A Code Inversion Encoding Technique to Improve Read Margin of A Cross-Point Phase Change Memory

In this paper, we propose a code inversion encoding technique to improve the read margin of a cross-point phase change memory (PCM). The proposed technique reduces the maximum number of low resistance state cells which significantly reduce read margin by increasing sneak current. Therefore, the proposed scheme can significantly improve the read margin of the cross-point PCM. To verify the improvement of read margin by the proposed technique, we simulated and compared read margins of various arrays with and without the proposed technique. According to the simulation, our technique improves the read margin by 102% or equivalently allows to increase the array size by 91.6% without decreasing for the read margin. The results show that the proposed technique greatly improves the read margin.11Nsciescopu

Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles

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

Design of Effective Surge Protection Circuits for an Active EMI Filter

An effective design method of surge protection circuits for an active EMI filter (AEF) is proposed. The overcurrent and overvoltage at the voltage-sense voltage-compensate type AEF due to a surge is analyzed, and protection circuits using the transient voltage suppression (TVS) diodes are designed. Effects of protection circuits are validated by a 2kV surge test. Performances of the AEF for the noise attenuation are also measured by a vector network analyzer (VNA), and the AEF performances after employing the protection circuits are compared

F-DCS: FMI-Based Distributed CPS Simulation Framework with a Redundancy Reduction Algorithm

A cyber physical system (CPS) is a distributed control system in which the cyber part and physical part are tightly interconnected. A representative CPS is an electric vehicle (EV) composed of a complex system and information and communication technology (ICT), preliminary verified through simulations for performance prediction and a quantitative analysis is essential because an EV comprises a complex CPS. This paper proposes an FMI-based distributed CPS simulation framework (F-DCS) adopting a redundancy reduction algorithm (RRA) for the validation of EV simulation. Furthermore, the proposed algorithm was enhanced to ensure an efficient simulation time and accuracy by predicting and reducing repetition patterns involved during the simulation progress through advances in the distributed CPS simulation. The proposed RRA improves the simulation speed and efficiency by avoiding the repeated portions of a given driving cycle while still maintaining accuracy. To evaluate the performance of the proposed F-DCS, an EV model was simulated by adopting the RRA. The results confirm that the F-DCS with RRA efficiently reduced the simulation time (over 30%) while maintaining a conventional accuracy. Furthermore, the proposed F-DCS was applied to the RRA, which provided results reflecting real-time sensor information

A Search Algorithm for the Worst Operation Scenario of a Cross-Point Phase-Change Memory Utilizing Particle Swarm Optimization

In this paper, we propose a search algorithm to find the worst operation scenario of a cross-point array of a phase-change random access memory to enable a precise read margin evaluation. The search algorithm utilizes a particle swarm optimization method to find the worst scenario quickly and efficiently. In an experiment, the proposed algorithm improves the search speed by 39.3x compared with the previous algorithm. With the improved search speed, the proposed algorithm could find the worst operation scenarios of large arrays whose worst operation scenarios had been only guessed before. In the experiment with a large array, the proposed algorithm proved that the worst high-resistance state read current can be 36x larger than the previous best guess. In the reliability test, the evaluation error of the worst read current found by the proposed algorithm is less than 0.2% with 99% probability. These results show that the proposed search algorithm can improve the precision and efficiency of the read margin evaluation in designing a cross-point phase-change memory array.11Nsciescopu

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

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

MIMIC Methods for Detecting DIF Among Multiple Groups: Exploring a New Sequential-Free Baseline Procedure

A simulation study was conducted to investigate the efficacy of multiple indicators multiple causes (MIMIC) methods for multi-group uniform and non-uniform differential item functioning (DIF) detection. DIF was simulated to originate from one or more sources involving combinations of two background variables, gender and ethnicity. Three implementations of MIMIC DIF methods were compared: constrained baseline, free baseline, and a new sequential-free baseline. When the MIMIC assumption of equal factor variance across comparison groups was satisfied, the sequential-free baseline method provided excellent Type I error and power, with results similar to an idealized free baseline method that used a designated DIF-free anchor, and results much better than a constrained baseline method, which used all items other than the studied item as an anchor. However, when the equal factor variance assumption was violated, all methods showed inflated Type I error. Finally, despite the efficacy of the two free baseline methods for detecting DIF, identifying the source(s) of DIF was problematic, especially when background variables interacted