Set Of Signals Research Articles

UWB-Modulated Microwave Imaging for Human Brain Functional Monitoring.

Morphological microwave imaging has shown interesting results on reconstructing biological objects inside the human body, and these parameters represent their actual biological condition, but not their biological activity. In this paper, we propose a novel microwave technique to locate the low-frequency (f≃1 kHz) -modulated signals produced by a microtag mimicking an action potential and proved it in a cylindrical phantom of the brain region. A set of two combined UWB microwave applicators, operating in the 0.5 to 2.5 GHz frequency band and producing a nsec interrogation pulse, is able to focus its radiated field into a small region of the brain containing the microtag with a modulated photodiode. The illuminating UWB microwave field was first modulated by the low-frequency (f≃1 kHz) electrical signal produced by the photodiode, inducing modulated microwave currents into the microtag that reradiating back towards the focusing applicators. At the receiving end, the low-frequency (f≃1 kHz) -modulated signal was first extracted from the full set of the backscattered signals, then focused into the region of interest and spatially represented in the corresponding region of the brain, resulting in a spatial resolution of the images in the order of 10 mm.

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.

Biobjective robust Network-wide traffic signal optimization against Cyber-attacks

In the cyber age, modern traffic signal control systems are inevitably exposed to a wide range of cyber-attacks, which would negatively impact traffic system performance. To defend against direct cyber-attacks that alter signal settings directly and maliciously by hacking into the wireless connected traffic control center or signal controllers, this study applies a precautionary and inexpensive measure of designing the robust signal control plan. This is realized by modelling the biobjective traffic signal control simulation-based optimization (SO) problem under cyber-attacks in a bi-level framework, with the aim of minimizing the negative impact of potential cyber-attacks on traffic efficiency and safety performance. Depending on the type of attacker with complete or incomplete knowledge on traffic signal control system, two typical bi-level models exist. Then, we propose an improved biobjective robust simulation-based optimization (IBORSO) algorithm to solve the two models, during which the sequential interaction between the attacker and the defender is seen as the Stackelberg game, and the resulting equilibrium solutions are the desired robust signal control plans to withstand cyber-attacks. An urban road network in Changsha, China, is modelled as the experimental site and numerical results show the effectiveness of IBORSO in solving the two bi-level models. Meanwhile, the solved robust signal control plans can defend against two typical attacks satisfactorily and achieve better or comparable biobjective performance compared with the field-implemented one and optimized ones by biobjective efficient global optimization (BOEGO).

Sky-averaged 21-cm signal extraction using multiple antennas with an SVD framework: the REACH case

ABSTRACT In a sky-averaged 21-cm signal experiment, the uncertainty of the extracted signal depends mainly on the covariance between the foreground and 21-cm signal models. In this paper, we construct these models using the modes of variation obtained from the singular value decomposition of a set of simulated foreground and 21-cm signals. We present a strategy to reduce this overlap between the 21-cm and foreground modes by simultaneously fitting the spectra from multiple different antennas, which can be used in combination with the method of utilizing the time dependence of foregrounds while fitting multiple drift scan spectra. To demonstrate this idea, we consider two different foreground models (i) a simple foreground model, where we assume a constant spectral index over the sky, and (ii) a more realistic foreground model, with a spatial variation of the spectral index. For the simple foreground model, with just a single antenna design, we are able to extract the signal with good accuracy if we simultaneously fit the data from multiple time slices. The 21-cm signal extraction is further improved when we simultaneously fit the data from different antennas as well. This improvement becomes more pronounced while using the more realistic mock observations generated from the detailed foreground model. We find that even if we fit multiple time slices, the recovered signal is biased and inaccurate for a single antenna. However, simultaneously fitting the data from different antennas reduces the bias and the uncertainty by a factor of 2–3 on the extracted 21-cm signal.

Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package

The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately.

A Universal Interfacial Strategy Enabling Ultra‐Robust Gel Hybrids for Extreme Epidermal Bio‐Monitoring

AbstractA seamless and tough interface to integrate incompatible/immiscible soft materials is highly desired for flexible/wearable electronics and many soft devices with multi‐layer structures. Here, a surfactant‐mediated interfacial chemistry is introduced to achieve seamless and tough interfaces in soft multi‐layer structures, with an ultra‐high interfacial toughness up to ≈1300 J m−2 for the architectural gel hybrid (AGH). The reversible noncovalent interfacial interactions efficiently dissipate energy at the interface, thereby providing excellent durability. The interfacial toughness only decreases by ≈6.9% after 10 000 tensile cycles. This strategy can be universally applied to hybrid systems with various interfaces between an interior hydrogel (PAA, PVA, PAAm, and gelatin) and an exterior hydrophobic soft matter (ionogel, lipogel and elastomer). The AGH‐based mechano‐sensor presents high robustness and stability in a wide range of conditions, including open air, underwater, and various solvents and temperatures. Epidermal bio‐monitoring, tactile trajectory, and facial expression recognition are demonstrated using the AGH sensors in various environments. A rich set of electrophysiological signals of high quality are acquired.

EEG-Based Emotional Video Classification via Learning Connectivity Structure

Electroencephalography (EEG) is a useful way to implicitly monitor the users perceptual state during multimedia consumption. One of the primary challenges for the practical use of EEG-based monitoring is to achieve a satisfactory level of accuracy in EEG classification. Connectivity between different brain regions is an important property for the classification of EEG. However, how to define the connectivity structure for a given task is still an open problem, because there is no ground truth about how the connectivity structure should be in order to maximize the classification performance. In this paper, we propose an end-to-end neural network model for EEG-based emotional video classification, which can extract an appropriate multi-layer graph structure and signal features directly from a set of raw EEG signals and perform classification using them. Experimental results demonstrate that our method yields improved performance in comparison to the existing approaches where manually defined connectivity structures and signal features are used. Furthermore, we show that the graph structure extraction process is reliable in terms of consistency, and the learned graph structures make much sense in the viewpoint of emotional perception occurring in the brain.

A Family of Bidirectional Series Resonant Converter With Sine-Wave Modulation in Wide Voltage Range

A topology family of bidirectional series resonant converter with sine-wave modulation (SWM) is proposed in this article. The single-stage power transmission, zero voltage switching (ZVS) operation, and wide voltage regulation capacity can be realized of the proposed converters. On the basis of the traditional bidirectional series resonant converter, the proposed converters are generated by reconstructing the bridge arm without adding auxiliary switches or magnetic components. After reconstruction, the proposed converters combine the traditional pulsewidth modulation (PWM) circuit and the resonant circuit. Besides, a coupled inductor is used to replace the filter inductor, resonant inductor, and transformer, which reduces the magnetic components. In addition, under the proposed SWM, the voltage is adjusted by the duty cycle with fixed frequency, and the voltage gain is independent of the load. The driving logic of the primary and secondary side are completely consistent. Especially in the half bridge structures, only one set of complementary PWM signals are required. Furthermore, in wide voltage range, the resonant current is always a sine-wave, which can simplify the voltage gain and ZVS analysis. Finally, one of the proposed topologies is analyzed in detail, and a 500 W, 250 kHz experimental prototype with 100–200 V input and 150 V output was built. The experimental results proved the effectiveness and feasibility.

Parkinson’s Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps

Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.

A CNN-Based E-Nose Using Time Series Features for Food Freshness Classification

In this article, a convolutional neural network (CNN)-based electronic nose system is proposed, which utilizes time series features to classify the freshness of food. The electronic nose is composed of three elements: a highly sensitive miniaturized micro-electromechanical systems (MEMS) metal–oxide (MOX)–semiconductor gas sensor array, a complementary metal–oxide–semiconductor (CMOS) integrated circuit, and a CNN-based classification unit. The gas sensors exhibit exceptional sensitivity, low power consumption, and a small size. The interface circuit for sensor readout is fabricated using a standard 180-nm CMOS process, with an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times1.5$ </tex-math></inline-formula> mm. The interface chip mainly consists of an analog-to-digital converter (ADC), an electrically erasable programmable read-only memory (EEPROM), an oscillator (OSC), power management, a heater driver, and a digital control circuit. Fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}{(}{t}{)}$ </tex-math></inline-formula> exposures are taken at specific intervals under gas input and output conditions. Time series features are extracted from a diverse set of sensor signals, which allows the subtle differences in various food odors at different freshness levels to be identified. The incorporation of time series feature extraction extends the data features, resulting in enhanced classification accuracy with a limited number of sensors. An abstract odor map input to the CNN is formed by combining the steady-state and transient information of the gas sensor response. The final freshness classification accuracy of 20 types of foods is 97.3%. The accuracy is improved by 6.5% after the implementation of time series feature extraction, demonstrating the significance of instantaneous fluctuation information for classification accuracy.

Nanopore-Based Fingerprint Immunoassay Based on Rolling Circle Amplification and DNA Fragmentation.

In recent years, nanopore-based sequencers have become robust tools with unique advantages for genomics applications. However, progress toward applying nanopores as highly sensitive, quantitative diagnostic tools has been impeded by several challenges. One major limitation is the insufficient sensitivity of nanopores in detecting disease biomarkers, which are typically present at pM or lower concentrations in biological fluids, while a second limitation is the general absence of unique nanopore signals for different analytes. To bridge this gap, we have developed a strategy for nanopore-based biomarker detection that utilizes immunocapture, isothermal rolling circle amplification, and sequence-specific fragmentation of the product to release multiple DNA reporter molecules for nanopore detection. These DNA fragment reporters produce sets of nanopore signals that form distinctive fingerprints, or clusters. This fingerprint signature therefore allows the identification and quantification of biomarker analytes. As a proof of concept, we quantify human epididymis protein 4 (HE4) at low pM levels in a few hours. Future improvement of this method by integration with a nanopore array and microfluidics-based chemistry can further reduce the limit of detection, allow multiplexed biomarker detection, and further reduce the footprint and cost of existing laboratory and point-of-care devices.

Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by machine learning

This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use a coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than previous model-independent searches. The sensitivity improvements are achieved across the broad spectrum of simulated signals, with the goal of testing the robustness of this model-agnostic search. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for the single-cycle signal morphologies. This ML-enhanced pipeline also improves the detection efficiency of compact binary mergers in a wide range of masses, from stellar mass to intermediate-mass black holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the twofold Hanford-Livingston and the threefold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from short-duration burst sources.

Multiobjective Optimization of Networked Switched Systems Subject to DoS Attack Using Artificial Bee Colony Algorithm

In this article, the multiobjective optimization problem of networked switched systems under denial-of-service (DoS) attack is studied. DoS attacks launched by network layer attackers will affect the performance of switched systems in the physical layer, so it is necessary to deploy a defender to deal with it. Taking account of the properties of each switched subsystem and the characteristics of the attacker group composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> DoS attackers, cost functions of the defender and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> attackers are first constructed. Next, the Stackelberg game is used to analyze the game between the defender and attacker group, and the strategy sets of both sides are given by Stackelberg Equilibrium. In addition, for switched systems of the physical layer, the switching times and performance index are modeled as two objectives to be optimized. Based on the equilibrium condition of the network layer, the control input of switched systems is designed. Then, an encoding and decoding strategy of the switching signal is proposed to ensure that the multiobjective artificial bee colony (MOABC) algorithm can be applied to the multiobjective optimization problem of switched systems. Furthermore, the Pareto optimal solution set of switching signals is obtained by using the MOABC algorithm. Finally, the effectiveness of the strategy proposed in this article is verified through the networked continuous stirred tank reactor and a numerical simulation.

Incorporation of market signals for the optimal design of post combustion carbon capture systems

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner’s NGCC configuration as representative of existing NGCC units and Svante’s rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. The results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process.

Tunable Microwave Frequency Comb Generation Based on Actively Mode-Locked OEO

A novel photonics-assisted method for tunable microwave frequency comb (MFC) generation based on an actively mode-locked optoelectronic oscillator (AML-OEO) is proposed and experimentally demonstrated in this letter. Two key factors for the proposed system, periodic gain modulation and widely tunable center frequency, are implemented by an externally introduced intensity-modulated optical signal and a microwave photonic filter (MPF) based on the phase-shifted fiber Bragg grating (PS-FBG) respectively. Unlike a conventional OEO with only one dominant oscillation mode, an AML-OEO operates in a stable multimode oscillation state and thus can generate MFC signal. Through simply adjusting the center frequency of the PS-FBG-based MPF, the center frequency of the generated MFC changes accordingly. In the proof-of-principle experiment, two sets of MFC signals with center frequency tuning from 3.5 GHz to 6.5 GHz and frequency intervals of 1.02 MHz and 340.46 kHz are generated.

Immunogenic Cell Death in Hematological Malignancy Therapy.

Although the curative effect of hematological malignancies has been improved in recent years, relapse or drug resistance of hematological malignancies will eventually recur. Furthermore, the microenvironment disorder is an important mechanism in the pathogenesis of hematological malignancies. Immunogenic cell death (ICD) is a unique mechanism of regulated cell death (RCD) that triggers an intact antigen-specific adaptive immune response by firing a set of danger signals or damage-associated molecular patterns (DAMPs), which is an immunotherapeutic modality with the potential for the treatment of hematological malignancies. This review summarizes the existing knowledge about the induction of ICD in hematological malignancies and the current research on combining ICD inducers with other treatment strategies for hematological malignancies.

Improving Traffic Flow in Emerging Cities: A SIDRA Intersection Based Traffic Signal Design

Intersections in urban centers, especially those without any form of signalization, are accident hotspots. This, therefore, calls for ef-fective and efficient traffic management at the intersections for improved safety and efficient traffic flow. This study aimed to improve traffic flow at the Gaa-Akanbi intersection in Ilorin, Nigeria, using a traffic signal scheme. A traffic volume study and geometric features survey was carried out at the intersection. The traffic volume study was performed to determine the number, movement, and classification of vehicles at this intersection using the manual method of traffic count, while the geometric survey of the intersection was done using tape and Total Station. A 3-phase traffic signal was proposed. The optimum cycle length and signal setting were determined using SIDRA Intersection software by adopting the maximum average passenger car unit on the intersection and targeted level of service (LOS) "D". A traffic signal plan with a cycle length of 150 seconds was designed for the intersection. The amber time was considered to be 2 seconds for all phases, and green time of 48, 46 and 38 seconds was gotten for phases 1, 2 and 3, respectively; this timing ensures that minimum delay occurs at the intersection. The proposed traffic signal should be adopted at the intersection by the metropolitan traffic management agency to improve traffic management.

HGSmark: An efficient ECG watermarking scheme using hunger games search and Bayesian regularization BPNN

The extensive use of telemedicine has led to an exponential increase in the exchange of medical data via insecure networks. For example, diagnosis in telemedicine practice often requires electrocardiograms to be shared via Internet. This raises security and authenticity concerns. In this paper, we have proposed HGSmark—an ECG signal watermarking scheme based on multiple embedding strength (MES). We have used the hunger games search (HGS) algorithm to optimize the MES values locally, while maintaining the imperceptibility-robustness trade-off. However, as the optimization using metaheuristic techniques is time-intensive, we have used HGS only once to train a Bayesian regularization-backpropagation neural network (BR-BPNN) on a set of ECG signals. Subsequently, the trained BR-BPNN is employed to quickly compute the optimum MES values. For watermarking, we have employed a wrapping-based fast discrete curvelet transform (FDCT) and singular value decomposition (SVD) to extract the principal component (PC) coefficients of the ECG signal, and embedded a watermark into PC coefficients using the MES values. We have used PC coefficients for watermarking in order to avoid the problem of false-positive. Further, to enhance security, we have encoded the watermark using the logistic chaotic map. We evaluated the performance of HGSmark on MIT-BIH normal sinus rhythm and MIT-BIH arrhythmia databases. The experimental results show that HGSmark achieved average PSNR and PRD values of 57.725 dB and 0.271, respectively and is robust to common interferences. Moreover, the comparison with the state-of-the-art ECG signal watermarking schemes establishes that HGSmark is superior to others in terms of imperceptibility and robustness.

Efficient Energy Management Strategy for an Electric Vehicle Powered by a Hybrid Energy Storage System Based on Hybrid GBDT-RSA Approach

This paper proposes an efficient energy management scheme for an EV with a hybrid energy storage system like super capacitor and battery based on hybrid optimization method. The proposed hybrid approach is a parallel performance of gradient boosting decision tree algorithm and reptile search algorithm. The major purpose is to diminish the variance among real and reference power on battery and super capacitor. The HESS is divided into two segments: (1) Determine the super capacitor reference voltage, which is affected by load dynamics. (2) Maximize the power flow via the HESS. The super capacitor reference voltage is first calculated by assessing the load dynamics on real time, such as vehicle dynamics, motor characteristics, and regenerative braking systems. The super capacitor’s input parameters include load current, battery current, and state of charge. The RSA is created on proposed system by combining the probable data set of HESS control signals. The GBDT algorithm is trained and forecasts optimal HESS parameters using the RSA’s practiced data set. The proposed approach also improves battery current magnitude, super capacitor voltage, battery current ranges, and battery power. It optimizes the HESS parameter provides specific solutions. The proposed hybrid system is implemented on MATLAB and its performance is related to that of existing approaches.