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Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter

The decline of fossil fuels as main world energy sources due to the global energy crisis has brought to the proliferation of clean energy and environmentally friendly transportation. This development grows together with a complete ecosystem, including an electric vehicle (EV) charger system. The str...

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Main Author: Suliana, Ab Ghani
Format: Thesis
Language:English
Published: 2021
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/34671/1/Online%20auto-tuned%20proportional-integral%20controller%20using%20particle%20swarm%20optimization.ir.pdf
http://umpir.ump.edu.my/id/eprint/34671/
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spelling my.ump.umpir.346712022-10-14T03:35:06Z http://umpir.ump.edu.my/id/eprint/34671/ Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter Suliana, Ab Ghani T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The decline of fossil fuels as main world energy sources due to the global energy crisis has brought to the proliferation of clean energy and environmentally friendly transportation. This development grows together with a complete ecosystem, including an electric vehicle (EV) charger system. The strong demand for fast EV charging sparks the growth of technological advancements in an EV charging station, especially on DCDC converter. The dual active bridge (DAB) is among the popular DC-DC converters in literature due to its attractive feature; bidirectional power flow, galvanic isolation and high power density. This research investigated the effectiveness of a robust controller in DAB, where the objective is to minimize steady-state error, ess and improved dynamic response. Since the linear controller such as the proportional-integral (PI) controller has some flaws of stability and performance in the nonlinear system, a direct control voltage through a phase-shift angle, <p using particle swarm optimization (PSO), namely PSO-d was introduced to evaluate the DAB performance without a classic linear controller. This research also explores the optimization of PI controller parameters in DAB in term of accuracy and dynamic response, where Kp and Ki coefficients were optimized by computational intelligence. The PI optimization is concerned because the traditional manual tuning of the PI controller only delivers satisfactory performance as long as the affecting variables do not deviate far from the original tuning condition. APSO-PI is an auto-tuned PI using the PSO algorithm where the optimal values of Kp and Kiwere tuned at the initial control process only. Both performances of PSO-d and APSO-PI were compared to the conventional Ziegler-Nichols (ZN-PI) method. However, the controller with fixed gains has the same reaction to the changes and gives limitation by not fully controlling the system output as needed, especially in dynamic change. Ultimately, an online auto-tuned PI using PSO (OPSO-PI) was proposed to produce higher robustness than the APSO-PI. The OPSO-PI with re-tuning approach allows the update process of Kp and Ki parameters concerning the system change. The performance for all four controllers (ZN-PI, PSO-d, APSO-PI and OPSO-PI) were rigorously tested through realtime implementation. The tests were made possible using Typhoon hardware-in-the-loop (Typhoon-HIL), based on a 200 kW DAB converter operated at 20 kHz switching frequency with single phase-shift (SPS) modulation. The tests were carried out in steadystate and various test cases such as variation of loads, desired output voltage step-change, load step-change, and input voltage step-change. The PSO-d was able to achieve higher accuracy than the traditional ZN-PI. However, PSO-d's subpar performance in dynamic response put the controller as the slowest about the four. After thorough evaluation and analysis, both APSO-PI and OPSO-PI gave excellent performance by producing higher accuracy control than PSO-d while maintaining a faster response than ZN-PI. APSO-PI was the fastest controller. Meanwhile, OPSO-PI was a superior controller with 97.4 % accuracy with a bit sacrifice on dynamic response compared to APSO-PI. With these remarkable outcomes, there is a potential for the EV charger to have a rapid and accurate controller. 2021-07 Thesis NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34671/1/Online%20auto-tuned%20proportional-integral%20controller%20using%20particle%20swarm%20optimization.ir.pdf Suliana, Ab Ghani (2021) Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter. PhD thesis, Universiti Malaysia Pahang.
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Suliana, Ab Ghani
Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
description The decline of fossil fuels as main world energy sources due to the global energy crisis has brought to the proliferation of clean energy and environmentally friendly transportation. This development grows together with a complete ecosystem, including an electric vehicle (EV) charger system. The strong demand for fast EV charging sparks the growth of technological advancements in an EV charging station, especially on DCDC converter. The dual active bridge (DAB) is among the popular DC-DC converters in literature due to its attractive feature; bidirectional power flow, galvanic isolation and high power density. This research investigated the effectiveness of a robust controller in DAB, where the objective is to minimize steady-state error, ess and improved dynamic response. Since the linear controller such as the proportional-integral (PI) controller has some flaws of stability and performance in the nonlinear system, a direct control voltage through a phase-shift angle, <p using particle swarm optimization (PSO), namely PSO-d was introduced to evaluate the DAB performance without a classic linear controller. This research also explores the optimization of PI controller parameters in DAB in term of accuracy and dynamic response, where Kp and Ki coefficients were optimized by computational intelligence. The PI optimization is concerned because the traditional manual tuning of the PI controller only delivers satisfactory performance as long as the affecting variables do not deviate far from the original tuning condition. APSO-PI is an auto-tuned PI using the PSO algorithm where the optimal values of Kp and Kiwere tuned at the initial control process only. Both performances of PSO-d and APSO-PI were compared to the conventional Ziegler-Nichols (ZN-PI) method. However, the controller with fixed gains has the same reaction to the changes and gives limitation by not fully controlling the system output as needed, especially in dynamic change. Ultimately, an online auto-tuned PI using PSO (OPSO-PI) was proposed to produce higher robustness than the APSO-PI. The OPSO-PI with re-tuning approach allows the update process of Kp and Ki parameters concerning the system change. The performance for all four controllers (ZN-PI, PSO-d, APSO-PI and OPSO-PI) were rigorously tested through realtime implementation. The tests were made possible using Typhoon hardware-in-the-loop (Typhoon-HIL), based on a 200 kW DAB converter operated at 20 kHz switching frequency with single phase-shift (SPS) modulation. The tests were carried out in steadystate and various test cases such as variation of loads, desired output voltage step-change, load step-change, and input voltage step-change. The PSO-d was able to achieve higher accuracy than the traditional ZN-PI. However, PSO-d's subpar performance in dynamic response put the controller as the slowest about the four. After thorough evaluation and analysis, both APSO-PI and OPSO-PI gave excellent performance by producing higher accuracy control than PSO-d while maintaining a faster response than ZN-PI. APSO-PI was the fastest controller. Meanwhile, OPSO-PI was a superior controller with 97.4 % accuracy with a bit sacrifice on dynamic response compared to APSO-PI. With these remarkable outcomes, there is a potential for the EV charger to have a rapid and accurate controller.
format Thesis
author Suliana, Ab Ghani
author_facet Suliana, Ab Ghani
author_sort Suliana, Ab Ghani
title Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
title_short Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
title_full Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
title_fullStr Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
title_full_unstemmed Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter
title_sort online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge dc-dc converter
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/34671/1/Online%20auto-tuned%20proportional-integral%20controller%20using%20particle%20swarm%20optimization.ir.pdf
http://umpir.ump.edu.my/id/eprint/34671/
_version_ 1748180676082925568
score 13.211869

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