who participated in the field inventory and data collection process.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The data supporting the results of this work are stated within the article and its supplementary material. The row data is confidential and could be acquired from the mentioned funding agency.

CRediT Authorship Contribution Statement

Conceptualization – MMK; MAH; HS; OAH; Methodology – MMK; MAH; OAH; Software – MMK; MAH; OAH; Validation – MMK; Formal analysis – MMK; MAH; OAH; Investigation – MMK; MAH; Resources – MMK; MAH; HS; OAH; Data Curation – MMK; OAH; Writing Original Draft – MMK; MAH; Writing Review & Editing – MMK; MAH; HS; OAH; Visualization – MMK; MAH; Supervision – MAH; HS; OAH; Project administration – MAH; HS; OAH; Funding acquisition – HS. MMK: Mahmoud M. Kamel; MAH: Muhammed A. Hassan; HS: Hindawi Salem; OAH: Omar A. Huzayyin