查看更多>>摘要：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 in Vellore, Ind ia, by NewsRx journalists, research stated, “In recent times, battery technology used in electric vehicles has drawn numerous researchers’ attention. Monitoring the battery condition, particularly the State of charge, is required to ensure the battery’s safe and reliable performance.” The news reporters obtained a quote from the research from VIT University, “Desp ite the fact that many SOC estimation methods have been proposed, further resear ch is necessary to identify an approach that adapts versatile lithium-ion batter y chemistries. In the recent study it has been demonstrated that Machine Learnin g approaches have better prediction accuracy compared to conventional methods. T o maximize the performance of Machine learning models, it is imperative to selec t the optimal hyperparameters and employ appropriate input parameters. At presen t, researchers employ, established heuristics methods to select hyperparameters, which may involve manual tuning or exhaustive search techniques such as random search and grid search. These techniques make the models less accurate and ineff icient. In this paper, a systematic, automated process for selecting hyperparame ters with a Bayesian optimization algorithm is proposed. In addition, along with the battery parameters (voltage, current and temperature), vehicle velocity, ro ad condition, motor characteristics and environmental conditions are used as the input parameters for accurate SOC prediction. The highly correlated input featu res are selected through the MRMR algorithm. The performance of six ML algorithm s, namely SVM, ANN, GPR, Ensembler, linear regression and Decision Tree, is test ed and validated with and without hyperparameter tuning for different data sets. The experimental results demonstrate that the hyperparameter-tuned model outper forms the standard model in estimating the State of charge (SOC).”
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