dc.contributor.author | Wang, Hong | |
dc.contributor.author | Huang, Yanjun | |
dc.contributor.author | Khajepour, Amir | |
dc.contributor.author | Song, Qiang | |
dc.date.accessioned | 2017-03-30 19:04:35 (GMT) | |
dc.date.available | 2017-03-30 19:04:35 (GMT) | |
dc.date.issued | 2016-11-15 | |
dc.identifier.uri | https://doi.org/10.1016/j.apenergy.2016.08.085 | |
dc.identifier.uri | http://hdl.handle.net/10012/11619 | |
dc.description | The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.apenergy.2016.08.085 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
dc.description.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. | en |
dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada (NSERC) || Ontario Research Fund | en |
dc.language.iso | en | en |
dc.publisher | Elsevier | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Series hybrid electric tracked bulldozer | en |
dc.subject | Energy management strategy | en |
dc.subject | Model predictive control | en |
dc.subject | Rule-based | en |
dc.subject | Dynamic programming | en |
dc.subject | Robustness | en |
dc.title | Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle | en |
dc.type | Article | en |
dcterms.bibliographicCitation | Wang, H., Huang, Y., Khajepour, A., & Song, Q. (2016). Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle. Applied Energy, 182, 105–114. https://doi.org/10.1016/j.apenergy.2016.08.085 | en |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.contributor.affiliation2 | Mechanical and Mechatronics Engineering | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Reviewed | en |
uws.scholarLevel | Faculty | en |