Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions

Jari Vepsäläinen,Kevin Otto,Antti Lajunen,Kari Tammi

doi:10.1016/j.energy.2018.12.064

Jari Vepsäläinen, Kevin Otto + Show 2 more

Open Access

https://doi.org/10.1016/j.energy.2018.12.064

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Journal: Energy	Publication Date: Dec 11, 2018
Citations: 62	License type: cc-by

Affiliation: Aalto University, University of Helsinki

Abstract

The uncertainty of operating conditions such as weather and payload cause variations in the energy demand of electric city buses. Uncertain variation in energy demand is a challenge in the design of charging systems and on-board energy storages. To predict the energy demand, a computationally efficient model is required for real-time applications. We present a novel approach to predict energy demand variation with a wide range of uncertain factors. A factor identification is carried out to recognize the range of variation in the operating conditions. A computationally efficient surrogate model is generated based on a previously developed numerical simulation model. The surrogate model is shown to be 10 000 times faster than the numerical model. The surrogate model output corresponds with the numerical model with less than 1% error. The energy demand of the surrogate model varied from 0.43 to 2.30 kWh/km, which is realistic in comparison to previous studies. Successful sensitivity analysis of the surrogate model revealed the most crucial factors. Uncertainty in temperature, rolling resistance and payload contributed most to the variation in energy demand. Variation in these factors should be taken into account when predicting energy consumption and while planning schedules for a bus network.

Highlights

Most city buses are still diesel-powered, even though an electric powertrain would offer superior efficiency, higher peak torque at low speeds, zero tank-to-wheel emissions and lower noise levels [1]
We extend the idea presented by Asamer et al [15] to electric buses with a wider scope of input factors
The resulting margins for the 14 factors are presented in Table 1, which are divided into tolerance noise and extensive noise

Summary

Introduction

Most city buses are still diesel-powered, even though an electric powertrain would offer superior efficiency, higher peak torque at low speeds, zero tank-to-wheel emissions and lower noise levels [1]. Electric city buses are often recharged at the route terminuses or at bus stops to allow minimal battery size and charging power [4]. Passenger cars do not operate on predetermined routes, have no driving schedules nor need to have extra-auxiliary devices, such as pneumatic actuators for doors For these reasons, the electric vehicle studies can be used as a reference point, yet separate analysis is needed for the energy consumption of electric city buses. The prediction of the energy consumption of electric vehicles and buses have been investigated previously and can be divided according to the given input factors These input factors are mission-related kinematic factors (i.e., driving aggressiveness, stops/km and travel time) and vehicle-related factors (i.e., ambient temperature, auxiliary power consumption, payload, and drive efficiency). The driving style has been shown to have a notable impact on energy demand variation of electric city buses [11]

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