A novel energy management for hybrid off-road vehicles without future driving cycles as a priori

Hong Wang, Yanjun Huang, Amir Khajepour, Hongwen He and Dongpu Cao

Energy, 2017, vol. 133, issue C, 929-940

Abstract: Hybrid electric tracked bulldozers use engine and ultracapacitor as the power sources for propulsion, and the fuel economy performance highly depend on the coordination of all subsystems. In this paper, a model predictive controller is developed to reduce the fuel consumption of hybrid electric tracked bulldozers. As an optimization-based approach, the model predictive controller usually requires the drive profile to be known a priori. However, in this study, an average concept based model predictive controller is proposed without such knowledge. Simulation results show that a prescient model predictive controller saves approximately 21% more fuel compared to the conventional bulldozer and the average concept based model predictive controller performs similarly to the prescient model predictive controller. Meanwhile, the results of the two model predictive controllers are compared with dynamic programming and rule-based energy management strategy to show the benefit of model predictive controllers. In addition, the robustness of this average concept based model predictive controller is also verified under several disturbed drive cycles. The proposed model predictive controller is independent of powertrain topology such that it can be directly extended to other types of hybrid electric tracked bulldozers, and it provides a way to apply the model predictive controller even though future driving information is unavailable.

Keywords: Hybrid electric tracked bulldozer; Energy management; Model predictive control; Dynamic programming; Rule-based; Robustness (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:133:y:2017:i:c:p:929-940

DOI: 10.1016/j.energy.2017.05.172

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