This paper proposes a fast and efficient maximum power tracking approach, which synthesizes simplified model-based state estimation (SMSE) with adaptive alpha perturb-and-observe (α-P&O) method, to further raise the capability and efficacy of maximum power point (MPP) tracking (MPPT). Only any two sampling points, in the first stage, on the operating characterization curve are needed to estimate the irradiance and temperature based on SMSE. In the second phase, the operation point is moved directly to the operating voltage command (OVC) derived from the fitting relationship between the voltages at MPP under various insolation intensities, and then the α-P&O method takes over the next tracking to pinpoint the exact MPP. Performances are demonstrated through simulations and experiments. Experimental results reveal that tracking accuracies exceed 99.2 % in all test scenarios. Comparing with conventional and variable step-size P&O methods, the tracking time is shortened by 84.6 % and 76.0 %, improvement of 1.26 % and 0.03 % in tracking accuracy can also be achieved, and the tracking energy loss is reduced by 68.0 % and 52.7 %, respectively.

The pulse width modulation (PWM) dimming mode features good dimming linearity and has been widely used for driving high-brightness light-emitting diodes (HBLEDs), in which the brightness change is reached by modulating the duty cycle of the dimming signal to regulate the average current flowing through LEDs. However, the current-illuminance characteristic curve of most LEDs is nonlinear in nature. Namely, under the same lighting power fed, the conventional PWM dimming cannot make the LED exert its best luminous efficiency (LE) specified in datasheets. This paper focuses on the study of further improving LED luminous efficacy via dimming manipulation. Thereby, two multilevel current dimming techniques with varied dimming signal voltage and varied current sensing resistance are presented. With limited dimming capability, the proposed dimming strategies can efficiently raise the luminous flux ratio without increasing the power consumption. A prototype constructed for a 115 W HBLED driver is developed and the devised dimming schemes are realized by a digital signal controller (DSC). Experimental results exhibited with illuminance-power curves and CIE1931 and CIE1976 chromaticity diagrams are given to validate the theoretical derivation and effectiveness. Compared with conventional PWM dimming, under the same illuminance, the driver average output power is respectively reduced by 17.08% and 13.17%; the improvement in average illuminance under the same output power is 13.66% and 11.17%, respectively. In addition, the entire average LE boost has respectively increased by 21.36% and 16.37%.

This study proposes a deep learning (DL)-based method for identifying the parameters of equivalent circuit models (ECMs) for lithium-ion batteries using time-series voltage response data from current pulse charge–discharge experiments. The application of DL techniques to this task is presented for the first time. The best-performing baseline model among the recurrent neural network, long short-term memory, and gated recurrent unit achieved a mean absolute percentage error (MAPE) of 0.52073 across the five parameters. Furthermore, more advanced models, including a one-dimensional convolutional neural network (1DCNN) and temporal convolutional networks, were developed using full factorial design (FFD), resulting in substantial MAPE improvements of 37.8% and 30.4%, respectively. The effectiveness of Latin hypercube sampling (LHS) for training data generation was also investigated, showing that it achieved comparable or better performance than FFD with only two-thirds of the training samples. Specifically, the 1DCNN model with LHS sampling achieved the best overall performance, with an average MAPE of 0.237409. These results highlight the potential of DL models combined with efficient sampling strategies.

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.

In this paper, a fast equalizer for series-connected battery packs with adaptive balancing current control is proposed. As the duty cycle of the power switch in conventional equalizers is kept constant during the equalization process, smaller voltage difference between cells will decrease balancing current and consequently result in extended balancing time, especially in the later phase of equalization. To deal with this problem and take the battery nonlinearity and circuit parameter non-ideality into consideration, an adaptive balancing current control based on a fuzzy logic inference is proposed. The presented approach can adjust the duty ratio adaptively, according to voltages of individual cells and pack, to keep the balancing current nearly constant: Therefore, the balancing time can be shortened and the balancing efficiency can be improved. Finally, experimental results of three compared methods will be given and discussed to validate the feasibility, effectiveness, and performance improvement of the studied method.