4.6 Article

Energy Demand Model of Battery E-Buses for LPT: Implementation, Validation and Scheduling Optimization

Journal

IEEE ACCESS
Volume 11, Issue -, Pages 52185-52198

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3280061

Keywords

Batteries; Urban areas; Torque; Numerical models; Energy consumption; Data models; State of charge; Public transportation; Scheduling; Charging infrastructure; electric bus; energy consumption; local public transport; state of charge; vehicle scheduling

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The conversion of conventional bus fleets into full-electric fleets is becoming a focus for public transport operators worldwide due to environmental awareness and urbanization trends. However, an efficient electrification process remains a challenge for most operators. This paper proposes an E-Bus vehicle model to estimate actual energy consumption and investigates optimization strategies for bus fleet utilization and charging solutions.
The progressive conversion of conventional bus fleets into full-electric fleets have gained focus in recent years, instilled by awareness about the environment and significant trends of urbanization. Public transport operators in major cities worldwide have put efforts into fulfilling this change. However, an efficient electrification process is still a challenge for most operators. This paper aims to propose an E-Bus vehicle model that estimates the actual energy consumption. The proposed model is implemented on the case study of a real bus line for Local Public Transport (LPT) and considers all technical characteristics of the vehicle. Real-time input data are represented by real driving cycles of the actual bus fleet and slope profile of the line. Real-time input data allow to establish directly the influence on the energy consumed during real operations. As simulation results, the global energy consumption and battery State of Charge (SOC) are then computed for the whole daily service operations. The simulation results are validated with the real data available and several scenarios are then considered within the simulations. Based on the results obtained, further improvements are proposed and discussed aiming to optimize the utilization of bus fleet, regarding both vehicles scheduling and new charging solutions.

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