Study on the Application of Transfer Learning for Railway Vehicle Propulsion Battery Aging Estimation

초록

The aging of lithium-ion battery packs which are used as energy sources in eco-friendly railway vehicles is affected by environmental factors and may act as potential causes of fires and accidents. The aging of railway vehicle batteries was estimated using methods such as coulomb counting and EIS as a preventative measure, but their effectiveness is limited by shortcomings such as time consuming processes and the need for costly equipment. aging estimation using artificial intelligence has been proposed as a solution. However, due to the experimental environment being limited by the size and capacity of railway vehicle propulsion battery packs, it is difficult to obtain training data for artificial intelligence. Thus, this paper proposes a railway vehicle propulsion battery aging estimation method for railway vehicle propulsion batteries using transfer learning.

키워드