A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control With Road Grade Preview

Abstract

The energy efficiency of parallel hybrid electric vehicles (HEVs) can degrade significantly when the battery state-of-charge (SoC) reaches its boundaries. The road grade has a great influence on the HEV battery charging and discharging processes, and therefore the HEV energy management can be benefited from the road grade preview. In real-world driving, the road grade ahead can be considered as a random variable because the future route is not always available to the HEV controller. This brief proposes a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs running in hilly regions with light traffic. The strategy does not require a determined route being known in advance. The road grade is modeled as a Markov chain and stochastic HEV fuel consumption and battery SoC models are developed. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming. The proposed method is evaluated in simulation and compared with an equivalent consumption minimization strategy and the dynamic programming results. It is shown that the developed method can help maintaining the battery SoC within its boundaries and achieve good energy consumption performance.