首页|Studies from Beijing Institute of Technology Further Understanding of Machine Le arning (State of Health Analysis of Batteries At Different Stages Based On Real- world Vehicle Data and Machine Learning)
Studies from Beijing Institute of Technology Further Understanding of Machine Le arning (State of Health Analysis of Batteries At Different Stages Based On Real- world Vehicle Data and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating from Beijing, P eople's Republic of China, by NewsRx correspondents, research stated, "The capac ity and performance of batteries decay over time. How to deal with retired batte ries is a major challenge at present." Funders for this research include Jilin Scientific and Technological Development Program, National Natural Science Foundation of China (NSFC), Beijing Municipal Science & Technology Commission, Opening Foundation of Key Labora tory of Advanced Manufacture Technology for Automobile Parts, Ministry of Educat ion, Fundamental Research Funds for the Central Universities. Our news editors obtained a quote from the research from the Beijing Institute o f Technology, "This study proposes a health state assessment method for retired batteries. The Forgetting Factor Recursive Least Square is used for parameter id entification based on the operation data of new energy vehicles at different mil eage periods. Ohmic internal resistance is extracted and used as a characteristi c parameter to characterize the state of health. The internal resistances of the vehicles at different driving cycles are compared and their variations are deri ved. Parameters highly correlated with the battery ohmic internal resistance are selected as input parameters for the long and short-term memory neural network. The accurate state of health prediction model is obtained after parameter tunin g. The root-mean-square error of the predicted results is less than 0.01 Omega. This shows that the proposed method can effectively assess the power battery sta te of health."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningBeijing Institute of Techno logy
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