Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm

Abstract

Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment. OPEN ACCESSEnergies 2015, 8 3662

Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer

“…The power flow calculation is based on the Matpower 6.0 toolbox in MATLAB R2014a. The performance of BFRL for RBED has been evaluated on IEEE RTS-79 system [43] compared with that of other algorithms, e.g., GA [23], QGA [24], ABC [25], PSO [26], BFO [27,28] and Q-learning [40]. For each algorithm, there are both feasible and infeasible solutions to the proposed RBED problem.…”

“…So far, an enormous variety of artificial intelligence (AI) algorithms, including genetic algorithm (GA) [23], quantum genetic algorithm (QGA) [24], artificial bee colony (ABC) [25], particle swarm optimization (PSO) [26] and bacteria foraging optimization (BFO) [27,28] have been successfully applied for an optimal power system operation due to their elegant merits of global convergence, model free feature, and applicability to discrete nonlinear problems. In particular, an optimization task can be tackled by variables, objective functions and the number of unsatisfied constraints.…”

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

“…The above optimization algorithms are used to optimize the parameters of the power system or energy control strategy of HEVs. Several algorithms have been employed to optimize the parameters of both the power system and control strategy, such as the particle swarm optimization (PSO) algorithm [8][9][10] and multi-objective genetic algorithm [11]. A genetic algorithm with simulated annealing is proposed in [12] to balance between economy and dynamic performance.…”

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

“…In contrast, the EV mode runs using the EM when the battery has a higher SOC. This is a basic rule-based power management strategy, which is also called the CD-CS strategy [20,21].…”