Parallel hybrid electric system energy optimization control with automated mechanical transmission

Abstract

Energy optimization control for a parallel hybrid electric system with automated mechanical transmission (AMT) can be divided into two steps in this paper. First, the AMT shift is not optimized and the optimal torque distribution strategy is proposed to minimize the powertrain equivalent specific fuel consumption by considering the power conversion efficiency, which distributes the vehicle single torque request into separate torque requests for the internal combustion engine (ICE) and the electric motor (EM). The distribution results are expressed in a table format and can be found from the simple process of looking up the table using the vehicle torque request, the ICE speed, and the battery state of charge (SOC). Then, the AMT shift control is suggested to maximize the powertrain system efficiency and optimizes the speed as the basis for the above-mentioned torque distribution, in which the ICE efficiency, the EM efficiency, and the battery efficiency are all explicitly taken into account. The AMT optimal shift control law and the EM optimal torque are essentially look-up-table-based control according to the ICE power, the EM power, the vehicle velocity, and the battery SOC after offline calculations. Simulation results reveal that potential fuel economy improvement has been achieved by using the energy optimization control.

Design and implementation of a real-time power management strategy for a parallel hybrid electric bus

“…22 At every sampling instant, the look-up table interpolations of the engine efficiency and the motor efficiency are executed M 3 N times, where M and N are the number of grid points for the torque slip ratios and the number of grid points for the gears respectively, causing a significant computational burden on the HCU. An online control solution proposed by van Keulen et al 8 and Wang et al, 23 which suggests calculating the ECMS control offline as a function of P dem (t), v(t) and l à (t) on a discrete grid and storing the result in a three-dimensional table, could be a solution for the computational burden. The disadvantages of this approach are that the optimal control derived from the ECMS might be irregular and against good driveability; also, for work points not on grids, interpolation must also be employed to obtain control solutions which might differ from those expected by the ECMS, especially when the calculated control drastically changes between two nearby grids (e.g.…”

Predictive gear shift control for a parallel Hybrid Electric Vehicle

“…A proportional controller as in (25) is used to control λ (k) to ensure that the end constraint on the battery state of energy (16) should be respected when finishing the drive cycle, [1].…”

“…Therefore, an EMS including an optimized gear shift strategy would result in fuel saving benefits. So far, a small number of published papers about designing the EMSs have taken the gear shift strategy into consideration, see [13]- [16]. All the proposed EMSs allowed the vehicle to freely shift to any gear position such that the instantaneous fuel consumption is minimized.…”

An Optimal Control Strategy for Parallel Hybrid Electric Vehicles

“…At each moment of the driving, this function is minimized and an optimal power split between the power sources is calculated. Equivalent Consumption Minimization Strategy (ECMS) is the most popular method of this group [9,10]; (iii) The methods based on global optimizations are of the third type by which the global optimum of the controller is found. These methods can be developed on the basis of di erent global optimization tools, for instance, Dynamic Programming (DP) [11][12][13], Simulated Annealing (SA) [14,15], and Particle Swarm Optimization (PSO) [16,17].…”

“…It is notable that, due to the heavy calculation burden, the latter type cannot be used for realtime power management calculations. This type is only used to evaluate the e ectiveness of the other strategies [9].…”