A systematic and low-complexity multi-state estimation framework for series-connected lithium-ion battery pack under passive balance control

TL;DR: In this paper , a low-complexity multi-state estimation framework for series-connected lithium-ion battery pack under passive balance control, including pack state-of-charge (SOC), state of health (SOH), and cell SOC inconsistences estimation, is presented.

Abstract: To reduce computational burden and achieve accurate states estimation, this paper presents a systematic and low-complexity multi-state estimation framework for series-connected lithium-ion battery pack under passive balance control, including pack state-of-charge (SOC), state-of-health (SOH) and cell SOC inconsistences estimation. Firstly, through SOC and SOH calculation simplification of battery pack under passive balance control, “representative cell” is determined among all in-pack cells to reflect battery pack's behavior by a rapid and reliable selection method. Secondly, a variable multi time-scale based framework is further applied to co-estimate SOC and SOH of the selected “representative cell”, which are also the co-estimation results of battery pack. Subsequently, with the estimated SOC of “representative cell”, a second-order extended Kalman filter is designed to realize cell SOC inconsistences tracking in macro time-scale, and the non-representative cells’ SOC can be further calculated. The validation results through sophisticated driving simulation show that both mean-absolute-error (MAE) and root-mean-square-error (RMSE) between battery pack's real SOC and representative cell's estimated SOC are below 1%, and the relative error band between battery pack's precise capacity and representative cell's calculated capacity is between 0 and 1.5%. Moreover, based on the monitored cell SOC inconsistences, both MAE and RMSE between the non-representative cells’ estimated SOC and reference SOC can be roughly limited below 3%.