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%.

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

The battery storage system (BSS) is one of the key components in many modern power applications, such as in renewable energy systems and electric vehicles. However, charge imbalance among batteries is very common in BSSs, which may impair the power efficiency, reliability, and safety. Hence, various battery equalization methods have been proposed in the literature. Among these techniques, switched-capacitor (SC)-based battery equalizers (BEs) have attracted much attention due to their low cost, small size, and controllability. In this paper, seven types of SC-based BEs are studied, including conventional, double-tiered, modularized, chain structure types I and II, series-parallel, and single SC-based BEs. Mathematical models that describe the charge–discharge behaviors are first derived. Next, a statistical analysis based on MATLAB simulation is carried out to compare the performance of these seven BEs. Finally, a summary of the circuit design complexity, balancing speed, and practical implementation options for these seven topologies is provided.