A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging
Tianlu Ma, Chaoqiang Jiang, Chen Chen, Yibo Wang, Jiayi Geng, Chi K. Tse
Abstract
Dynamic wireless charging (DWC) technology can help alleviate the problem of short driving range for battery-powered vehicles. In this article, a model predictive control (MPC) is applied to the buck converter on the secondary side of a DWC system to address fast output fluctuations. This approach features a fast-dynamic response, and no communication link is required. To solve the key issue of MPC, which is the computational burden, a polynomial fitting method based on the parsing solution of the sampled-data model is proposed. The complex matrix exponential calculation is replaced by simple polynomial operations, and the optimal duty cycle can be calculated directly by solving a quadratic function. This significantly reduces the computational burden. A DWC experimental setup is constructed, and results show that the proposed MPC has a better dynamic performance compared to proportional-integral control. The adjustment time is only 140 μs (around seven switching cycles) when the reference voltage is stepping. Moreover, the computational burden for matrix calculation in two-step prediction can be reduced by 50.6% and 79.7% compared to the lookup table and Taylor series approximation, respectively. Meanwhile, MPC with current limitation is analyzed and demonstrates a neat spectrum, small ripple but large response time.