Global optimal energy management strategy research for hybrid electric off-road vehicles
| dc.contributor.author | Lyu, C. | |
| dc.date.accessioned | 2017-02-13T16:04:25Z | |
| dc.date.available | 2017-02-13T16:04:25Z | |
| dc.date.issued | 2017-03 | |
| dc.description.abstract | Energy management strategy influences the power performance and fuel economy of series hybrid electric tracked bulldozers greatly. In this paper, we will present a procedure for the design of a rule-based power management strategy by defining a cost function, such as minimizing the fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, dynamic programming algorithm is then utilized to determine the optimal control actions for a series hybrid powertrain in order to minimize fuel consumption, which can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that there is an approximately 6.8%improvement in fuel economy. Index Terms- Series hybrid electric tracked vehicle, power management strategy, rule-based, dynamic programming.Energy management strategy influences the power performance and fuel economy of series hybrid electric tracked bulldozers greatly. In this paper, we will present a procedure for the design of a rule-based power management strategy by defining a cost function, such as minimizing the fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, dynamic programming algorithm is then utilized to determine the optimal control actions for a series hybrid powertrain in order to minimize fuel consumption, which can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that there is an approximately 6.8%improvement in fuel economy. Index Terms- Series hybrid electric tracked vehicle, power management strategy, rule-based, dynamic programming. Paper originally presented at the WCX17: SAE World Congress Experience, Detroit, 4 – 6 April 2017. | en_UK |
| dc.identifier.citation | Wang H, Huang Y, Lv C, Khajepour A, Global Global optimal energy management strategy research for hybrid electric off-road vehicles, SAE International Journal of Commercial Vehicles, Vol. 10 Issue 2, article number 2017-01-0425. | en_UK |
| dc.identifier.issn | 1946-391X | |
| dc.identifier.uri | https://saemobilus.sae.org/content/2017-01-0425 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/11438 | |
| dc.language.iso | en | en_UK |
| dc.publisher | SAE International | en_UK |
| dc.rights | Published by SAE International. This is the Author Accepted Manuscript. Please refer to any applicable publisher terms of use. | |
| dc.subject | Series hybrid electric tracked vehicle | en_UK |
| dc.subject | Power management strategy | en_UK |
| dc.subject | Dyanmic programming | en_UK |
| dc.subject | Rule-based | en_UK |
| dc.title | Global optimal energy management strategy research for hybrid electric off-road vehicles | en_UK |
| dc.type | Article | en_UK |