Stochastic Nonlinear Model Predictive Control of Battery-Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles

In this paper, a stochastic nonlinear model predictive control (S-NMPC) approach has been illustrated for the energy management of a battery/SC hybrid energy storage systems (HESSs) in a Toyota Rav4EV. As the performance of predictive controllers in these systems is highly dependent on the future power demand estimation, a stochastic approach has been adopted in order to include uncertainties in the driver's power demand prediction, exploiting the ideas of the two-stage stochastic programming method. The power demand is modeled as a Markov chain and has been trained with several standard and real-world driving cycles. Model-in-the-Loop (MIL) simulation results have been presented over a driving cycle different from that used as training data. The results of the S-NMPC method has been compared against other deterministic approaches.