International Journal of Automotive Technology

, Volume 20, Issue 5, pp 1009–1022 | Cite as

Adaptive Control Strategy Extracted from Dynamic Programming and Combined with Driving Pattern Recognition for SPHEB

  • Xinyou LinEmail author
  • Hailin Li


An appropriate control strategy can play an important role in further improving the fuel economy performance of hybrid electric vehicle (HEV). This research developed a novel adaptive control strategy to achieve optimal power distribution for a series-parallel hybrid electric bus (SPHEB) to adapt driving pattern instantaneously. First, a methodology of extracting mode transition control and power distribution strategy from dynamic programming (DP) solution is proposed for the development of the hierarchical energy management strategy. A SPHEB energy management problem under the Chinese typical bus driving schedule at urban district (CTBDS_UD) is investigated as a case study. Second, an approach of driving pattern recognition (DPR) module is developed. For adaptive learning, four typical driving patterns are selected as the database of driving condition and using the extraction method described above to acquire optimal control strategies for four driving patterns. Third, a framework of adaptive control strategy has been proposed based on the extracted hierarchical energy management strategy from DP and combined with DPR. Finally, the simulation results demonstrate the proposed adaptive strategy can make power distribution proper adjustments in real time and be capable of improving significantly the fuel efficiency of the SPHEB.

Key words

Hybrid electric vehicle Adaptive control strategy Driving pattern recognition Dynamic programming Multivariable nonlinear regression Learning vector quantization 


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The authors greatly appreciate the financial supported both from the National Natural Science Fund of China (Grant No. 51505086) and China Scholarship Council. This work also has been developed as Visiting Scholar at Center for Alternative Fuels, Engines and Emissions of the West Virginia University.


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Copyright information

© KSAE 2019

Authors and Affiliations

  1. 1.College of Mechanical Engineering and AutomationFuzhou UniversityFuzhouChina
  2. 2.Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic ControlFujian Province UniversityFuzhouChina
  3. 3.Department of Mechanical and Aerospace EngineeringWest Virginia UniversityMorgantownUSA

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