A relevant challenge in hybrid electric vehicles (HEVs) and full EVs is the torque control of externally excited synchronous machines (EESMs). Effective torque control requires an efficient solution to the state reference generation problem, which is a nonlinear nonconvex optimization problem. The goal of this paper is to develop a state reference generation algorithm based on the gridding of the state and output spaces. First, an approximation defined over a cubic partition of the torque function with a piecewise affine (PWA) function is made. As a result, the state reference generation problem is reduced in each cube to solving a convex optimization problem. Moreover, this approach provides guarantees about the error bound introduced by the state reference generation procedure for the full operational state-space. To illustrate the effectiveness and robustness of the proposed algorithm, several real-time results obtained on an industrial hardware-in-the-loop (HIL) test-bench are presented. The obtained results show significant improvement compared with existing state-of-the-art reference generation methods.

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