Abstract
In the process of battery system design and operation, accurate battery modelling is a key factor. Generally speaking, the electric characteristics of a given battery cell are necessary for a designer to build an equivalent circuit model. The equivalent circuit design entails the creation of both the sizing of components used in the circuit and the topology. So, it is very hard to build an accurate battery model for electric vehicles. This paper presents a single method to design an accurate equivalent circuit by computer automatically. The obtained model enables the assessment of the cells’ state of charge (SOC) precisely using model-based state estimation approaches.
Keywords
References
- 1. (2011). ‘Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy II: modelling’. Journal of Power Sources. 196, 5349-5356 Google Scholar
- 2. (2011). ‘State-of-health estimator based on extension theory with a learning mechanism for lead-acid batteries’. Expert Systems with Applications. 38, 15183-15193 Google Scholar
- 3. (2012). ‘Training artificial neural networks using APPM’. International Journal of Wireless and Mobile Computing. 5, 168-174 Abstract, Google Scholar
- 4. (2012). ‘Modelling of VRLA batteries over operational temperature range using pseudo random binary sequences’. Journal of Power Sources. 207, 56-59 Google Scholar
- 5. (2013). ‘The testing of batteries linked to supercapacitors with electrochemical impedance spectroscopy: a comparison between Li-ion and valve regulated lead acid batteries’. Journal of Power Sources. 226, 299-305 Google Scholar
- 6. (2010). ‘Dynamic modeling and simulation on a hybrid power system for electric vehicle applications’. Energies. 3, 1821-1830 Google Scholar
- 7. (2011a). ‘Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach’. Energies. 4, 582-598 Google Scholar
- 8. (2011b). ‘State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model’. Energies. 60, 1461-1469 Google Scholar
- 9. (2012a). ‘Comparison study on the battery models used for the energy management of batteries in electric vehicles’. Energy Conversion and Management. 64, 113-121 Google Scholar
- 10. (2012b). ‘Online model-based estimation of state-of-charge and open-circuit voltage of lithium ion batteries in electric vehicles’. Energy. 39, 310-318 Google Scholar
- 11. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI:University of Michigan Press Google Scholar
- 12. (2011). ‘Linear parameter varying battery model identification using subspace method’. Journal of Power Sources. 196, 5, 2913-2923 Google Scholar
- 13. (2010). Battery Test Manual for Plug-in Hybrid Electric Vehicles., Assistant Secretary for Energy Efficiency and Renewable Energy. Idaho Falls, ID, USA:Idaho Operation Office Google Scholar
- 14. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA:MIT Press Google Scholar
- 15. (1994). Introduction to Genetic Programming. Cambridge, MA:MIT Press Google Scholar
- 16. (2006). ‘Energy efficiency and QoS optimisations of IEEE 802.11 communications using frame aggregation’. International Journal of Wireless and Mobile Computing. 1, 229-238 Abstract, Google Scholar
- 17. (2004). ‘Extended Kalman filtering for battery management system of LiPB-based HEV battery packs., part 2: modeling and identification’. Journal of Power Source. 134, 262-276 Google Scholar
- 18. (2011). ‘Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries’. Journal of Power Sources. 196, 331-336 Google Scholar
- 19. (2010). ‘Adaptive online state-of-charge determination based on neuro-controller and neural network’. Energy Conversion and Management. 51, 1093-1098 Google Scholar
- 20. (2011). ‘Integrated battery controller for distributed energy system’. Energy. 36, 2392-2398 Google Scholar
- 21. (2012). ‘Online estimation of peak power capability of Li-ion batteries in electric vehicles by a hardware-in-loop approach’. Energies. 5, 1455-1469 Google Scholar
- 22. (2010). ‘Robust state of charge estimation for hybrid electric vehicles: framework and algorithms’. Energies. 3, 1654-1672 Google Scholar
- 23. (2011). ‘A simplified equivalent circuit model for simulation of Pb-acid batteries at load for energy storage application’. Energy Conversion and Management. 52, 2794-2799 Google Scholar