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Jie Fan, Yuan Zou, Zehui Kong, Ludger Heide. Cloud Computing Based Optimal Driving for a Parallel Hybrid Electric Vehicle[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 155-161. doi: 10.15918/j.jbit1004-0579.17160
Citation: Jie Fan, Yuan Zou, Zehui Kong, Ludger Heide. Cloud Computing Based Optimal Driving for a Parallel Hybrid Electric Vehicle[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 155-161.doi:10.15918/j.jbit1004-0579.17160
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Cloud Computing Based Optimal Driving for a Parallel Hybrid Electric Vehicle

doi:10.15918/j.jbit1004-0579.17160

  • National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2017-11-13
    Abstract
  • A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation. Based on principles of vehicle dynamics, the driving optimization problem has been formulated into an optimal control problem constrained by traffic rules, directed at achieving lower equivalent fuel consumption and shorter travel time. In order to conveniently specify the constraints and facilitate the application of the dynamic programming (DP) algorithm, the driving optimization problem is transformed into spatial domain and discretized properly. Considering the heavy computational costs of the DP algorithm, a cloud computing based platform structure is proposed to solve the optimal driving problem in real-time. A case study is simulated based on a real-world traffic scenario in Matlab. Simulation results demonstrate that the cloud computing framework is promising toward realizing the real-time energy management for hybrid electric vehicles.
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