Abstract
As a key interface, Dual-Active-Bridge (DAB) converters are widely used in new energy system (NES). For the NES, which usually requires fast dynamic performance and high efficiency, model predictive control (MPC) can be a promising option. Classical MPC, depended on the model parameters, will produce steady-state deviation due to the inaccurate model. Among all parameters, the inductor has a major impact, which may even lead to control failure. To tackle this issue, we propose a novel MPC strategy combined with the recursive least square (RLS) in this paper. The inductance can be accurately identified and updated online in real time without adding new sampling devices. Finally, experimental results show the influence of the inaccurate inductance on MPC and prove that the proposed strategy: (i) can eliminate the steady-state deviation (ii) maintain the excellent dynamic performance of MPC.
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This work was supported by natural science foundation of china (52007106).
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Li, X., Liang, C., Dong, Z., Li, Z., Liu, Y., Zhang, Z. (2023). Model Predictive Control with Parameter Identification for DAB Converter Improving Dynamic and Static Performance. 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_31
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DOI: https://doi.org/10.1007/978-981-99-0553-9_31
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