This paper proposes a searching strategy based on particle swarm optimization, combined with a fuzzy-deduced fitness evaluator (FDFE), to find the optimal multistage charging pattern that delivers the most discharged capacity within the shortest charging time (CT). The objective function of the optimization problem is to maximize the cost effectiveness for the applied charging pattern based on the CT and normalized discharged capacity (NDC). Therefore, this paper proposes an FDFE to combine CT and NDC into a unified cost function to properly evaluate the multiple performance characteristics index in the charge problem. The experimental results show that the obtained pattern is capable of charging the batteries to over 88% capacity within 51 min. Compared with the conventional constant current-constant voltage method, the CT, the obtained life cycle, and the charging efficiency of the lithium-ion (Li-ion) battery for the devised approach are improved by approximately 56.8%, 21%, and 0.4%, respectively. The presented charging approach is suitable for the increasingly applications, in which the batteries are “sealed” inside the products to extend the life span.

In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.