Multi-Speed Transmission Optimization of Electric Vehicles Based on Shifting Pattern Considering Dynamic Inertia Efficiency

Multi-speed transmissions for electric vehicles (EVs) can achieve superior economic and dynamic performances than single-speed transmissions. Since gear shifting causes an equivalent inertia variation in multi-speed transmissions, the optimal shifting pattern should be determined by considering the inertia variation effect to maximize EV performances. To consider the dynamic inertia variation effect owing to gear shifting, the equivalent inertia for each speed gear and dynamic inertia efficiency are mathematically derived. An EV analysis model is constructed to evaluate the EV performances, and energy efficiency and acceleration ability are adopted as quantification measures for economic and dynamic performances, respectively. The result comparison of the optimal shifting patterns when considering and not considering the dynamic inertia efficiency exhibits the importance of the optimal shifting pattern considering the dynamic inertia efficiency for the superior transmission design of EVs. A multi-objective optimization problem is formulated that includes the design variables as gear ratios and shifting patterns and the objective functions as energy efficiency and acceleration ability. As an alternative to the excessive calculation burden of conducting multi-objective optimization, an artificial neural network (ANN)-based multi-objective optimization process is utilized. To verify the importance of the dynamic inertia efficiency on economic and dynamic performances, the gear ratios and shifting patterns are optimized by considering the dynamic inertia efficiency and none. The different optimum solutions and objective function values demonstrate the necessity of considering the dynamic inertia efficiency owing to gear shifting; the economic and dynamic performances are improved from 2.7% to 7.8% and 2.8% to 3.0%, respectively.