Xiaohui Ye^1,*, Fucheng Tan¹, Xinli Song², Hanyang Dai², Xia Li², Shixia Mu², Shaohang Hao²

Energy Engineering, Vol.121, No.12, pp. 3689-3710, 2024, DOI:10.32604/ee.2024.055200 - 22 November 2024

Abstract Energy storage batteries can smooth the volatility of renewable energy sources. The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system (BESS). However, the current modeling of grid-connected BESS is overly simplistic, typically only considering state of charge (SOC) and power constraints. Detailed lithium (Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions. Additionally, there is a lack of real-time batteries risk assessment frameworks. To address these issues, in this… More >

Peng Wang^1,*, Ningbo Li¹, Ruolan Jiang¹, Bing Dong¹, Dongliang Sun¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012551

Abstract Lithium-ion batteries, renowned for their lightweight design and voltage stability, have found widespread applications in portable electronic devices, stationary energy storage systems, and electric vehicles. Slurry coating stands out as a pivotal manufacturing process for lithium-ion battery electrodes. In particular, slot-die coating technology, known for its rapid coating speed, has seen extensive engineering adoption in recent years. Utilizing numerical simulations to study the slurry coating process for lithium-ion battery electrodes allows for a detailed analysis of the complex fluid dynamics involved, thereby playing a crucial role in improving coating uniformity and enhancing battery performance. This… More >

Yuhang Lyu¹, Shaohai Dong¹, Li Ting Gao¹, Zhan-Sheng Guo^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.012815

Abstract Drying and calendering processes are crucial in electrode manufacturing, as they significantly affect the mechanical and electrochemical performances of lithium-ion batteries. In this study, we established a three-dimensional (3D) representative volume element (RVE) of electrodes composed of active material particles, carbon binder domain particles, solvent, and different particle contact types. We continuously simulated the 3D macroscopic and microscopic structural evolutions of the RVE during drying and calendering using the discrete element method (DEM). Based on the evolution of the particle coordination numbers and contact networks during drying, we propose a three-stage-drying scheme, consistent with the More >

Hamad Naeem¹, Amjad Alsirhani^2,*, Faeiz M. Alserhani³, Farhan Ullah⁴, Ondrej Krejcar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2185-2223, 2024, DOI:10.32604/cmes.2024.056308 - 31 October 2024

Abstract When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized… More >

Qi Wang^1,2,3, Yandong Gu^1,*, Tao Zhu¹, Lantian Ge¹, Yibo Huang¹

Energy Engineering, Vol.121, No.11, pp. 3221-3239, 2024, DOI:10.32604/ee.2024.053758 - 21 October 2024

Abstract Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters… More >

Aimable Ngendahayo^1,*, Adrià Junyent-Ferré², Joan Marc Rodriguez Bernuz³, Etienne Ntagwirumugara¹

Energy Engineering, Vol.121, No.11, pp. 3121-3131, 2024, DOI:10.32604/ee.2024.053100 - 21 October 2024

Abstract There are obstacles to the widespread use of small electric vehicles (EVs) in Rwanda, including concerns regarding the battery range and lifespan. Lithium-ion batteries (LIBs) play an important role in EVs. However, their performance declines over time because of several factors. To optimize battery management systems and extend the range of EVs in Rwanda, it is essential to understand the influence of the driving profiles on lithium-ion battery degradation. This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions… More >

Haojie Yang, Xiang Wen, Peng Geng^*

Journal on Artificial Intelligence, Vol.6, pp. 283-300, 2024, DOI:10.32604/jai.2024.056303 - 18 October 2024

Abstract To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms. By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius, the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are… More >

Tongan Yu¹, Yali Xue^1,*, Yiming He¹, Shan Cui², Jun Hong²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.056075 - 15 October 2024

Abstract With the rapid development of deep learning-based detection algorithms, deep learning is widely used in the field of infrared small target detection. However, well-designed adversarial samples can fool human visual perception, directly causing a serious decline in the detection quality of the recognition model. In this paper, an adversarial defense technology for small infrared targets is proposed to improve model robustness. The adversarial samples with strong migration can not only improve the generalization of defense technology, but also save the training cost. Therefore, this study adopts the concept of maximizing multidimensional feature distortion, applying noise… More >

Bum-Sok Kim¹, Hye-Won Suk¹, Yong-Hoon Choi², Dae-Sung Moon³, Min-Suk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1551-1574, 2024, DOI:10.32604/cmes.2024.052375 - 27 September 2024

Abstract Currently, cybersecurity threats such as data breaches and phishing have been on the rise due to the many different attack strategies of cyber attackers, significantly increasing risks to individuals and organizations. Traditional security technologies such as intrusion detection have been developed to respond to these cyber threats. Recently, advanced integrated cybersecurity that incorporates Artificial Intelligence has been the focus. In this paper, we propose a response strategy using a reinforcement-learning-based cyber-attack-defense simulation tool to address continuously evolving cyber threats. Additionally, we have implemented an effective reinforcement-learning-based cyber-attack scenario using Cyber Battle Simulation, which is a… More >

Tianqing Yuan^1,2, Na Li^1,2, Hao Sun³, Sen Tan^4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4497-4512, 2024, DOI:10.32604/cmc.2024.056061 - 12 September 2024

Abstract To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with… More >