Abstract:
This study presents a hybrid optimization technique to optimize stator and rotor slots
of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic
optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm
optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and
computational burden. Therefore, this study proposes a new hybrid optimization technique called
opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based
learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown
torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and
rotor slots based on the formulated multi-objective. The performance is compared with conventional
optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization
techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore,
the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average
can be achieved compared with JSO. In conclusion, the proposed technique can be used to help
engineers in the automotive industry design a high-performance IM for EVs as an alternative to the
existing motor.
