Short-term power demand prediction for energy management of an electric vehicle based on batteries and ultracapacitors

• Published in: Energy (Oxford) Vol. 247; p. 123430
• Main Authors: Asensio, E. Maximiliano, Magallán, Guillermo A., Pérez, Laura, De Angelo, Cristian H.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2022
• Subjects: Battery; Electric vehicles; Energy management; Model predictive control; Power demand prediction; Ultracapacitor; Model predictive control; Electric vehicles; Ultracapacitor; Energy management; Battery; Power demand prediction
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2022.123430

Model predictive control applied to energy management of hybrid energy storage system (HESS) in electric vehicles (EV) requires a proper knowledge of the power demanded by the traction system. As a key point of this work, two strategies to predict the power demand profile based on an autoregressive (AR) model and a Kalman Filter scheme are proposed. It is shown that using a Kalman filter with an AR model to predict the power demand, an error of 0.2% is achieved for the first prediction compared to 1.4% obtained for the case in which the power demand is considered constant on a standard drive cycle. These strategies are used to implement a nonlinear model predictive control (NMPC) strategy for the power split of a HESS based on batteries and Ultracapacitor (UC) in an EV. To preserve the health of the battery, a cost function is proposed to minimize large and highly variant battery currents. Regarding the cost of battery degradation, it is shown that the proposed strategies obtain results comparable to the ideal case in which the required power is fully known. •Kalman filter based on AR model satisfactorily predicts required power.•The periodic update of the coefficients of the AR model gives good predictions.•NMPC with proper demand prediction strategies helps to preserve battery life.