A remaining charging electric quantity based pack available capacity optimization method considering aging inconsistency

Regarding battery packs in electric vehicles (EVs), the variations in the electric quantity and capacity of a pack due to aging inevitably affects its available capacity and performance. Thus, it is necessary to optimize the available capacity of the battery pack. Focusing on this problem, this paper proposes a novel concept of the remaining charging electric quantity (RCQ) to describe the battery state while considering aging. Then, a dual adaptive dual extended Kalman filter (Dual-ADEKF) algorithm is utilized to identify the model parameters and RCQ of each cell under working conditions. Furthermore, the actual RCQ of the battery pack after constant current charging is predicted while considering the aging caused capacity degradation and the increase in the DC internal resistance. To maximize the available capacity, a bidirectional active balancing system is used to adjust the electric quantity distribution in the pack to ensure that the RCQ of each cell is consistent; the available capacity of the pack is then determined by the battery with the lowest actual RCQ. The results show that the available capacity estimation of aged batteries while considering the influence of capacity degeneration and the DC internal resistance is effectively achieved. To test a battery pack with an initial capacity and an electric quantity inconsistency caused by aging, a federal urban driving schedule (FUDS) test is performed. The electric quantity to be balanced of each cell is calculated according to the RCQ estimation results with the target of maximizing the available capacity of the pack. It is verified that the available capacity of the pack increases by 11.7% compared to that before equalization, while the maximum electric quantity variation between cells is improved, decreasing from 20% to 2.6%. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces the concept of remaining charging electric quantity (RCQ) to address the aging issue in electric vehicle battery packs, and utilizes a dual adaptive dual extended Kalman filter (Dual-ADEKF) algorithm for state estimation and balancing. The results show significant improvement in available capacity by effectively considering capacity degradation and DC internal resistance during aging.