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Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In this paper, a particle swarm optimization (PSO)-based power dispatch algorithm is proposed to deal with the energy management problem of the hybrid generation system (HGS). For conventional PSO method, the search space is only defined by inequality constraints. However, as for power dispatch problems, it is vital to maintain power balance, which can be represented as an equality constraint. To address this issue, a roulette wheel re-distribution mechanism is proposed. With this re-distribution mechanism, unbalanced power can be reallocated to more superior element and the searching diversity can be preserved. In addition, the effect of depth of discharge on the life cycle of the battery bank is also taken into account by developing a penalty mechanism. The proposed method is then applied to a HGS consisting of photovoltaic array, wind turbine, microturbine, battery banks, utility grid and residential load. To validate the effectiveness and correctness of the proposed method, simulation results for a whole day will also be provided. Comparing with three other power dispatching methods, the proposed method can achieve the lowest accumulated cost. [ABSTRACT FROM AUTHOR]