An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance.

With the rising demand for higher quantities and quality of semiconductors, refining production processes is essential. Plasma etching, a key step in semiconductor manufacturing, is valued for its precision and speed but is also vulnerable to plasma arcing - an electrical discharge that can cause significant damage to wafers and equipment. Rapid and reliable detection of arcing incidents is critical for maintaining yield and minimizing downtime. Traditional detection methods, such as static thresholds applied to Radio Frequency signals, often struggle with non-linear signal characteristics and ambiguous edge cases, resulting in false positives or missed events. To address these challenges, this paper introduces a novel, real-time, configurable statistical anomaly detection framework specifically designed for arcing incidents in plasma etching. The system combines dynamic feature monitoring with a multi-feature validation approach, reducing false positives and improving detection accuracy. In addition, a proof-of-concept severity scoring system is introduced to provide tunable, heuristic-based scoring of detected events, enabling engineers to assess potential impact. Evaluated under log-level labeling constraints using proprietary etching logs, the proposed framework exhibits promising detection performance relative to naive threshold-based approaches and representative machine learning baselines evaluated for comparison, including LSTM, one-class SVM, and Isolation Forest, while maintaining compatibility with real-time process constraints. The system’s modular design further allows for potential adaptation across different etching configurations, offering a practical and efficient proof-of-concept framework for real-time process monitoring in semiconductor manufacturing.