Abstract
This paper proposes a novel predictive strategy based on a model predictive control (MPC) for the interior permanent magnet synchronous motors (IPMSMs) driven by a three-level simplified neutral-point clamped inverter (3L-SNPC) for electric vehicle applications (EVAs). Based on the prediction of the future behavior of the controlled variables, a predefined multiobjective cost function incorporates the control objectives which are evaluated for every sampling period to generate the optimal switching state applied directly to the inverter without the modulation stage. The control objectives in this paper are tracking current capacity, neutral-point voltage balancing, common-mode voltage control, and switching frequency reduction. The principal concepts of the novel scheme are summarized as follows: first, the delay compensation based on the long horizon of prediction is adopted by a multilevel power converter structure. Second, based on the modified Lyapunov candidate function, both stability and recursive feasibility are ensured of the proposed predictive scheme. Third, the practicability of the real-time implementation is improved by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. Finally, the proposed concepts are implemented in the novel predictive control formulation as additional constraints without compromising the complexity and the good performances of the predictive controller. Therefore, only the switching states that guarantee the stability and the reduction of calculation burden criteria are considered in the evaluation of cost function. The proposed predictive scheme based on the “SVV” principle has demonstrated superior performance in simulation compared with the proposed scheme with the “SSV” principle. The computational burden and switching frequency rates are reduced by 35% and 56.22%, respectively.
Highlights
Permanent magnet synchronous machines (PMSMs) are widespread in many electric vehicle applications (EVAs) [1, 2]
We propose to reduce computation burden by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. e computational burden is reduced by 35%. e aspect of reduction of switching frequency is introduced and decreased by 56.22%
To compensate the computational delay, according to the long horizon of the prediction approach, we propose to use a two-step horizon of prediction, Section 2.2 and Figure 1. e improved cost function Γ ∗ with computational delay compensation of improved finite control set MPC (FMPC) (I-FMPC) is evaluated at time k + 2 to generate the optimal switching state μoapbct applied at time k + 1: Γ∗ Γi∗ + Γn∗p + Γc∗m + Γs∗w
Summary
Permanent magnet synchronous machines (PMSMs) are widespread in many electric vehicle applications (EVAs) [1, 2]. (v) In order to guarantee the practicability of the realtime implementation and computational efficiency of the proposed predictive control, “static voltage vector” (SVV) and “single state variation” (SSV) principles are proposed and embedded in MPC problem formulation as additional constraints. To compensate the computational delay for the 3L-SNPC inverter, using a two-step horizon of prediction, a discrete set of 212 441 possible trajectories of the switching states has to be enumerated for the evaluation of the cost function, Figure 2(a). To reduce the number of real-time evaluations, this paper proposes a modified two-step horizon prediction using a proposed “static voltage vector” (SVV), Figure 2(b), and “single state variation” (SSV), Figure 2(c), principles
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