Online Identification of Battery Internal Resistance under extreme Temperatures

Abstract

Lithium ion batteries are the key component in electric vehicles and hybrid electric vehicles. Monitoring adequately this component can be very challenging due to its nonlinear electrochemical behavior. Several factors, such as the temperature and the aging, impact the battery’s performances and its models’ parameters. In order to make a good use of this component and to ensure its safety it is necessary to keep track of its models’ parameters in real time. This paper provides an accurate online identification process to estimate the battery internal resistance under extreme temperatures. This online identification process is validated through experimental testing.