Neural network trained via reinforced learning as control for a battery super-capacitor hybrid energy storage system in electric vehicles
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Dueck, Kevin
issued: 2020-08-26T21:24:10Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Electric vehicles (EVs) are becoming a more popular alternative to internal combustion engine vehicles, however a concern among EV manufacturers and customers is the longevity of the EV's energy source, the battery. The battery is a large contributor to the cost of an EV and is susceptible to wear due to charge/discharge cycles and heat. This wear is a main depreciator to the EV's worth. Batteries are considered a low power density energy storage device, however a hybrid energy storage system (HESS) can be formed with an improved power density by interfacing batteries and super-capacitors (SCs). An HESS utilizes the high energy density of batteries and the high power density of SCs; this lowers the wear of the batteries by directing high current transients to the capacitors and lowering the heat generated by the batteries. Proper control is imperative to developing an effective HESS that will extend the life of the batteries. This thesis presents a novel control for a typical EV with a battery size of 24kWh coupled with a minimally sized HESS comprised of a SC bank with a 94.5kJ or 26.3Wh capacity using a neural network (NN) trained by a genetic algorithm. This method uses a NN to find patterns in simulated driving profiles to optimize the SCs' state of charge and SCs' current in a way that reduces the RMS current delivered by the batteries by up to 15% and reduces the peak currents by up to 52.5%. A 15% reduction in battery RMS current correlates to a 28% reduction the thermal energy produced by the battery due to its internal resistance. This reduction in heat reduces wear on the battery and simplifies thermal management strategies for the battery. This thesis will discus the construction of such a control system, the code of the genetic algorithm, parameter selection, and the effectiveness of this solution in controlling an HESS for practical use in consumer EVs.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75699
note: Neural Network Trained via Reinforced Learning asControl for a Battery Super-Capacitor Hybrid EnergyStorage System in Electric VehiclesbyKevin DueckB.A.Sc. Hons., The University of British Columbia, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)August 2020© Kevin Dueck, 2020The following individuals certify that they have read, and recommend to the College ofGraduate Studies for acceptance, a thesis/dissertation entitled:Neural Network trained via Reinforced Learning as Control for a BatterySuper-Capacitor Hybrid Energy Storage System in Electric VehiclesSubmitted by Kevin Dueck in partial fulfilment of the requirements of the degree ofMaster of Applied ScienceDr. Wilson Eberle, Faculty of Applied Science, UBC OkanaganSupervisorDr. Liwei Wang, Faculty of Applied Science, UBC OkanaganSupervisory Committee MemberDr. Morad Abdelaziz, Faculty of Applied Science, UBC OkanaganSupervisory Committee MemberDr. Zheng Liu, Faculty of Applied Science, UBC OkanaganUniversity ExamineriiAbstractElectric vehicles (EVs) are becoming a more popular alternative to internal combustionengine vehicles, however a concern among EV manufacturers and customers is the longevityof the EV’s energy source, the battery. The battery is a large contributor to the cost of anEV and is susceptible to wear due to charge/discharge cycles and heat. This wear is a maindepreciator to the EV’s worth. Batteries are considered a low power density energy storagedevice, however a hybrid energy storage system (HESS) can be formed with an improvedpower density by interfacing batteries and super-capacitors (SCs). An HESS utilizes thehigh energy density of batteries and the high power density of SCs; this lowers the wear ofthe batteries by directing high current transients to the capacitors and lowering the heatgenerated by the batteries. Proper control is…
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0392972
language: eng
degree discipline: Electrical 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/2429/75699