A Novel Discharge Mode Identification Method for Series-Connected Battery Pack Online State-of-Charge Estimation Over A Wide Life Scale

Lithium-ion batteries are widely used in energy storage nowadays. However, differences caused by aging among in-pack cells are inevitable, which makes accurate state-of-charge (SOC) estimation for packs still challenging. In this article, a novel discharge mode identification (DMI) method for series-connected battery pack online SOC estimation is proposed. The DMI method simplifies the process of searching for "poor SOC cell." The discharge process is defined into two different modes on the basis of inconsistency analysis. The pack SOC is estimated based on the average cell or all cells during different discharge phases, respectively. Furthermore, considering lower computational load is critical in a battery management system (BMS), a novel segmented coulomb counting (SCC) method based on partial adaptive forgetting factors recursive least square (PAFFRLS) is proposed as a part of the DMI method, which provides a balanced solution to the cell SOC estimation. Eventually, numerous simulations and experiments for LiNCM and LiFePO4 packs are employed to verify the validity of the proposed DMI over a wide life scale. The average estimation errors of a series-connected battery pack under different working conditions at different temperatures are within 2.5%, which shows a good performance and provides a better guidance to the design of BMS.