Real-Time Energy Management Strategy of Multi-Wheel Electric Drive Vehicles With Load Power Prediction Function

Abstract

The lack of a load power prediction function in conventional multi-wheel electric drive vehicles leads to lags in control action. To address this issue, we developed a real-time energy management strategy with improved load power prediction accuracy. Based on an assessment of the overall vehicle structure, a mathematical model for each power source was established using theoretical analysis and data fitting. A method for the joint prediction of non-stationary load power combining Kalman filter and Markov chain forecasting methods was established, and a multi-objective optimization function was constructed under the nonlinear model predictive control framework. To enable real-time optimal control command, a sequential quadratic programming method in the finite time domain was applied. Finally, the multi-power source was optimized and coordinated. Multi-road driving experiments were carried out using a hardware-in-the-loop simulation platform. Comparisons of energy management control strategies with and without power prediction revealed that applying the former enhances the predictability of future load power, significantly optimizes the coordinated control of multiple power sources, improves vehicle fuel economy, and stabilizes bus voltage and battery state of charge. Moreover, it has specific reference significance in engineering application scenarios under conventional model predictive control.