Battery State-of-Charge and Parameter Estimation Algorithm Based on Kalman Filter

Electrochemical battery is the most widely used energy storage technology, finding its application in various devices ranging from low power consumer electronics to utility back-up power. All types of batteries show highly non-linear behaviour in terms of dependence of internal parameters on operating conditions, momentary replenishment and a number of past charge/discharge cycles. A good indicator for the quality of overall customer service in any battery based application is the availability and reliability of these informations, as they point out important runtime variables such as the actual state of charge (SOC) and state of health (SOH). Therefore, a modern battery management systems (BMSs) should incorporate functions that accommodate real time tracking of these nonlinearities. For that purpose, Kalman filter based algorithms emerged as a convenient solution due to their ability to adapt the underlying battery model on-line according to internal processes and measurements. This paper proposes an enhancement of previously proposed algorithms for estimation of the battery SOC and internal parameters. The validity of the algorithm is confirmed through the simulation on experimental data captured from the lead acid battery stack installed in the real-world remote telecommunication station.