A Novel Most Significant Cell Methodology in a Battery Pack with Serial Cell Connection

Abstract

Rechargeable lithium-ion batteries are now widely adopted in our life. To fulfil various energy and power requirements in real-world applications, battery cells are connected to form battery packs. The cell-to-cell difference exists in the battery pack after manufactured, and this difference will further deteriorates when the battery cells are exposed and used in various operating conditions. This unavoidable cell-to-cell difference results in early cut-off on the battery pack, which influences the performance of the battery pack and makes accurately estimating the battery pack SOC challenging. This paper proposes a novel real-time algorithm to effectively identify the most significant cells in a serial-connected battery pack in order to accurately estimate the SOC of the battery pack. A battery pack composed of ten serial-connected battery cells is carried out in this paper to evaluate the performance of the proposed algorithm. The results show that the most significant cells are successfully identified, and the SOC of the battery pack is estimated accurately based on the identified most significant cell.