Print Email Facebook Twitter Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains Title Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains Author Roşca, B. Wilkins, S. Jacob, J. Hoedemaekers, E.R.G. van den Hoek, S.P. Publication year 2014 Abstract This paper presents a method of predicting the maximum power capability of a Li-Ion battery, to be used for electric motor control within automotive powertrains. As maximum power is highly dependent on battery state, the method consists of a pack level state observer coupled with a predictive battery model. Results indicate that the battery state estimation algorithm can estimate a cell State-of-Charge (SoC) within 3%, while pack level simulations show how this method can be enhanced to provide battery pack level estimates, correctly capturing the spread in terms of State of Charge of the cells within the pack, which is essential for accurate maximum power prediction. Tests show that the maximum battery power varies significantly with SoC. At an ambient temperature of 20°C, as much as a three-fold decrease in power capability is measured for charging power, at SoC values above 90%, and discharging power, at SoC values under 20%. The maximum power prediction algorithm presented in this study is able to correctly predict the maximum battery power over the complete operating range of SoC, at 20°C. Low temperature maximum discharging power tests were carried out, to investigate electric vehicle cold start scenarios. The tests show a strong impact of temperature on the power which can be withdrawn from the battery. At 35% SoC, 2.5 times less power can be withdrawn from the battery at a temperature of 0°C, compared to 20°C. cop. 2014 IEEE. Subject Fluid Mechanics Chemistry & EnergeticsPT - Power TrainsTS - Technical SciencesPhysicsMobilityBattery power predictionBattery management systemsBMSEKFLi-ion batteryPowertrain ControlSoCState-of-Charge To reference this document use: http://resolver.tudelft.nl/uuid:d3a06fbe-bc9a-4539-a45a-89a750bd3c70 DOI https://doi.org/10.1109/ievc.2014.7056166 TNO identifier 527045 Publisher Institute of Electrical and Electronics Engineers Inc. ISBN 9781479960750 Source 2014 IEEE International Electric Vehicle Conference, IEVC 2014 Article number 7056166 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.