A realistic model for battery state of charge prediction in energy management simulation tools. (15th March 2019)

• Record Type: Journal Article
• Authors: Homan, Bart; ten Kortenaar, Marnix V.; Hurink, Johann L.; Smit, Gerard J.M.
• Is Part Of: Energy. Volume 171(2019)
• Page Start: 205
• Page End: 217
• Publication Date: 2019-03-15
• Subjects: Pb-acid battery -- Li-poly battery -- LiFePo battery -- State-of-Charge -- Prediction -- Energy management; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.12.134
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this paper, a comprehensive model for the prediction of the state of charge of a battery is presented. This model has been specifically designed to be used in simulation tools for energy management in (smart) grids. Hence, this model is a compromise between simplicity, accuracy and broad applicability. The model is verified using measurements on three types of Lead-acid ( Pb-acid ) batteries, a Lithium-ion Polymer ( Li-Poly ) battery and a Lithium Iron-phosphate ( LiFePo ) battery. For the Pb-acid batteries the state of charge is predicted for typical scenarios, and these predictions are compared to measurements on the Pb-acid batteries and to predictions made using the KiBaM model. The results show that it is possible to accurately model the state of charge of these batteries, where the difference between the model and the state of charge calculated from measurements is less than 5%. Similarly the model is used to predict the state of charge of Li-Poly and LiFePo batteries in typical scenarios. These predictions are compared to the state of charge calculated from measurements, and it is shown that it is also possible to accurately model the state of charge of both Li-Poly and LiFePo batteries. In the case of the Li-Poly battery the difference between the measured and predicted state of charge is less than 5% and in the case of the LiFePo battery this difference is less than 3%. Highlights: Accurate models for SoC prediction are needed in (smart)grid simulations. The Distribution Buffer model (DiBu-model) is designed to be used within decentralized energy management tools. The SoC of Pb-acid, Li-ion and LiFePo batteries can be predicted with the DiBu-model. Differences between the predicted SoC and measured SoC are generally below 5%.