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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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| 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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| Summary: | 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. |
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