Abstract
Intelligent energy management of hybrid electric vehicles is feasible with a priori information of route and driving conditions.Model Predictive Control (MPC) with finite horizon road grade preview has been proposed as a viable predictive energy management approach. We propose that our novel Distance Constrained- Adaptive Real Time Dynamic Programming (DC-ARTDP) approach can provide better energy management than MPC without any road grade information in context of an Extended RangeElectric Vehicle (EREV). In this article, we have evaluated and compared the MPC and DC-ARTDP energy management strategies for a real-world driving scenario. The simulations were conducted for a 160km drive with road grade variation between +4% and -1%. Results show that the DC-ARTDP approach is optimal and at max 4.25% better than the simple MPC with a finite horizon road grade preview implementation. Additionally, a higher value for energy storage system SOC tracking penalty p2, results in the net energy consumption for MPC to converge towards that of DC-ARTDP. A combination of the MPC and DC-ARTDP approach is also evaluated with only 1.25% maximum improvement over simple MPC.
Hybrid model predictive control for optimal energy management of a smart house
