Uncertainty in electric bus mass and its influence in energy consumption

Abstract

Throughout recent years, a public awareness of climate change and a social trend for preserving the environment have emerged. Transport sector is the principal contributor to greenhouse gas emissions, consequently electric buses are a great opportunity to reduce these emissions and fossil fuel dependence. To increase the competitiveness of electric buses, batteries with an accurate size are needed in order to optimize the charging infrastructure and reduce the total costs. Therefore, it is necessary to analyse the influence of certain parameters on electrical consumption.

This thesis evaluates the impact of passenger loads on the electrical consumption of an electric city bus and provides a reliable energy consumption forecast. An electrical consumption sensitivity analysis was created with the number of passengers in the bus as uncertainty. This uncertainty is stochastically modelled for each stop in the bus route based on actual data and it is evaluated with the Monte Carlo method. In addition, the uncertainty in the number of stops is also considered.

An algorithm for passenger load calculation was created in Matlab, based on driving cycles generated randomly (with a random number of stops and different speed profiles). Passenger data for each bus stop were represented by a normal probability distribution and they were related to each other using a multivariate normal distribution. These are the uncertain inputs of the model, as well as the number of stops which was modelled previously by another normal distribution. A validated electric bus model created in Simulink was simulated by means of the Monte Carlo sampling method, varying in each iteration the driving cycle and passenger flow introduced.

The results obtained for a particular bus route, described as a probability distribution, define an electrical consumption with an average of 0.549 kWh/km. It is also possible to assure with an 80% of probability that the electrical consumption in this route will be between 0.485 kWh/km and 0.613 kWh/km. These results represent an electrical consumption forecast for the route, including all the possible outcomes taking into account the uncertainties of the model. Moreover, the analysis of the results indicates that the passenger load has a clear influence on the bus electrical consumption that increases with the number of passengers. In addition, the results show a clear influence of driving cycle average speed and number of stops on the consumption. Electrical consumption increases as the number of stops increases and as the average speed decreases. The results also confirm that Monte Carlo method provides an efficient tool for estimating the consumption of an electric city bus since it enables to obtain results for the different possible scenarios and cover all the variations.