ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Dong, Zhekang; Ji, Xiaoyue; Wang, Jiayang; Gu, Yeting; Wang, Junfan; Qi, Donglian

doi:10.1109/tce.2023.3257201

Cited by 26 publications

(8 citation statements)

References 35 publications

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“…In [18], authors present a novel adaptive control technique, MMGDI-AC, for grid-interfaced photovoltaic-assisted onboard EV charging infrastructure, featuring damping reduction, noise removal, delay minimization, harmonic component filtering, and accurate fundamental component estimation. Another work [19] presents an innovative internal cascaded neuromorphic computing system (ICNCS) utilizing memristor circuits for accurate and fast state of charge (SOC) estimation of Lithium-ion batteries in electric vehicles (EVs) and smart home energy management systems. An efficient charging/discharging scheduling mechanism for plug-in electric vehicles in a shared parking facility among multiple smart households is presented in [20].…”

Section: Literature Surveymentioning

confidence: 99%

Leveraging Generative and Explainable AI for Electric Vehicle Energy Toward Sustainable, Consumer-Centric Transportation

Kumar Mohanty,

Hemant Kumar Reddy,

Panigrahy

et al. 2024

IEEE Access

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In Industry 5.0, predicting electric vehicle energy usage enhances efficiency and sustainability, optimizes charging infrastructure, and meets consumer demands. Leveraging IoT, AI, and analytics enables smart charging that aligns with renewable energy goals, addresses infrastructure limitations, and promotes sustainable transportation by improving user experience, cutting costs, and boosting trust in electric vehicles. This research aims to create a synthetic data set for electric vehicles using an enhanced Generative adversarial network model and, from that, predict the energy for charging electric vehicles using ensemble Machine Learning algorithms. The importance of more detailed features for the best-performing machine learning model has been done utilizing Explainable Artificial Intelligence, specifically, the Shapley Additive Explanations approach, to provide more understanding and derive the inter-dependency among features. The enhanced TemporalCharge Generative adversarial network model provides Skewness and Kurtosis values as -0.51 and -0.182, respectively, for synthetically generated data for the city of Berhampur, Odisha, India, which is very close to four-wheeler electric vehicle charging data of the city. Several plots illustrate the influence of key features on electric vehicle energy consumption during charging, which enhances user optimization, owner empowerment, and ecosystem sustainability.

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Section: Literature Surveymentioning

confidence: 99%

Leveraging Generative and Explainable AI for Electric Vehicle Energy Toward Sustainable, Consumer-Centric Transportation

Kumar Mohanty,

Hemant Kumar Reddy,

Panigrahy

et al. 2024

IEEE Access

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“…Furthermore, with the continuous improvement of AI hardware acceleration systems, the deployment of complex CNN networks has become feasible [34][35][36][37][38]. For instance, Gu et al [39] proposed a lightweight real-time traffic sign detection framework based on YOLOv4.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, with the continuous improvement of AI hardware acceleration systems, the deployment of complex CNN networks has become feasible [34–38]. For instance, Gu et al.…”

Section: Related Workmentioning

confidence: 99%

TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT

Qian,

Wang

2023

IET Image Processing

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In real scenarios, accurate and real‐time detection of traffic signs is of great significance to the automatic driving system. To meet the requirements of detection accuracy and speed, a new traffic sign detection method based on YOLOv5 and Swin‐Transformer is proposed in this paper. First, based on the traditional Focus structure, a lightweight shallow information enhancement module is designed. Second, to enhance the channel weights of useful information, an adjustable channel attention mechanism is proposed. Additionally, a Cross Stage Partial module based on Swin‐Transformer is designed to capture contextual information around traffic signs, thereby improving the detection accuracy of small‐scale traffic signs. Finally, to better fuse deep semantic features and shallow detail features, an adaptive feature fusion method is proposed. To verify the superiority of the proposed method, experimental verification was carried out on TT100K and DFG traffic sign detection datasets, and their mAP, AP50 and FPS were (75.3, 94.8, 82) and (79.7, 85.9, 118), respectively. The experimental results show that the proposed traffic sign detection method has high accuracy and real‐time performance, and can meet the needs of traffic sign detection in the actual scene.

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“…D. Qi et al reported a brain-inspired hierarchical interactive in-memory computing system for video sentiment analysis based on the Ag/a-Carbon/Ag memristor with a structure of 1T1M [7]. However, very few cognitive emotions (e.g., empathy) can be realized at the hardware level [7][8][9][10][11][12]. Memristive devices have been approved as candidates for affective devices; this opens up the possibility of using memristors to achieve empathic emotion [13,14].…”

Section: Introductionmentioning

confidence: 99%

Realization of Empathy Capability for the Evolution of Artificial Intelligence Using an MXene(Ti3C2)-Based Memristor

Wang,

Zhang,

Wang

et al. 2024

Electronics

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Empathy is the emotional capacity to feel and understand the emotions experienced by other human beings from within their frame of reference. As a unique psychological faculty, empathy is an important source of motivation to behave altruistically and cooperatively. Although human-like emotion should be a critical component in the construction of artificial intelligence (AI), the discovery of emotional elements such as empathy is subject to complexity and uncertainty. In this work, we demonstrated an interesting electrical device (i.e., an MXene (Ti3C2) memristor) and successfully exploited the device to emulate a psychological model of “empathic blame”. To emulate this affective reaction, MXene was introduced into memristive devices because of its interesting structure and ionic capacity. Additionally, depending on several rehearsal repetitions, self-adaptive characteristic of the memristive weights corresponded to different levels of empathy. Moreover, an artificial neural system was designed to analogously realize a moral judgment with empathy. This work may indicate a breakthrough in making cool machines manifest real voltage-motivated feelings at the level of the hardware rather than the algorithm.

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ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Cited by 26 publications

References 35 publications

Leveraging Generative and Explainable AI for Electric Vehicle Energy Toward Sustainable, Consumer-Centric Transportation

Leveraging Generative and Explainable AI for Electric Vehicle Energy Toward Sustainable, Consumer-Centric Transportation

TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT

Realization of Empathy Capability for the Evolution of Artificial Intelligence Using an MXene(Ti3C2)-Based Memristor

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