A novel maximum power point tracking technique with improved particle swarm optimization for proton exchange membrane fuel cell

Abstract

Abstract The performance of proton exchange membrane fuel cell (PEMFC) can be significantly affected by its operating conditions, i.e. the temperature, membrane water content. Aimed at maximizing the performance of PEMFC, maximum power point tracking (MPPT) technology plays an important role in PEMFC system. Most traditional MPPT algorithms will generate steady-state oscillations, which result in power loss and damage to PEMFC. In addition, most MPPT controllers based on intelligent algorithms need to use PID to track the MPP, which increases the complexity of the controller and makes the tracking result strongly depend on the PID gain. To overcome steady-state oscillation and reduce the complexity of the MPPT controller, a MPPT controller based adaptive particle swarm optimization algorithm (APSO) without a PID controller is developed in this paper. The performance of the presented algorithm is investigated under three cases including stable operating condition, temperature change and membrane water content variation, and compared with traditional particle swarm optimization algorithm (PSO) and perturbation and observation (P&O) method. The obtained results indicate that APSO has faster tracking speed and smaller search oscillation than PSO, and has better stability than P&O. Moreover, the results demonstrate that by using duty cycle as decision variable, simple design of MPPT control system can be obtained, which shows great superiority over PID controller. This not only enables real-time online tracking, but also reduces hardware manufacturing costs.