Comparison of common and combined state of charge estimation methods for VRLA batteries

Electrochemical batteries play a key role in electrical and electronics devices such are laptops, cellphones, electric cars, etc. Battery packs in devices having higher power are still too much expensive for wide applications. To achieve an effective exploitation of battery packs it is important to have a robust and reliable battery management. Accurate State of Charge estimation in battery management is the basis of economical and energy performance assessment of battery pack including lifetime extension. The most popular used State of Charge estimation methods are analyzed and compared in this paper including Kalman filter approach. An interesting option is also a combination of two or more methods to achieve effective estimation with acceptable computational demands. Self-learning algorithms such as Neural Networks, Fuzzy Logic or Support Vector Machine are not included in this comparison since these methods need large amount of training data. The goal of this paper is the selection of accurate and simple SOC method suitable for battery pack used in stand-alone energy system.