Artificial intelligence approach to SoC estimation for smart BMS

Abstract

One of the most important and indispensable parameters of a Battery Management Systems (BMS) is accurate estimates of the State of Charge (SoC) of the battery. It can prevent battery from damage or premature aging by avoiding over charge/discharge. Due to the limited capacity of a battery, advanced methods must be used to estimate precisely the SoC in order to keep battery safely being charged and discharged at a suitable level and to prolong its life cycle. In this paper, we review several effective approaches: Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation; then we propose Artificial Intelligence (AI) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using our proposed approach, a more accurate SoC measurement will be obtained for the smart battery management system.