首页|Study Findings on Machine Learning Are Outlined in Reports from Guangxi Universi ty (Enhanced State-of-charge and State-of-health Estimation of Lithium-ion Batte ry Incorporating Machine Learning and Swarm Intelligence Algorithm)
Study Findings on Machine Learning Are Outlined in Reports from Guangxi Universi ty (Enhanced State-of-charge and State-of-health Estimation of Lithium-ion Batte ry Incorporating Machine Learning and Swarm Intelligence Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting from Nanning, People's Republic of China, by Ne wsRx journalists, research stated, "Accurately estimating the state -of -charge (SOC) and state -of -health (SOH) of lithium batteries used in electric vehicles is critical but challenging. Machine learning advances aid battery health monit oring, but optimizing model performance often requires adjusting hyperparameters which can lead to local optimization issues." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Liuzhou Science Research and Planning Development Project, I nnovation Project of Guangxi Graduate Education. The news correspondents obtained a quote from the research from Guangxi Universi ty, "Gaussian process regression (GPR), one of the commonly used methods, typica lly uses the conjugate gradient method to search for the optimal hyperparameters in lithium -ion battery state estimation, which often results in local optimiza tion. In this paper, the improved firefly algorithm (IFA) is proposed to improve the predictive performance of the GPR model from the internal predictive proces s perspective. To be specific, the four swarm intelligence algorithms are compar ed for hyperparameter optimization and finally a novel IFA-GPR model is proposed . Compared with the traditional conjugate gradient method, the proposed model im proves the accuracy by 6.75 % and 3.12 % in two curr ent conditions for SOC estimation, and by 91.64 % and 78.12 % in two schemes for SOH prediction, respectively. Moreover, compared with other e xisting algorithms, the statistical results again verify the high precision and adaptability of the proposed method in battery diagnosis."
NanningPeople's Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningSwarm Intellige nceGuangxi University
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