# Online PSO-tuned phase shift angle controller for dual active bridge DC–DC converter

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## Abstract

The deep concern towards efficient and green energy has brought to the development of intermediate energy storage system. Electric vehicles (EVs), for example, is an application where the energy storage devices are vital. In EV, the bidirectional DC–DC converter is high in demand due to its nature EV of having two or more separate DC sources. A recent technology called the dual active bridge (DAB) converter has quickly become popular in DC–DC converter thanks to its inherent galvanic isolation via a high-frequency transformer. Furthermore, DAB topology allows for high-efficiency conversion and soft switching adaption. This paper presents the optimization of the phase shift angle controller of DAB using Particle Swarm Optimization (PSO). From the preliminary study, it was found out that the optimization performs well until there are abrupt changes in the system. The system is not able to respond to the reference voltage step change as the traditional one-time execution PSO have local optima entrapped in react to the changes. The paper proposes a reset function to allow PSO to identify that its current optimized value is not optimal anymore. This drives PSO to look for a new optimal value to improve DAB’s overall dynamic performance. Simulation of the modified PSO is done on a 200 kW DAB system with 20 kHz switching frequency is developed in MATLAB/Simulink environment. Simulation has been carried out with the objective to minimize the steady-state error as well as to improve the dynamic performance of the DAB. The DAB performance with the proposed solution is evaluated in terms of steady-state error and transient response by testing the system under various reference voltage and step-change input voltage. The self-excited re-exploration PSO also have been presented as to respond to the reference voltage step change as the basic PSO have local optima entrapped in react to the changes. In order to validate the simulation results, a hardware-in-the-loop (HIL) experimental circuit is built in Typhoon HIL-402 to verify the dynamic response and the steady state performance of the system.

## Keywords

Dual active bridge Particle swarm optimization Online tuning Steady-state error Dynamic performance## 1 Introduction

Recently, electric vehicles (EVs) application has become an emerging technology in industry [1, 2]. The high demand of EVs application have carried to the development of intermediate storage system that required bidirectional power flow [3, 4]. Hence, the dual active bridge (DAB) DC–DC converter are most chosen as power conversion platform due to high efficiency, zero voltage switching, galvanic isolation with high frequency transformer and high density [5, 6, 7].

Basically, the phase shift angle between primary and secondary bridge will determine the magnitude and direction of the power in DAB converter. Therefore, many modulation techniques have been applied in DAB system to control the power transmission such as single phase shift (SPS), extended phase shift (EPS), dual phase shift (DPS) and triple phase shift (TPS) [8]. The difference among the aforementioned methods is the dimension control of the angle, which SPS is one dimension control as just control one angle between primary and secondary part. On the other hand, the two dimension control of EPS required one additional inner phase shift in primary or secondary bridge compared to the single dimension control in SPS. Whereas in DPS and TPS, both modulation have inner phase shift at primary and secondary bridges, which same degree for both inner phase shift in DPS is required and different degree should be applied in TPS modulation technique. In this paper, since the limited range of EV voltage is considered, the simplest and reliable of SPS modulation techniques is chosen as this technique can guarantee better efficiency and extend the soft switching region in DAB converter [3, 6, 8].

As to produce the optimal phase shift angle, many optimization technique have been proposed such as Lagrange multiplier method, mathematical programming methods, genetic algorithm (GA) and Ant Colony Optimization (ACO) [6, 9, 10]. Due to high computational burden and complicated techniques, an offline tuning method by using particle swarm optimization (PSO) is proposed in [6] where the swarm intelligence offers the advantages of reducing the computational load, easy to implement and high convergence speed. However, since the lookup table is required to implement the offline methods, it is not so practical to certain circumstances especially with the presence of disturbance in DAB system. Thus, the online tuning method using PSO algorithm is proposed in this paper to overwhelm the demerits of the offline tuning and offers a promising high robustness system.

## 2 Operating principle converter

*L*

_{k}is use to transfer energy from primary to secondary bridges and vice versa. Equation (1) presents the formulae to calculate the

*L*

_{k}. The ratio of high frequency transformer and voltage conversion ratio of DAB can be determine by using Eqs. (2) and (3) respectively. As the SPS modulation is applied, the voltage conversion ratio,

*k*must close to one in ensuring soft switching and high efficiency in DAB [6]. Thus, the operating voltage that was considered in this paper is 250–420 V with range of

*k*is 0.67–1.12.

## 3 Particle swarm optimization algorithm

Particle swarm optimization is a swarm intelligence that was initiated by Kennedy and Ebehart [13]. The PSO imitate the behavior of birds flock where its intelligence are able to resolve any optimization problems with group communications [14]. The PSO are chosen in this research paper due to its simplicity and efficient approach [15]. Starting with the random initialization value of the phase shift angle (initial position), particles population doing the searching process. In PSO, each particles have potential solution that reflect to the optimization objective, where the minimum steady state error is the objective in this online tuning method.

*P*

_{best}) in every iteration. The global best (

*G*

_{best}) is the best fitness that was selected among the best particles. The position and velocity of the particles will be updated correspondingly using Eqs. (4) and (5) at the end of each iteration.

*i*, \( G_{best} \) = best position of the particles and \( k \) representing the iteration number.

Design parameters of DAB converter

Item | Parameter |
---|---|

Transformer turns ratio, | 2 |

Rated power, | 200 kW |

Switching frequency, | 20 kHz |

Input voltage, | 750 V |

Output voltage, | 500 V |

Leakage inductance, | 24 µH |

## 4 Results and discussion

Results for difference reference voltages

Vo (V) | Steady state error (%) | Settling time (s) | ||
---|---|---|---|---|

Simulation | Experimental | Simulation | Experimental | |

270 | 0.62 | 1.81 | 0.1081 | 0.1570 |

300 | 0.44 | 3.94 | 0.1331 | 0.1497 |

365 | 0.48 | 1.89 | 0.1047 | 0.1128 |

400 | 0.70 | 1.65 | 0.1043 | 0.1008 |

420 | 1.11 | 2.45 | 0.1041 | 0.0970 |

Dynamic response of DAB (V_{in} step change)

V | Rise time (s) | Settling time (s) | ||
---|---|---|---|---|

Simulation | Experimental | Simulation | Experimental | |

420 | 0.0009 | 0.0014 | 0.3220 | 2.0718 |

400 | 0.0010 | 0.0016 | 0.3226 | 2.0716 |

330 | 0.0010 | 0.0013 | 0.3217 | 2.0715 |

Dynamic response of DAB (V_{ref} step change)

Step V | Steady-state error (%) | Settling time (s) | ||
---|---|---|---|---|

Simulation | Experimental | Simulation | Experimental | |

420–365 | 0.48 | 2.07 | 0.3627 | 2.1261 |

300–420 | 0.96 | 2.82 | 0.3442 | 2.0970 |

250–375 | 0.80 | 3.30 | 0.3359 | 2.0900 |

## 5 Conclusion

This paper has proposed the online tuning phase shift angle of DAB DC–DC converter using traditional one-time execution PSO algorithm and using Self-excited re-exploration PSO algorithm. The optimal phase shift angle produced the output voltage with minimal steady state error and good dynamic response for various reference voltages, input voltage step changes and reference voltage step changes. The simulation and experimental results show that the proposed method can produce less than 5% steady state error and have good performance of transient response.

## Notes

### Acknowledgements

This work is supported by Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, under research Grant RDU1703129.

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

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