Barai, Anup
(Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)
,
Ashwin, T.R.
(Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)
,
Iraklis, Christos
(Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)
,
McGordon, Andrew
(Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)
,
Jennings, Paul
(Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)
Abstract An automotive Battery Management System (BMS) provides the on-board estimation of remaining energy, which in-turn employs an equivalent circuit model (ECM). ECM provides vital information like state of charge and state of health of the battery. The ECM is commonly developed and parameteris...
Abstract An automotive Battery Management System (BMS) provides the on-board estimation of remaining energy, which in-turn employs an equivalent circuit model (ECM). ECM provides vital information like state of charge and state of health of the battery. The ECM is commonly developed and parameterised using cell level test data. The lithium-ion battery pack has tens to thousands of cells, connected in series-parallel configuration within the modules, and multiple modules are connected in series/parallel to form the battery pack. The ECM is usually scaled-up from a cell to a battery module and pack; which introduces inaccuracy, reflected as poor prediction of remaining energy. As a first step to the long-term goal to enhance the BMS performance, this research is focused on identifying the sources which contribute toward discrepancies of battery capacity and resistance, two key model parameters measured from cell level and module level test data. To achieve this, capacity and resistance of the battery cells has been measured. The same cells were used to construct four different battery modules and module capacity and resistance were measured. From the capacity test it was found that depending on how the cells are arranged within the module the capacity will vary by 5.3%. The resistance was found to be increasing as well, by 2.1-5.3%. The resistance variation mainly originates from interconnections of the cells within the modules. Electrochemical impedance spectroscopy tests were performed on the cells and modules to measure the impedance, which suggest similar results as internal resistance measured from pulse power test. This research will enable development of a methodology for robust model parameter extraction and thus ECM development for battery packs.
Abstract An automotive Battery Management System (BMS) provides the on-board estimation of remaining energy, which in-turn employs an equivalent circuit model (ECM). ECM provides vital information like state of charge and state of health of the battery. The ECM is commonly developed and parameterised using cell level test data. The lithium-ion battery pack has tens to thousands of cells, connected in series-parallel configuration within the modules, and multiple modules are connected in series/parallel to form the battery pack. The ECM is usually scaled-up from a cell to a battery module and pack; which introduces inaccuracy, reflected as poor prediction of remaining energy. As a first step to the long-term goal to enhance the BMS performance, this research is focused on identifying the sources which contribute toward discrepancies of battery capacity and resistance, two key model parameters measured from cell level and module level test data. To achieve this, capacity and resistance of the battery cells has been measured. The same cells were used to construct four different battery modules and module capacity and resistance were measured. From the capacity test it was found that depending on how the cells are arranged within the module the capacity will vary by 5.3%. The resistance was found to be increasing as well, by 2.1-5.3%. The resistance variation mainly originates from interconnections of the cells within the modules. Electrochemical impedance spectroscopy tests were performed on the cells and modules to measure the impedance, which suggest similar results as internal resistance measured from pulse power test. This research will enable development of a methodology for robust model parameter extraction and thus ECM development for battery packs.
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Barai, A., Improvement of Consistency, Accuracy and Interpretation of Characterisation Test Techniques for Li-ion Battery cells for Automotive Application, in WMG. 2015, University of Warwick: UK.
10.1109/ITSC.2014.6958105 Birrell, S.A., A. McGordon, and P.A. Jennings. Defining the accuracy of real-world range estimations of an electric vehicle. in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. 2014.
Williams, T. Real World Test: 2013 Nissan LEAF Range vs 2012 Nissan LEAF Range (w/Viideo). 2013 01/06/2015]; Available from: http://insideevs.com/real-world-test-2013-nissan-leaf-range-vs-2012-nissan-leaf-range/.
Gordon-Bloomfield, N. StaffCar Update: After 73,100 Miles, Our Nissan LEAF Loses its Second Capacity Bar. 2015 01/06/2015]; Available from: https://transportevolved.com/2015/04/14/staffcar-update-after-73100-miles-our-nissan-leaf-loses-its-second-capacity-bar/.
Grandjean, T., A. Barai, A. McGordon, Y. Guo, and J. Marco, Large format lithium ion pouch cell thermal gradient characterisation at different ambient temperatures and current discharge rates. Journal of Power Sources, 2017. Under Review.
Journal of Power Sources Barai 280 0 74 2015 10.1016/j.jpowsour.2015.01.097 A study on the impact of lithium-ion cell relaxation on electrochemical impedance spectroscopy
10.4271/2016-01-1290 Groenewald, J., J. Marco, N. Higgins, and A. Barai, In-Service EV Battery Life Extension Through Feasible Remanufacturing. SAE Technical Paper 2016-01-1290, 2016.
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