Electric machines have broadly been used in many industries including the transportation industry. With the evolving trend of electrification in transportation, electric machines with higher power density and higher efficiency are demanded and, thus, more stringent thermal management requirements are needed for electrified vehicle applications. This study comprehensively presents various important aspects of thermal management in electric machines with the main focus on transportation applications. Design considerations, challenges, and methods for enhanced thermal management are discussed. Fundamental thermal properties of common materials are presented and sources of losses in various parts of machines are explained. Furthermore, typical cooling techniques and thermal analysis approaches for electric machines are reviewed in detail. This study will serve as a reference guideline for machine designers, who are interested in thermal management, and for thermal researchers working on electric machines.

This study presents an online multiparameter estimation scheme for interior permanent magnet motor drives that exploits the switching ripple of finite control set (FCS) model predictive control (MPC). The combinations consist of two, three, and four parameters are analysed for observability at different operating states. Most of the combinations are rank deficient without persistent excitation (PE) of the system, e.g. by signal injection. This study shows that high frequency current ripples by MPC with FCS are sufficient to create PE in the system. This study also analyses parameter coupling in estimation that results in wrong convergence and propose a decoupling technique. The observability conditions for all the combinations are experimentally validated. Finally, a full parameter estimation along with the decoupling technique is tested at different operating conditions.

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