Towards a hybrid approach to SoC estimation for a smart Battery Management System (BMS) and battery supported Cyber-Physical Systems (CPS)

One of the most important and indispensable parameters of a Battery Management System (BMS) is to accurately estimate the State of Charge (SoC) of battery. Precise estimation of SoC can prevent battery from damage or premature aging by avoiding over charge or 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. We review several existing effective approaches such as Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation. Then we investigate both Artificial Intelligence (AI) approach and Formal Methods (FM) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using presented approach, a more accurate SoC measurement can be obtained for the smart battery management system and battery supported Cyber-Physical Systems (CPS).