This paper proposes a new bidirectional buck-boost converter, which is a key component in the photovoltaic and energy storage system (PV-ESS) for smart grid. The conventional bidirectional buck-boost converters for ESS operate in discontinuous conduction mode (DCM) to achieve zero-voltage-switching turn-on for switches. However, the operation in DCM causes a high output voltage/current ripples and a low power-conversion efficiency. To improve the performance of the conventional converter, the proposed converter has the new combined structure of a cascaded buck-boost converter and an auxiliary capacitor. The combined structure of the proposed converter reduces the output current ripple by providing a current path. Therefore, the output voltage ripple is reduced and the efficiency is increased. To verify the effectiveness of the proposed converter, a prototype was built and tested. The proposed converter with a maximum efficiency of 98% had an output voltage ripple of less than 5.14 V $_{p.p}$ , and an output current ripple of less than 7.12 A $_{p.p}$ at input voltage of 160 V, switching frequency of 45 kHz, output voltage of 80 ~ 320 V, and output power of 16 ~ 160 W. These experimental results showed that the proposed converter improved the performance of the conventional converter.

Accurate battery state-of-charge (SOC) estimation is important for the efficient and reliable operation of battery application systems. The extended Kalman filter (EKF), which is based on the battery model, is widely used as a real-time SOC estimation algorithm; furthermore, its accuracy depends on the model accuracy. However, the conventional EKF uses one value for each battery parameter ( R_i , R_d and C_d ) regardless of the SOC, even though their values change according to the SOC. To address this problem, this study proposes an improved EKF that applies battery parameters that change according to the SOC of a battery model. In the proposed method, the entire SOC was divided into several sections considering the deviation of the parameter values according to the SOC. Subsequently, the average values for each SOC section were calculated, and the values of the battery parameters were updated with the average values according to the SOC. To verify the performance of the proposed EKF, the parameters of commercial Li-ion batteries were extracted with dis-charge currents of 1C- and 2C-rates at ambient temperatures of 0 °C, 25 °C, and 45 °C, and MATLAB simulations were performed. Compared to the conventional EKF, the proposed EKF estimated the SOC more accurately under all the simulation conditions. Compared to the conventional EKF, the maximum reduced root-mean-square error and maximum error of the proposed method were 49.37% and 56.41%, respectively.

The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

This paper proposes a quasi-resonant boost half-bridge DC-DC converter with high power conversion efficiency and a wide input voltage range for photovoltaic micro-inverter. The proposed converter has a combined structure of a boost integrated half-bridge converter on the primary side of the transformer and a voltage doubler with a snubber capacitor on the secondary side. By using the quasi-resonance techniques, it achieves zero-voltage switching (ZVS) turn-on and low turn-off losses for the switches, as well as ZVS turn-on for the diodes. In addition, the proposed converter has no DC-magnetizing current for the transformer due to the DC blocking capacitor, and it doubles the voltage gain with half the voltage stress of diodes through the voltage doubler. The effectiveness of the converter was verified with a 300-W prototype with input voltage range of 13 ~ 50 V, output voltage of 400 V, and switching frequency of 50 kHz. The experimental results show that the converter had more than 90.0% power conversion efficiency for the output power range of ~ 300 W, and its maximum power conversion efficiency was 97.5% for an output power of 160 W.

Accurately estimating the actual capacity of the battery is crucial for stable battery operation and user safety. This paper combined feature extraction through differential thermal voltammetry analysis and long short-term memory for accurate state of health estimation. First, differential thermal voltammetry curves according to battery degradation were extracted for various cathode materials. Then, health indicators are collected from the differential thermal voltammetry curve for deep learning-based state of health estimation. In particular, to improve the state of health estimation performance, the integral value of the differential thermal voltammetry curve through the specific voltage ranges was additionally introduced along with the peak and valley. Second, the correlation between the extracted health indicators and capacity was analyzed using Pearson correlation analysis. Finally, a framework was developed to estimate the state of health of the battery using high-quality health indicators as inputs to the long short-term memory model. The state of health estimation performance of the proposed algorithm, which reflected the integral value of the differential thermal voltammetry curve, was compared with the case that did not reflect. From the result, the mean absolute error decreased by 11.6% and the root mean square error by 10.01% for the two battery data sets.

This paper presents a highly efficient three-port converter to integrate energy storage (ES) and wireless power transfer (WPT) systems. The proposed converter consists of a bidirectional DC-DC converter and an AC-DC converter with a resonant capacitor. By sharing an inductor and four switches in the bidirectional DC-DC converter, the bidirectional DC-DC converter operates as a DC-DC converter for ES systems and simultaneously as a DC-AC converter for WPT systems. Here, four switches are turned on under the zero voltage switching conditions. The AC-DC converter for WPT system achieves high voltage gain by using a resonance between the resonant capacitor and the leakage inductance of a receiving coil. A 100-W prototype was built and tested to verify the effectiveness of the converter; it had a maximum power-conversion efficiency of 95.9% for the battery load and of 93.8% for the wireless charging load.