A novel low-complexity state-of-energy estimation method for series-connected lithium-ion battery pack based on representative cell selection and operating mode division

In this paper, we present a novel low-complexity state-of-energy (SOE) estimation method for series-connected lithium-ion battery pack based on representative cell selection and operating mode division. Firstly, an ohmic resistance-based representative cell selection method is proposed to determine the freshest cell and the oldest cell among all in-pack cells reliably and rapidly. Subsequently, according to the cell inconsistences degree, battery cell operating mode is artificially divided into two classes. In mode 1, only the oldest cell's SOE needs to be online tracked, which can be directly seen as battery pack's estimated SOE because of the low cell inconsistences degree. In mode 2, a second-order extended Kalman filter is designed to estimate the SOE difference between the freshest cell and the oldest cell under macro time-scale to further compute the freshest cell's SOE. With the freshest cell's and the oldest cell's estimated SOE, battery pack's SOE in mode 2 can be finally obtained by the adaptive weighted strategy. The validation results through sophisticated driving simulation show that the developed method can achieve accurate SOE estimation for battery pack, where the SOE error bands after convergence by two different data are limited within -2% and 0, and within -3% and 0, respectively.

Automated summary

This paper presents a novel low-complexity state-of-energy (SOE) estimation method for series-connected lithium-ion battery pack based on representative cell selection and operating mode division. The method accurately estimates the SOE of the battery pack by tracking the freshest cell and the oldest cell, dividing battery cell operating mode into two classes, and using an adaptive weighted strategy. Validation results show that the developed method achieves accurate SOE estimation with limited error bands.