The objective of this paper is to propose an active ripple filter (ARF) using the patented DC-side direct current control for eliminating the double-line-frequency current ripple in a single-phase DC/AC conversion system. The proposed ARF and its control strategies can not only prolong the usage life of the DC energy source but also improve the DC/AC system performance. At first, the phenomena of double-line-frequency current ripple and the operation principle of the ARF are illustrated. Then, steady-state analysis, small-signal model, and control loop design of the ARF architecture are derived. The proposed control system includes: (1) a DC current control loop to provide the excellent ripple eliminating performance on the output of the DC energy source; (2) a voltage control loop for the high-side DC-bus voltage of the ARF to achieve good steady-state and transient-state responses; (3) a voltage feedforward loop for the low-side voltage of the ARF to cancel the voltage fluctuation caused by the instability of the DC energy source. Finally, the feasibility of the proposed concept can be verified by the system simulation, and the experimental results show that the nearly zero double-line-frequency current ripple on the DC-side in a single-phase DC/AC conversion system can be achieved.

A deep neural network model for the investigation of the discharging voltage, temperature, cycle, and state of charge-dependent behavior of gel batteries is presented in this paper. The proposed model utilizes a long short-term memory recurrent neural network to investigate the estimation of the state of charge and the state of health. The model could serve as a management strategy for the Vehicle Service Management Centers to reduce maintenance costs by regulating the battery status, managing the driving paths, adjusting the driving areas, and replacing the batteries in batches. The management strategy utilizes the the state of charge and the state of health of gel batteries as key variables in assigning driving paths and areas to golf carts. In the actual on-road experiment, the model successfully tested and verified the state of charge of the gel batteries in the golf cart with a mean absolute error of 0.376% which was better than the feedforward neural network of 2.8% and the recurrent neural network of 1.7%. These results indicate that the model can accurately estimate the state of charge and the remaining service life of batteries which provide clear indicators for the vehicle service management systems.

Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly.

In this paper, a comprehensive model for LiFePO₄ batteries is proposed to ensure high efficiency and safe operation. The proposed model has a direct correlation between its parameters and the electrochemical principles to estimate the state of charge (SoC) and the remaining capacity of the LiFePO₄ battery. This model was based on a modified Thévenin circuit, Butler–Volmer kinetics, the Arrhenius equation, Peukert’s law, and a back propagation neural network (BPNN), which can be divided into two parts. The first part can be represented by the dual exponential terms, responsive to the Coulomb efficiency; the second part can be described by the BPNN, estimating the remaining capacity. The model successfully estimates the SoC of the batteries that were tested with an error of 1.55%. The results suggest that the model is able to accurately estimate the SoC and the remaining capacity in various environments (discharging C rates and temperatures).

The impact of different charging currents and surrounding temperatures has always been an important aspect of battery lifetime for various electric vehicles and energy storage equipment. This paper proposes a bidirectional long short-term memory model to quantify these impacts on the aging of gel batteries and calculate their state of health. The training data set of the bidirectional long short-term memory model is collected by charging and discharging the gel battery for 300 cycles in a temperature-controlled box and an automated charge and discharge device under different operating conditions. The testing set is generated by a small energy storage device equipped with small solar panels. Data for 220 cycles at different temperatures and charging currents were collected during the experiment. The results show that the mean absolute error (MAE) and root-mean-square error (RMSE) between the training set and testing set are 0.0133 and 0.0251, respectively. In addition to the proposed model providing high accuracy, the gel battery proved to be stable and long-lasting, which makes the gel battery an ideal energy storage solution for renewable energy.

This study investigates the application of a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) hybrid model for typhoon-induced storm surge forecasting, examining the effects of including or excluding time-lagged inputs (specifically 3-hour lag, t-3, and 6-hour lag, t-6) on prediction performance. By combining the strength of CNNs in extracting local features with the ability of LSTMs to capture long-term temporal dependencies, this hybrid approach is particularly well-suited for extending forecast lead times and improving prediction accuracy in storm surge applications. Moreover, CNNs can effectively reduce the number of model parameters through weight sharing in convolutional layers. Compared to fully connected neural networks (FCNs), this design helps lower the risk of overfitting and enhances training efficiency. The Longdong tidal station on the northeastern coast of Taiwan was selected as the study area, using 10 historical typhoon events from 2005 to 2024 for model training and validation. The results show that under a one-hour lead time forecast, the 1D CNN-LSTM model achieved a correlation coefficient of 0.98 with observed data, highlighting its strong potential for short-term prediction. Even for longer lead times (24-hour ahead prediction), the model maintained a correlation coefficient above 0.90, demonstrating significant improvements over traditional shallow static networks (BPNN) and standalone dynamic networks (LSTM). Overall, this hybrid model is promising for early warning and emergency response planning for typhoon-induced storm surges.