Abstract
Dual Active Bridge (DAB) converters are widely used in energy storage systems, fuel cell systems and microgrids for its excellent characteristics. Recently, model predictive control (MPC), which is known for its high dynamic performance and multi-objective optimization capability, has been applied to DAB converters. MPC relies on feedback variables to achieve state variable prediction, where the sensor sampling noise will degrade MPC performance and greatly limits its application. In this work, we analyze the effect of sensor noise on the MPC performance of a DAB converter, and propose an MPC method with active sensor noise suppression. The proposed method independently sets the MPC discretization cycle and the switching cycle, so that the control performance deterioration can be suppressed without reducing the switching frequency or adding filters. The simulation results verify that compared with the classical MPC method, the proposed method can effectively reduce the inductor current stress and the output voltage ripple, suppress the transient bias, and improve the efficiency of the DAB converter, when the sensors suffer from sampling noise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao, B., Song, Q., Liu, W., et al.: Overview of dual-active-bridge isolated bidirectional DC–DC converter for high-frequency-link power-conversion system. IEEE Trans. Power Electron. 29(8), 4091–4106 (2013)
Yao, J., Chen, W., Xue, C., et al.: An ISOP hybrid DC transformer combining multiple SRCs and DAB converters to interconnect MVDC and LVDC distribution networks. IEEE Trans. Power Electron. 35(11), 11442–11452 (2020)
Guan, Y., Cecati, C., Alonso, J.M., et al.: Review of high-frequency high-voltage-conversion-ratio DC–DC converters. IEEE J. Emerg. Sel. Top. Ind. Electron. 2(4), 374–389 (2021)
Jafari, A., Nikoo, M.S., Karakaya, F., et al.: Enhanced DAB for efficiency preservation using adjustable-tap high-frequency transformer. IEEE Trans. Power Electron. 35(7), 6673–6677 (2019)
Xiao, Y., Zhang, Z., Andersen, M.A.E., et al.: Impact on ZVS operation by splitting inductance to both sides of transformer for 1-MHz GaN based DAB converter. IEEE Trans. Power Electron. 35(11), 11988–12002 (2020)
Chen, L., Shao, S., Xiao, Q., et al.: Model predictive control for dual-active-bridge converters supplying pulsed power loads in naval DC micro-grids. IEEE Trans. Power Electron. 35(2), 1957–1966 (2019)
An, F., Song, W., Yu, B., et al.: Model predictive control with power self-balancing of the output parallel DAB DC–DC converters in power electronic traction transformer. IEEE J. Emerg. Sel. Top. Power Electron. 6(4), 1806–1818 (2018)
Łakomy, K., Madonski, R., Dai, B., et al.: Active disturbance rejection control design with suppression of sensor noise effects in application to DC–DC buck power converter. IEEE Trans. Ind. Electron. 69(1), 816–824 (2021)
Han, M., He, H., Wang, X., et al.: Current-sensorless model predictive control of dual active bridge converters with Kalman filter. In: 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), pp. 663–667. IEEE (2021)
Acknowledgments
This work was supported by Jinan Innovation Team Funding Project (2020GXRC009-2).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y. et al. (2023). Model Predictive Control with Active Sensor Noise Suppression for Dual Active Bridge Converter. In: Cao, W., Hu, C., Chen, X. (eds) Proceedings of the 3rd International Symposium on New Energy and Electrical Technology. ISNEET 2022. Lecture Notes in Electrical Engineering, vol 1017. Springer, Singapore. https://doi.org/10.1007/978-981-99-0553-9_25
Download citation
DOI: https://doi.org/10.1007/978-981-99-0553-9_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0552-2
Online ISBN: 978-981-99-0553-9
eBook Packages: EnergyEnergy (R0)