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Online Multi Chemistry SoC Estimation Technique Using Data Driven Battery Model Parameter Estimation

ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
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Paper presented at EVS30 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Stuttgart, Germany, 9–11 October 2017.
Paper presented at EVS30 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Stuttgart, Germany, 9–11 October 2017.
World Electr. Veh. J. 2018, 9(2), 16; https://doi.org/10.3390/wevj9020016
Received: 8 May 2018 / Revised: 15 June 2018 / Accepted: 20 June 2018 / Published: 22 June 2018
Kalman filters have shown to be a very accurate and robust method for State of Charge estimation. However, their performance depends heavily on the accuracy of the used battery model and its parameters. These battery model parameters have shown to vary with the State of Health, cell chemistry, temperature and load current. This paper studies a data driven battery model parameter estimation technique based on the recursive least squares method as an alternative to extensively characterizing every cell of interest with time-consuming test procedures. The performance of two commonly used electrical models is compared and extensively validated on three different cell chemistries (Nickel Cobalt Manganese, Lithium Iron Phosphate and Lithium Titanate Oxide), under load conditions of varying dynamic nature representative for electric vehicle (EV) applications, using a Dynamic Discharge Pulse Test (DDPT) and the Worldwide harmonized Light vehicles Test Procedure (WLTP). The developed model is able to identify and update battery model parameters online, for three different chemistries, potentially reducing offline characterization efforts and allowing monitoring of battery electrical behavior and state estimation over its entire lifetime. View Full-Text
Keywords: battery model; battery management system; electric vehicle; modeling; state of charge battery model; battery management system; electric vehicle; modeling; state of charge
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De Sutter, L.; Nikolian, A.; Timmermans, J.-M.; Omar, N.; Van Mierlo, J. Online Multi Chemistry SoC Estimation Technique Using Data Driven Battery Model Parameter Estimation. World Electr. Veh. J. 2018, 9, 16.

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