Researcher at Sichuan University Targets Machine Learning (State of Health Estim ation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current I nternal Resistance)
Researcher at Sichuan University Targets Machine Learning (State of Health Estim ation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current I nternal Resistance)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Chengdu, Peopl e’s Republic of China, by NewsRx editors, research stated, “Battery state of hea lth (SOH), which is a crucial parameter of the battery management system, reflec ts the rate of performance degradation and the aging level of lithium-ion batter ies (LIBs) during operation.” Our news reporters obtained a quote from the research from Sichuan University: “ However, traditional machine learning models face challenges in accurately diagn osing battery SOH in complex application scenarios. Hence, we developed a deep l earning framework for battery SOH estimation without prior knowledge of the degr adation in battery capacity. Our framework incorporates a series of deep neural networks (DNNs) that utilize the direct current internal resistance (DCIR) featu re to estimate the SOH. The correlation of the DCIR feature with the fade in cap acity is quantified as strong under various conditions using Pearson correlation coefficients. We utilize the K-fold cross-validation method to select the hyper parameters in the DNN models and the optimal hyperparameter conditions compared with machine learning models with significant advantages and reliable prediction accuracies.”
Key words
Sichuan University/Chengdu/People’s Re public of China/Asia/Cyborgs/Emerging Technologies/Machine Learning
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