This paper proposes a fuzzy model predictive direct torque control (FMP-DTC) strategy of interior perma- nent magnet synchronous motors (IPMSMs) for electric ve- hicle (EV) applications. The fuzzy logic control technique incorporated into the proposed FMP-DTC scheme dynam- ically 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 suit- able 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 perfor- mance evaluation is carried out via both MATLAB/Simulink and a prototype IPMSM test-bed with a TMS320F28335 digi- tal signal processor (DSP). Comparative simulation and ex- perimental results present the evidence of the performance improvements based on the proposed FMP-DTC strategy compared with the conventional MP-DTC strategy by indi- cating a fast transient torque response, low ripples, and an accurate speed tracking even under rapid climbing or emer- gency braking situations.This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government [Ministry of Science, ICT, and Future Plan- ning (MSIP)] under Grant 2015R1A2A2A01003513