Optimal Design and Control of a Two-Speed Planetary Gear Automatic Transmission for Electric Vehicle

Abstract

Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward–feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque.

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

“…A Matlab implementation of the genetic algorithm NSGA-II is used as the optimization algorithm, which can handle discrete and real-valued design variables and consider constraints and multiple objectives. The algorithm is suitable for optimizing various engineering problems ranging from gearboxes [42,43] to wind farms [44,45]. In the MBSE system model, the optimize action exhibits black box behavior, which is resolved with the help of the workflow architecture (Figure 10) and internal behavior (Figures 11 and 12).…”

“…A Matlab implementation the genetic algorithm NSGA-II is used as the optimization algorithm, which can hand discrete and real-valued design variables and consider constraints and multip objectives. The algorithm is suitable for optimizing various engineering problems rangi from gearboxes [42,43] to wind farms [44,45]. The design of the electric coolant pump is based on the procedure in [39].…”

Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

“…Compared with the traditional genetic algorithm, fast non-dominated sorting method, elitist maintenance strategy, and efficient crowding distance estimation procedure are introduced into the NSGA-II. Then it greatly improves iterative convergence rate, reduces computational complexity and ensures population diversity [22]. Figure 8 shows the principle of NSGA-II algorithm, and its basic steps are as follows:…”

Gear Shift Coordinated Control Strategy Based on Motor Rotary Velocity Regulation for a Novel Hybrid Electric Vehicle

“…References [20,21] proposed a new hybrid power system, in which different powertrain components were coordinated to form an independent torque path to fill the torque gap during gear shifting. Reference [22] proposed a transmission that included a two-stage planetary gear set and two brake mechanisms, with an optimal control method used to achieve seamless shifting. Reference [23] proposed an uninterrupted shift transmission, with a structural layout similar to that of an automated manual transmission (AMT) or dual clutch transmission (DCT).…”