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Neural network trained via reinforced learning as control for a battery super-capacitor hybrid energy storage system in electric vehicles

University of British Columbia

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.

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2020-08-26

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75699

Electrical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/