Data-driven power estimation of electric buses using deep LSTM neural networks and symbolic aggregate approximation filtering
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Zarei, Pegah
issued: 2022-01-31T08:00:00Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: This thesis proposes a deep learning framework for estimating the power use of electric vehicle drivetrains, using an artificial recurrent neural network architecture (long short-term memory), and techniques for data curation and analysis. Power samples of battery-electric buses operating in Victoria, B.C. were deployed to train, validate, and test the model. Microtrips were extracted from the dataset by taking out proportions of time spent at standstill. Anomalous trends of the dataset components were detected, marked as outliers, and excluded from the model input using Symbolic Aggregate approXimation (SAX) transformation filtering algorithm. A systematic optimization process is adopted to achieve the best hyperparameter configuration for this estimation task. The resulting fine-tuned deep LSTM model is then created, evaluated, and tested. More effort has been devoted to enhancing the efficacy of the model through investigating alternative model configurations and formulations. Among the several configurations tested, the best power consumption modeling approach yielded an error of about 5% (the RMSE of 0.05747) and a squared correlation coefficient of beyond 90% (R² of 0.92502). [This thesis was revised on October 25, 2021.]
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/76914
note: i DATA-DRIVEN POWER ESTIMATION OF ELECTRIC BUSES USING DEEP LSTM NEURAL NETWORKS AND SYMBOLIC AGGREGATE APPROXIMATION FILTERING by Pegah Zarei B.A.Sc., Sharif University of Technology, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2020 © Pegah Zarei, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Data-driven power estimation of electric buses using deep LSTM neural networks and symbolic aggregate approximation filterin Submitted by Pegah Zarei in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering Examining Committee: Dr. Walter Mérida (Supervisor) Professor, Department of Mechanical Engineering, UBC Dr. Dana Grecov (Supervisory Committee Member) Associate Professor, Department of Mechanical Engineering, UBC Dr. Fariborz Taghipour (Supervisory Committee Member) Professor, Department of Chemical and Biological Engineering, UBC iii Abstract This thesis proposes a deep learning framework for estimating the power use of electric vehicle drivetrains, using an artificial recurrent neural network architecture (long short-term memory), and techniques for data curation and analysis. Power samples of battery-electric buses were deployed to train, validate, and test the model. Microtrips were extracted from the dataset by taking out proportions of time spent at standstill. Anomalous trends of the dataset components were detected, marked as outliers, and excluded from the model input using Symbolic Aggregate approXimation (SAX) transformation filtering algorithm. A systematic optimization process is adopted to achieve the best hyperparameter configuration for this estimation task. The resulting fine-tuned deep…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0395392
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/76914