Aiming at the problems of output voltage fluctuation and current total harmonic distortion (THD) in the front stage totem-pole bridgeless PFC of two-stage V2G (Vehicle to Grid) vehicle-mounted bi-directional converter, a fuzzy linear active disturbance rejection control strategy for V2G front-stage AC-DC power conversion system is proposed. Firstly, the topological working mode of the totem-pole bridgeless PFC is analyzed, and the mathematical model is established. Combined with the system model and the linear active disturbance rejection theory, a double closed-loop controller is designed with the second-order linear active disturbance rejection control as the voltage outer loop and PI control as the current inner loop. The controller can realize self-adaptive tuning of the proportional gain coefficient of the active disturbance rejection controller through fuzzy reasoning and realize self-adaptive control. Simulation and experimental results show that this method can better solve the problems of slow system response and high total harmonic distortion rate of input current and effectively improve the system’s robustness.

With the increasing proportion of power electronic energy in new energy power systems, the indirectness, randomness, and load time-space mismatch of renewable energy power generation seriously threaten the power grid’s security [

Totem-pole bridgeless PFC converter is applied to the front stage circuit of the V2G vehicle converter because of its simple control, few switching devices, and high power density [_{p} and K_{i} parameters. To avoid the problem that the reference value of the current loop depends too much on the voltage loop, Hou et al. [

Nonlinear control can effectively compensate for unknown interference. Therefore, Hasan et al.[

This paper proposes a Fuzzy LADRC control strategy for a totem-pole bridgeless PFC converter, which combines Fuzzy LADRC voltage loop control and PI current loop control. The method treats model errors and circuit parameter perturbations as total disturbances to the system, and the total disturbance is compensated by the Linear Extended State Observer (LESO). Fuzzy inference is added to realize the adaptive dynamic adjustment of the LADRC proportional gain coefficient, which can optimize the voltage loop’s control parameters. The optimized voltage control quantity is used as the reference quantity of the current inner loop further to reduce the total harmonic distortion of the current. The contributions and innovations of this paper also include: (1) Keep the THD within the limits of IEEE-519. (2) Model information is thoroughly used in designing the Fuzzy LADRC controller, which eliminates the defects of traditional model-free information in the design of the LADRC controller. (3) Only one fuzzy control variable is used in the design of the Fuzzy LADRC controller, which reduces the difficulty of application and the dependence on fuzzy rules; (4) Compared with the literature [

The circuit structure of the two-stage V2G vehicle-mounted bi-directional converter is shown in

As shown in _{s}, high-frequency switches _{1} and _{2}, power frequency switches _{3} and _{4}, and capacitor _{1}. The converter operates in current continuous mode (CCM), which can reduce inductance current ripple and improve power factor.

High-frequency switch tubes _{1} and _{2} use switching frequency as the cycle to realize the storage of inductor _{s} energy and load energy supply, while power frequency switch tubes _{3} and _{4} work at 50 Hz switching frequency to provide energy continuation channel.

According to the working state of the inductor and the working cycle of the input AC voltage, the working mode of the system in the CCM is analyzed as follows: the working mode of the energy storage of the inductor _{s} when the grid voltage is positive is shown in _{1} and _{3} are off, and the current passes through inductive _{s}, switch tubes _{2} and _{4} to form a closed circuit for inductive energy storage. Load R is CLLC resonant converter powered by capacitor _{1}.

_{s} in the energy release stage in the positive half cycle. _{4} remains on during the positive half cycle of the grid, _{2} is off and _{1} is on, the inductor begins to release energy, and the energy released by the inductor supplies power to R and the capacitor through the AC grid. The voltage on the load side is a dual-frequency pulsating DC voltage.

_{1} and _{3} are turned on, and current flows through switches _{1} and _{3} to form a closed loop for the inductor to store energy. When the inductor releases energy, switch _{1} is turned off, and _{2} is turned on, forming a closed loop for inductor energy release.

Take the energy storage and release state of the inductor as a switching cycle T. If the duty cycle is set as _{in}, then the state equation of the circuit at the inductive energy storage stage is:

The circuit state equation of the energy release phase of the inductor in the 1-

Combining

The small-signal disturbance is added to the input of the circuit and each state variable, and the small signal model is solved by the disturbance method. The expression of each disturbance is:

In _{s(t)}, _{BUS(t)}, _{in(t)} are the direct current (DC) components of each state variable,

Ignoring the high-order components and equivalently reducing the DC components at both ends in

From

According to

The two-stage topology of the V2G vehicle-mounted converter is a complex system with strong coupling and variable parameters. In practical application, some phenomena exist, such as perturbation of battery terminal voltage parameters. Therefore, a fuzzy linear active disturbance rejection controller is designed in this paper. The controller can adaptively adjust the proportional gain of the active disturbance rejection controller through fuzzy reasoning, which realizes adaptive control. It effectively improves the robustness of the electric vehicle under complex working conditions and reduces the total harmonic distortion.

The front-stage circuit of the V2G bidirectional converter adopts double closed-loop control. The outer voltage loop stabilizes the output bus voltage, and the inner current loop realizes the function of the PFC. The structure of the double closed-loop controller is shown in _{BUS} output by the totem-pole bridgeless PFC converter is subtracted from the reference voltage _{ref}, and the error value _{err} is obtained. The error value _{err} passes through the control output control quantity of the voltage outer loop Fuzzy LADRC, The control quantity is multiplied with the AC sinusoidal half-wave to obtain the current reference quantity _{ref} consistent with the phase of the input voltage. The reference value _{ref} is subtracted from the sampling current _{s} of the inductor to obtain the current error value. The current error value is input to the PI compensator of the inner current ring and output control quantity. After limiting the amplitude, the microprocessor generates pulse modulation waves to realize the control of switch tubes _{1}~_{4} and finally realize the double closed-loop control of the V2G front-stage totem-pole bridgeless PFC converter.

The core idea of LADRC is to use LESO to estimate the internal disturbance caused by imprecise mathematical models and parasitic parameters of devices and the external disturbance caused by environmental factors. By estimating and compensating the total disturbance of the system in real-time, the controlled object is compensated as a linear integrator in series [

The differential equation of the LADRC second-order controlled object is:

_{1} and a_{0} are unknowns, the known part of _{0}.

Set the total disturbance of the system as:

According to the totem-pole bridgeless PFC mathematical model and power balance equation, the following equation can be obtained [

Let x_{1} =

Let _{1} = y, x_{2} = _{3} = f, the state equation expression of the system can be obtained as [

According to

_{1}~ _{3} are state variable matrices, then, as long as the appropriate observer gains

The expression of the proportional differential controller is:

_{p} is the proportional gain of the controller, _{d} is the differential gain of the controller.

According to

According to

_{o} represents the observer bandwidth, _{c} represents the controller bandwidth.

After the above analysis, because the totem-pole bridgeless PFC mathematical model makes some parameters of LADRC known, the parameters to be tuned for LADRC are only _{o} and _{c}. In engineering, _{o} = (3~5) _{c} is generally taken.

Electric vehicle batteries are easily affected by ambient temperature and SOC, which leads to significant changes in their electrochemical characteristics, and the performance of the system can be improved by adaptively adjusting the controller parameters. Therefore, in this paper, the LADRC proportional gain coefficient kp is changed timely by introducing fuzzy control. The proportional gain coefficient kp can compensate for the observation error of the state observer, thereby improving the system’s robustness. The method is to calculate the difference _{p} as the output, and then according to the voltage difference _{p}. The principle is: the proportional gain coefficient at the time (T−1) is _{p} (T−1), and the proportional gain adjustment value obtained by fuzzy reasoning is recorded as Δ_{p}. Then, the proportional gain coefficient of LADRC at time T can be obtained as _{p} (T) = _{p} (T−1) + Δ_{p}. The structure of the fuzzy controller is shown in

According to the experience of simulation and debugging, the range of the DC bus voltage difference E and its difference rate of change Δ_{p} is [−6, 6]. The fuzzy subsets are taken as seven subsets such as NB (negative large), NM (negative medium), NS (negative small), Z0 (zero), PS (positive small), PM (positive medium), and PB (positive large).

The fuzzy rules formulated according to the experiment are shown in

ΔK_{p} |
ΔE | |||||||
---|---|---|---|---|---|---|---|---|

NB | NM | NS | Z0 | PS | PM | PB | ||

NB | PB | PB | PM | PM | PS | PS | Z0 | |

NM | PB | PM | PM | PM | PS | Z0 | NS | |

NS | PM | PM | PS | Z0 | Z0 | NS | NS | |

E | Z0 | PM | PS | Z0 | Z0 | Z0 | NS | NM |

PS | PS | PS | Z0 | Z0 | NS | NM | NM | |

PM | PS | Z0 | NS | NS | NM | NM | NB | |

PB | Z0 | NS | NS | NM | NM | NB | NB |

The simulation models of the totem-pole bridgeless PFC power circuit, PI double closed-loop control circuit, and Fuzzy LADRC double closed-loop control circuit are built by MATLAB/Simulink. The comparative simulation experiments are carried out under different working conditions. Finally, an actual physical platform is built, and experiments are carried out to verify the correctness and effectiveness of the proposed control strategy.

The optimal parameters of PI controller are as follows: voltage outer loop K_{p} = 9.95, K_{i} = 10000, current inner loop K_{p} = 3.1, K_{i} = 30. The LADRC voltage outer loop parameters to be adjusted include observer bandwidth _{o} and controller bandwidth _{c}. Considering their optimal values, _{o} is 6050 and _{c} is 1600. The system simulation parameters are shown in

Symbol | Name | Value | Unit |
---|---|---|---|

System power | 4 | kW | |

_{N} |
AC voltage | 220 | V |

_{N} |
Grid frequency | 50 | Hz |

Energy storage inductors | 480 | μH | |

Filter capacitor | 3450 | μF | |

_{BUS} |
DC voltage | 400 | V |

_{s} |
Switching frequency | 50 | kHz |

Load resistance | 40 |

In order to verify the robustness of the proposed control strategy and the effectiveness of reducing harmonic distortion rate, Fuzzy LADRC and PI algorithms are used to compare the simulation results of the system startup, load mutation, and reference voltage mutation, respectively.

Performance | Fuzzy LADRC | PI |
---|---|---|

Grid current THD/% | 2.390 ↓ | 3.350 |

Overshoot/% | 1.038 ↓ | 1.086 |

Steady-state time/ms | 150.1 ↓ | 240.4 |

Voltage oscillation amplitude/V | 24.22 ↓ | 60.96 |

Voltage ripple/% | 2.28 | 2.28 |

In order to test the response capability of the system, the reference value of the system’s output voltage is reduced from 400 to 350 V at 0.5 s, and the simulation results are shown in

We can see from

Performance | Fuzzy LADRC | PI |
---|---|---|

Grid current THD/% | 1.860 ↓ | 2.460 |

Steady-state time/ms | 276.7 ↓ | >400.0 |

Voltage oscillation amplitude/V | 27.20 ↓ | 31.91 |

Voltage ripple/% | 2.19 | 2.19 |

Performance | Fuzzy LADRC | PI |
---|---|---|

Grid current THD/% | 1.660 ↓ | 2.670 |

Steady-state time/ms | 173.2 ↓ | 223.7 |

Voltage oscillation amplitude/V | 33.53 ↓ | 45.90 |

Voltage ripple/% | 3.60 ↓ | 4.95 |

According to the simulation results of the system under different working conditions, we can find that the Fuzzy LADRC control strategy can effectively improve the dynamic and steady-state performance of the totem-pole bridgeless PFC topology circuit and effectively reduce THD. Simulation results verify the effectiveness of this method.

Control algorithms | Authors | THD |
---|---|---|

Ref. [ |
Lv et al. | 2.42 |

Ref. [ |
Ma et al. | 2.01 |

Ref. [ |
Zhang et al. | 3 |

Ref. [ |
Ruan et al. | 1.98 |

Ref. [ |
Zafer et al. | 2.64 |

Proposed control algorithm | Li et al. | 1.66 |

We have developed a 3 kW V2G bidirectional converter to verify the control effect of the proposed method in practical application. The experimental platform and prototype are shown in

Symbol | Name | Value | Unit |
---|---|---|---|

_{AC} |
AC voltage | 220 | V |

_{DC} |
DC voltage | 320~650 | V |

_{S} |
Switching frequency | 100 | kHz |

System power | 3 | kW | |

Load resistance | 200 | ||

Efficiency | >95 | % |

When the output DC voltage is 400 V, and the load changes from 200 to 100

When the output voltage is 400 V and the load changes from 200 to 400

When the load is 100

According to

The experimental results show that the control method in this paper has more robust adaptability, can compensate for the unknown disturbance in real-time, and the output voltage is smoother when the working condition changes.

This paper proposed a Fuzzy LADRC control strategy for the V2G totem-pole bridgeless PFC converter. Through the simulation and experimental comparison with the traditional PI control, the conclusions are as follows: (1) The disturbance caused by load change can be solved by self-tuning the LADRC proportional gain coefficient through fuzzy reasoning. The experimental results show that compared with PI control, the response time of this method at the sudden increase and decrease of the output voltage is reduced by 95 and 61 ms, respectively, and the voltage fluctuation is reduced by 27 and 28 V, respectively. When the load suddenly increases and decreases, the response time decreases by 149 and 108 ms, respectively, and the voltage fluctuation decreases by 27 and 21 V, respectively. Therefore, the response speed of this method is faster, the anti-interference ability is significantly improved, and the dynamic and steady-state performance of the system is improved considerably. (2) Fuzzy LADRC controllers can estimate and compensate for load disturbance in real time. Therefore, compared with the PI controller and references [

This project is supported by the

The authors declare that they have no conflicts of interest to report regarding the present study.