Robust Real-Time Energy Management Control Strategy based on the Prediction of Hybrid Vehicle's Futures States

The energy management of a hybrid vehicle consists in developing a strategy, which determines at each moment the distribution of thermal and electrical energy flows, minimizing the overall consumption of the vehicle. Hybrid vehicle consumption modeling makes it possible to write this problem in the form of a dynamic optimization problem under evolution constraints. This problem is solved optimally when all the rolling conditions are known a priori. Optimal control obtained serves as a reference for evaluating the performance of the strategies embedded in the vehicle. Based on the theory of optimal optimization, the proposed robust energy management control strategy based on the Prediction of hybrid vehicle's futures states were created. For plug-in hybrids, their energy capacity and the ability to recharge on the electricity grid release constraints in the problematic of energy optimization. That is why a new specific strategy has been developed with the aim of making the most of on-board electrical energy to minimize vehicle emissions. For all hybrid vehicles, the battery is a key component of aging changes its economic and energy profitability. This is why an Adaptive Extended Kalman Filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. This information is used by a specific strategy optimizing consumption while preserving the battery of extreme temperatures, harmful to its longevity.