Using the Pareto Set Pursuing Multiobjective Optimization Approach for Hybridization of a Plug-In Hybrid Electric Vehicle

Abstract

A plug-in hybrid electric vehicle (PHEV) can improve fuel economy and emission reduction significantly compared to hybrid electric vehicles and conventional internal combustion engine (ICE) vehicles. Currently there lacks an efficient and effective approach to identify the optimal combination of the battery pack size, electric motor, and engine for PHEVs in the presence of multiple design objectives such as fuel economy, operating cost, and emission. This work proposes a design approach for optimal PHEV hybridization. Through integrating the Pareto set pursuing (PSP) multiobjective optimization algorithm and powertrain system analysis toolkit (PSAT) simulator on a Toyota Prius PHEV platform, 4480 possible combinations of design parameters (20 batteries, 14 motors, and 16 engines) were explored for PHEV20 and PHEV40 powertrain configurations. The proposed approach yielded the optimal solution in a small fraction of computational time, as compared to an exhaustive search. This confirms the efficiency and applicability of PSP to problems with discrete variables. In the design context we have found that battery, motor, and engine collectively define the optimal hybridization scheme, which also varies with the drive cycle and all electric range (AER). The proposed method and software platform could be applied to optimize other powertrain designs.

Multi-Objective Optimization Study of Regenerative Braking Control Strategy for Range-Extended Electric Vehicle

“…The optimization calculation results are shown in Figure 11 and Table 3. (21) where xn (k) is the optimal solution set in the k-th generation; X (k) is the averaging value of optimal solution set in the k-th generation; yn (k) is the evaluation indicator in the k-th generation; and Y (k) is the averaging value of evaluation indicator in the k-th generation.…”

“…Obviously, the essential MOO problem is to solve the mapping relationship of the function. The mapping relationship between the decision space and objective space belongs to a complex many-to-many relationship [21,22]. As we know, the solution set of the multi-objective problem belongs to vector optimization in nature.…”

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

“…It is also capable of posing the significant robustness in the presence of any type of uncertainties like change in dynamics of plant and nonavailability of vehicle load torque. Shahi et al [171] proposed an approach for optimal PHEV hybridization using Pareto set pursuing (PSP) multiobjective optimization algorithm. The main feature of this algorithm is that it uses very less time (17 days) compared to exhaustive search approach (560 days) for PHEV 20 on urban dynamometer driving schedule (UDDS).…”

Design of a lithium-ion battery pack for PHEV using a hybrid optimization method

“…The degree of hybridization in the PHEV, which is the ratio of electric motor power to the total drivetrain power, is shown to affect the optimality of the drivetrain components performance [51]. Therefore, the overall hybridization scheme of a PHEV is defined by the battery, electric motor and internal combustion engine collectively [14]. Inclusion of various components also necessitates the coupling of power control strategies with design parameters to determine the optimal performance.…”

“…Shahi et al [14] applied a multi-objective optimization approach for the hybridization of a PHEV subject to Urban Dynamometer Driving Schedule (UDDS) and Winnipeg Weekday Duty Cycle (WWDC) drive cycle requirements. Wu et al [15] described a methodology to minimize the drivetrain cost of a parallel PHEV by optimizing its component sizes.…”