Degradation modeling of serial space lithium-ion battery pack based on online inconsistency representation parameters

Abstract

Establishing an inconsistency-based degradation model for lithium-ion battery packs is crucial for suppressing the degradation of battery packs by optimizing the inconsistency. This paper proposes a method for modeling the degradation of serial space lithium-ion battery packs based on online inconsistency representation parameters. Firstly, the static inconsistency representation parameters are acquired online by quantifying the difference among voltage intervals of cells through Gaussian distribution, addressing the challenge of acquiring static inconsistency representation parameters online. Simultaneously, dynamic inconsistency representation parameters are serialized representations by quantifying the voltage differences of cells at multiple moments, better capturing the rapid evolutionary process of inconsistency. Secondly, a linear model is used to model the serialized parameters and degradation, simplifying the modeling process while achieving multi-source information fitting. Meanwhile, a nonlinear model is constructed to better fit the nonlinear degradation trend of the battery pack. Then, Kalman filter is utilized for model fusion to achieve complementary advantages and improve modeling accuracy. Cross-validation with laboratory battery pack data confirms that this method outperforms comparison approaches, achieving the mean absolute error and maximum error of less than 1.73 % and 3.31 %, respectively. The method proposed in this paper provides a basis for future work on battery pack life extension.