An efficient and independent modeling method for lithium-ion battery degradation

Abstract

Degradation in the lithium-ion battery performance is an inevitable phenomenon during the service life. Therefore, the state of health (SOH) of the battery is an important indicator of the battery management system (BMS) in electric vehicles (EVs). The existing estimation techniques to predict the losses due to degradation and SOH of the battery are limited by the situation of being incomplete or computationally expensive. This work develops an efficient and independent modeling method for SOH estimation and degradation analysis, in which a simple parameter fitting can be used to represent the discharge profile that can be represented with an accuracy of > 99.9%, using simple parameter fitting. The homogeneity of the active lithium distribution, the discharge capacity and the evolution of internal resistance have been well demonstrated by the model parameters. In addition, through simple calculation, the loss of active materials and the state of electrode balance are accurately represented by the derivate parameters.

A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance

“…However, it is a single state estimation method, which can only estimate SOH effectively. In [41], an independent battery degradation modeling method was proposed to estimate SOH. It can better simulate the aging characteristics of batteries under different working conditions, but it can not realize dynamic tracking.…”