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PROJECT TITLE :

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

ABSTRACT:

The energy potency of parallel hybrid electric vehicles (HEVs) can degrade considerably when the battery state-of-charge (SoC) reaches its boundaries. The road grade encompasses a nice influence on the HEV battery charging and discharging processes, and thus the HEV energy management will be benefited from the road grade preview. In real-world driving, the road grade ahead can be thought-about as a random variable because the future route is not continually obtainable to the HEV controller. This transient proposes a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain data of the area for HEVs running in hilly regions with light traffic. The strategy will not require a determined route being known beforehand. 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 method and solved using stochastic dynamic programming. The proposed methodology is evaluated in simulation and compared with the same consumption minimization strategy and therefore the dynamic programming results. It's shown that the developed technique can facilitate maintaining the battery SoC among its boundaries and achieve good energy consumption performance.


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