Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration

Mohd Azrin Mohd Zulkefli, Jianfeng Zheng, Zongxuan Sun, Henry X. Liu

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps' car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin's Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG's improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case.

Original languageEnglish (US)
Pages (from-to)41-63
Number of pages23
JournalTransportation Research Part C: Emerging Technologies
Volume45
DOIs
StatePublished - Jan 1 2014

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Hybrid powertrains
Trajectories
infrastructure
communication
Communication
communications
Powertrains
traffic
energy
Energy management
Fuel economy
simulation
economy
interaction
management
Railroad cars

Keywords

  • Fuel consumption reduction
  • Hybrid powertrain optimization
  • Intelligent transportation system
  • Inter-vehicle communication
  • Trajectory prediction

Cite this

Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration. / Mohd Zulkefli, Mohd Azrin; Zheng, Jianfeng; Sun, Zongxuan; Liu, Henry X.

In: Transportation Research Part C: Emerging Technologies, Vol. 45, 01.01.2014, p. 41-63.

Research output: Contribution to journalArticle

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abstract = "Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps' car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin's Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6{\%} average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7{\%} improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG's improvements, with 1.6{\%} to 4.3{\%} improvements for the 6-mile case and 2.6{\%} to 3.4{\%} improvements for the 15-mile case.",
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