Fuzzy Model Predictive Direct Torque Control of IPMSMs for Electric Vehicle Applications

Abstract

This paper proposes a fuzzy model predictive direct torque control (FMP-DTC) strategy of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) applications. The fuzzy logic control technique incorporated into the proposed FMP-DTC scheme dynamically determines the appropriate values of the weighting factors, and then generates the optimal switching states that minimize the electromagnetic torque and stator flux errors. Unlike the conventional model predictive (MP)-DTC strategy, the optimal switching states of the proposed FMP-DTC are selected without retuning the weighting factors. It means that they are updated depending on the specific operating conditions. Therefore, the proposed FMP-DTC is effective in various operating conditions that make it suitable for the EV-traction operating environment. Hence, the proposed FMP-DTC method has a simple control structure and can explicitly handle the system constraints. The performance evaluation is carried out via both MATLAB/Simulink and a prototype IPMSM test-bed with a TMS320F28335 digital signal processor (DSP). Comparative simulation and experimental results present the evidence of the performance improvements based on the proposed FMP-DTC strategy compared with the conventional MP-DTC strategy by indicating a fast transient torque response, low ripples, and an accurate speed tracking even under rapid climbing or emergency braking situations.