首页|Studies in the Area of Machine Learning Reported from Ningbo University of Techn ology (Application of machine learning in ultrasonic diagnostics for prismatic l ithium-ion battery degradation evaluation)
Studies in the Area of Machine Learning Reported from Ningbo University of Techn ology (Application of machine learning in ultrasonic diagnostics for prismatic l ithium-ion battery degradation evaluation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting from Zhejiang, People's Repu blic of China, by NewsRx journalists, research stated, "Lithium-ion batteries ar e essential for electrochemical energy storage, yet they undergo progressive agi ng during operational lifespan." The news editors obtained a quote from the research from Ningbo University of Te chnology: "Consequently, precise estimation of their state of health (SOH) is cr ucial for effective and safe operation of energy storage systems. This paper inv estigates the viability of ultrasound-based methods for assessing the SOH of pri smatic lithium-ion batteries. In the experimental framework, a designated prisma tic lithium-ion battery was subjected to numerous charging and discharging cycle s using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algori thms: linear regression, support vector machines, Gaussian process regression, a nd neural networks."
Ningbo University of TechnologyZhejian gPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Lea rning
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