Design of a Digital Dashboard on Low-Cost Embedded Platform in a Fully Electric Vehicle

Benedetti, David; Agnelli, Jacopo; Gagliardi, Alessio; Dini, Pierpaolo; Saponara, Sergio

doi:10.1109/eeeic/icpseurope49358.2020.9160509

Cited by 18 publications

(11 citation statements)

References 5 publications

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“…Looking ahead, the future of deep learning in the IoT appears promising, especially in the realms of automotive, industrial, automation, and mechatronics applications. In the automotive sector, the potential for deep learning to enhance autonomous driving systems, advanced driver-assistance systems (ADAS), and predictive maintenance stands as a critical area for development [138][139][140][141][142][143][144][145][146][147][148][149][150][151][152]. Similarly, in industrial and manufacturing settings, the integration of deep learning holds the promise of optimizing production processes, predicting equipment failures, and improving overall operational efficiency.…”

Section: Discussionmentioning

confidence: 99%

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.

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Section: Discussionmentioning

confidence: 99%

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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“…The estimation of losses in inverter components is an essential point to obtain a realistic model in power electronic systems [69][70][71][72][73][74][75][76].…”

Section: Inverter Losses Modelingmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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“…The most commonly used technique is that of forced oscillations, often accompanied by the classic Ziegler-Nichols table [43][44][45][46][47][48]. Due to the ease of development and integration on an embedded platform, it is also one of the most widely used control techniques in power and control electronics in general [49][50][51], not only in electrical drives.…”

Section: Resonant Controlmentioning

confidence: 99%

Advanced Permanent Magnet Machines and Drives

2023

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Design of a Digital Dashboard on Low-Cost Embedded Platform in a Fully Electric Vehicle

Cited by 18 publications

References 5 publications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Advanced Permanent Magnet Machines and Drives

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