Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 36, Issue 1, Pages 326-341Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2020.3001020
Keywords
State of charge; Batteries; Estimation; Discharges (electric); Resistance; Voltage measurement; Aging; Discharge mode identification (DMI); inconsistent lithium-ion battery pack; partial adaptive forgetting factors; segmented coulomb counting (SCC); state of charge (SOC) estimation
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Funding
- National Key Research and Development Program of China [2017YFB1201002, 2017YFB1201003]
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This article proposes a new discharge mode identification (DMI) method for online SOC estimation of series-connected battery packs, which simplifies the process of searching for poorly performing cells by analyzing inconsistencies. The pack SOC is estimated based on the average or all cells during different discharge phases. Furthermore, a segmented coulomb counting (SCC) method based on partial adaptive forgetting factors recursive least square (PAFFRLS) is introduced to address the computational load issue in a battery management system (BMS).
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.
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