Abstract
In this paper, a detailed implementation of a lithium-ion battery life prognostic system using a particle filtering framework is presented. A lumped parameter battery model is used to account for all the dynamic characteristics of the battery: a non-linear open-circuit voltage, current, temperature, cycle number, and time-dependent storage capacity. The internal processes of the battery are used to form the basis of this model. Statistical estimates of the noise in the system and the anticipated operational conditions are processed to provide estimates of the remaining useful life. The model is then subsequently used in the particle-filtering framework with a sequential importance resampling algorithm to predict the remaining useful life of the battery for individual discharge cycles as well as for the battery cycle life. The research presented in this paper provides the necessary steps towards a comprehensive battery health management solution for energy storage devices.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
