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

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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes two strategies for predicting the power demand of the traction system in a hybrid energy storage system (HESS) in electric vehicles (EV). The strategies are based on an autoregressive (AR) model and a Kalman Filter scheme. The results show that using a Kalman filter with an AR model significantly improves the accuracy of power demand prediction and battery health preservation when applied in a nonlinear model predictive control (NMPC) strategy for power allocation.