Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor

As one of promising solutions towards future cleaner transportation, fuel cell electric vehicles have been widely regarded as an attractive technology in both academia and industry. To enhance the vehicle's operation efficiency, this paper proposes a multi-criteria power allocation strategy for a fuel cell/battery-based plug-in hybrid electric vehicle. Firstly, an adaptive online-learning enhanced Markov velocity-forecast approach is proposed. Its predictive behaviors can be adjusted accordingly under various driving scenarios through the real-time-identified transition probability matrices. Subsequently, based only on the previewed trip duration information and the speed prediction results, a state-of-charge (SOC) reference planning approach is designed to guide the allocation of battery energy. Combining with the velocity-forecast results and the reference SoC, model predictive control derives the optimal power-allocation decision through minimizing the multi-purpose objective function in a finite time horizon. It has been verified that (1) the presented power allocation strategy can reduce over 12.05% H2 consumption and over 94.40% fuel cell power spikes against the commonly used Charge-Depleting/Charge-Sustaining strategy; (2) despite the existence of mission time estimation errors, the presented control strategy could still bring performance enhancement over the benchmark strategy, thus demonstrating its feasibility for real-world implementations.