Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle

Hong Wang, Yanjun Huang, Amir Khajepour and Qiang Song

Applied Energy, 2016, vol. 182, issue C, 105-114

Abstract: The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.

Keywords: Series hybrid electric tracked bulldozer; Energy management strategy; Model predictive control; Rule-based; Dynamic programming; Robustness (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (39)

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DOI: 10.1016/j.apenergy.2016.08.085

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