Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology

Abstract

The authors of this paper propose a Markov-chain-based method for the synthesis of naturalistic, high-sampling-rate driving cycles based on the route segment statistics extracted from low-sampling-rate vehicle-tracking data. In the considered case of a city bus transport system, the route segments correspond to sections between two consecutive bus stations. The route segment statistics include segment lengths and maps of average velocity, station stop time, and station-stopping probability, all given along the day on an hourly basis. In the process of driving cycle synthesis, the transition probability matrix is built up based on the high-sampling-rate driving cycles purposely recorded in a separate reference city. The particular emphasis of the synthesis process is on satisfying the route segment velocity and acceleration boundary conditions, which may be equal to or greater than zero depending on whether a bus stops or passes a station. This enables concatenating the synthesized consecutive micro-cycles into the full-trip driving cycle. The synthesis method was validated through an extensive statistical analysis of generated driving cycles, including computational efficiency aspects.

A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses

“…Those driving cycles were then fed to the developed physical e-bus model to obtain the energy consumption data. The transformation was based on the Markov chain synthesis method proposed in [13].…”

“…In total, 4057 synthetic driving cycles were generated [13]. Each cycle is unique with respect to route (considering diverse road and traffic conditions, including varying road grades) and trip (considering fluctuating traffic and ridership conditions).…”

“…The elementary models link electric energy consumption with basic features of driving cycles such as distance traveled [12]. Physical models generally offer high prediction accuracy, but they are often unsuitable for application in transport system planning due to demand on usually unavailable data related to high sampling rate driving cycles [13], and physical parameters and maps of vehicle powertrains [14]. With the development of machine learning techniques, data models are gaining popularity owing to their adaptability and ability to describe complex energy consumption patterns.…”

Advances in Electric Traction System—Special Issue

“…The papers presented in this Special Issue cover a wide range of selected problems of advances in electric traction systems [1][2][3][4][5][6][7][8], electric drives for ground and air transport [9][10][11], DC [12,13] and AC [14,15] power supply used in network traction, automation and control [16,17], and energy savings that help the environment [18].…”

“…Paper [16] presents a Markov-chain-based method for the synthesis of high-samplingrate (HSR) driving cycles for buses that satisfy statistical features extracted from lowsampling-rate (LSR) vehicle-tracking data over trip segments. The proposed HSR driving cycle synthesis method was validated against the LSR recorded data in terms of micro-cycle mean velocity and stopping probability.…”