Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles

• Published in: IEEE transactions on energy conversion Vol. 36; no. 3; pp. 2217 - 2226
• Main Authors: Diao, Kaikai, Sun, Xiaodong, Lei, Gang, Guo, Youguang, Zhu, Jianguo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Belt drives; Belt-driven starter/generator (BSG); Design optimization; Electric vehicles; Finite element analysis; Finite element method; Generators; Genetic algorithms; Hybrid electric vehicles; hybrid electric vehicles (HEVs); Mathematical models; multimode optimization; Multiple objective analysis; Optimization; Optimization methods; Permanent magnet motors; Reluctance machinery; Reluctance motors; Sensitivity analysis; switched reluctance motor (SRM); Switches; Torque measurement
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2020.3046721

The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO 2 . Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.