Sensorless current estimation and sharing in multiphase input-parallel output-parallel DC-DC converters

Nowadays, the demand for high power converters for DC applications, such as renewable sources or ultra-fast chargers for electric vehicles, is constantly growing. Galvanic isolation is mandatory in most of these applications. In this context, the Solid State Transformer (SST) converter plays a fundamental role. The adoption of the Medium Frequency Transformers (MFT) guarantees galvanic isolation in addition to high performance in reduced size. In the present paper, a multi MFT structure is proposed as a solution to improve the power density and the modularity of the system. Starting from 20kW planar transformer model, experimentally validated, a multi-transformer structure is analyzed. After an analytical treatment of the Input Parallel Output Series (IPOS) structure, an equivalent electrical model of a 200kW IPOS (made by 10 MFTs) is introduced. The model is validated by experimental measurements and tests.

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Sensorless LC Filter Implementation for Permanent Magnet Machine Drive Using Observer-Based Voltage and Current Estimation

Sensors ◽

10.3390/s21113596 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3596

Author(s):

Chia-Ming Liang ◽

Yi-Jen Lin ◽

Jyun-You Chen ◽

Guan-Ren Chen ◽

Shih-Chin Yang

Keyword(s):

Permanent Magnet ◽

High Speed ◽

Pulse Width Modulation ◽

Output Current ◽

Current Harmonics ◽

Pwm Inverter ◽

Current Estimation ◽

Filter Output ◽

Lc Filter ◽

Parameter Error

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance.

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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽

10.3390/s21124068 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4068

Author(s):

Zheshuo Zhang ◽

Jie Zhang ◽

Jiawen Dai ◽

Bangji Zhang ◽

Hengmin Qi

Keyword(s):

Unscented Kalman Filter ◽

Time Estimation ◽

Estimation Algorithm ◽

Estimation Methods ◽

Fusion Algorithm ◽

State Variable ◽

Current Estimation ◽

Real Time Estimation ◽

And Control ◽

Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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