A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Abstract

With respect to cell-to-cell variation in battery packs for electric vehicles (EVs), the estimation of state of charge (SOC) and state of health (SOH) of battery systems remains a challenging problem that needs to be solved under the strict computational limitations of battery management system. This paper aims at proposing a stable and accurate SOC/SOH estimation framework for battery pack with multi-cells connected in series with passive balance control. First, the concepts of cell-level and pack-level state definitions, which clearly describe the relationship between the states of the battery cells and those of the battery pack, are introduced. Then a multi time-scale framework for estimating the SOC/SOH of pack is developed. Within the framework, the SOH values (slow dynamics) are estimated with long time interval, while the SOC (fast dynamics) is estimated in real-time. For the framework implementation, nonlinear predictive filter (NPF) is used as the estimation algorithm to provide accurate estimates of SOC and SOH. Finally, experiments are conducted on a battery pack under UDDS driving cycles to validate the performance of the proposed framework. The experimental validation indicates that the SOC and SOH of the battery pack can be accurately estimated using the proposed framework.