Estimating the parameters of lithium-ion (Li-ion) batteries under dynamic working conditions is a critical challenge in the health management of electrical energy storage systems. This paper estimates the equivalent circuit model (ECM) parameters and analyzes the influence of different factors on the Li-ion batteries impedance using the electrochemical impedance spectroscopy (EIS) technique. Firstly, the influence of the temperature, state of charge (SOC) and number of charging/discharging cycles on the impedance spectrum was studied. Nyquist plots were used to perform this analysis. Subsequently, ECMs were used to characterize the Li-ion batteries under study at different operating conditions. Specifically, the ECM parameters were estimated using the gaining-sharing knowledge (GSK) metaheuristic algorithm, allowing the consequent estimation of the impedance spectra obtained by the EIS technique. Experimental results included 15 datasets measured experimentally at three temperature levels for five SOCs and different number of charging/discharging cycles. To investigate the variation of Li-ion battery parameters with cyclic aging 350 datasets from the literature were also used. The results showed that the impedance and model parameters depend considerably on the considered factors. The estimation of the ECM parameters demonstrated high accuracy and reliability (mean RMSE values of 4.32E-02 and 2.33E-02 and mean STD values of 3.38E-16 and 5.82E-13 with measured and literature datasets, respectively), allowing the robust determination of the numerous impedance spectra under study.

Among the wide diversity of existing technologically mature batteries, lithium-ion (Li-ion) batteries have become popular because of their longevity, high energy density, high efficiency and lack of memory effect. Differential charging of cells with age has turned balancing management systems into an important research subject. This paper proposes a new battery management system (BMS) to improve the capacity usage and lifespan of large Li-ion battery packs and a new charging algorithm based on the traditional multistage method. The main advantages of the proposed system are its versatility and ability to implement different charging and balancing methods in a very accessible way. The combination of charging methods with balancing methods represents an evolution when compared with other works in the literature.

This paper proposes a new method for the simultaneous determination of the optimal control parameters of proportional resonant controllers and the optimal design of the output filter of a grid-tied three-phase inverter. The proposed method, based on the grey wolf optimization (GWO) algorithm, addresses both optimization problems as a single process to achieve a better system frequency response. It optimizes the unknown parameters by using a fitness function to find the best trade-off between the following fundamental terms: the harmonic attenuation rate; the power loss, through the damping resistor; and the current tracking error in the stationary frame, ensuring the system and grid stability. To validate the proposed optimization methodology, two case studies are considered with different output filter topologies with passive damping methods. The results obtained from the proposed optimization procedure were analyzed and discussed according to the fitness function terms.

This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%). Thus, the results show that the proposed algorithm based on AI is able to effectively charge and balance batteries and can be regarded as a subject of interest for future research.

This paper proposes a comprehensive modeling and parameters extraction method of solar photovoltaic module based on the Particle Swarm Optimization algorithm. The characteristic curves of photovoltaic panel are obtained by using only the information provided by the manufacturer data-sheet, avoiding the need to carry out experimental data. The performance and the accuracy of the proposed method are evaluated by applying the one-diode model and the results are compared with those obtained by the well-known Lambert W function. The proposed method shows a higher performance.