To the Editor,The common causes of liver cirrhosis are alcoholic liver disease,nonalcoholic fatty liver disease(NAFLD),and hepatitis B and C viruses[1].Autoimmune liver diseases(AiLD)account for only approximately 6%o...To the Editor,The common causes of liver cirrhosis are alcoholic liver disease,nonalcoholic fatty liver disease(NAFLD),and hepatitis B and C viruses[1].Autoimmune liver diseases(AiLD)account for only approximately 6%of all etiologies causing liver diseases in India and include autoimmune hepatitis(AIH),primary biliary cholangitis(PBC),primary sclerosing cholangitis(PSC),overlap of these three disorders,and immunoglobulin G4 related disease[2].展开更多
Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and en...Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions.Additionally,state-of-health(SOH)and remaining-useful-life(RUL)predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance.Due to the non-linear behaviour of the health prediction of electric vehicle batteries,the assessment of SOH and RUL has therefore become a core research challenge for both business and academics.This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management,emphasizing state prediction and ageing prognostics.The objective is to provide comprehensive information about the evaluation,categorization and multiple machine-learning algorithms for predicting the SOH and RUL.Additionally,lithium-ion bat-tery behaviour,the SOH estimation approach,key findings,advantages,challenges and potential of the battery management system for different state estimations are discussed.The study identifies the common challenges encountered in traditional battery manage-ment and provides a summary of how machine learning can be employed to address these challenges.展开更多
文摘To the Editor,The common causes of liver cirrhosis are alcoholic liver disease,nonalcoholic fatty liver disease(NAFLD),and hepatitis B and C viruses[1].Autoimmune liver diseases(AiLD)account for only approximately 6%of all etiologies causing liver diseases in India and include autoimmune hepatitis(AIH),primary biliary cholangitis(PBC),primary sclerosing cholangitis(PSC),overlap of these three disorders,and immunoglobulin G4 related disease[2].
文摘Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility.However,predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions.Additionally,state-of-health(SOH)and remaining-useful-life(RUL)predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance.Due to the non-linear behaviour of the health prediction of electric vehicle batteries,the assessment of SOH and RUL has therefore become a core research challenge for both business and academics.This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management,emphasizing state prediction and ageing prognostics.The objective is to provide comprehensive information about the evaluation,categorization and multiple machine-learning algorithms for predicting the SOH and RUL.Additionally,lithium-ion bat-tery behaviour,the SOH estimation approach,key findings,advantages,challenges and potential of the battery management system for different state estimations are discussed.The study identifies the common challenges encountered in traditional battery manage-ment and provides a summary of how machine learning can be employed to address these challenges.
