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Application of neural network to downhill shift strategy for automatic transmission

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Abstract

A method is proposed to estimate the longitudinal road gradient with a concept “general gradient force (GGF)”, in which uncertain factors such as additional vertical load, road surface change, and strong wind are also taken into account. An adaptive downhill shift control system is then developed to help driver to use the engine brake with lower gears while downhill driving. In the adaptive system, a three-layer neural network is built to evaluate the necessity to make use of engine brake capability in current downhill situation, and the neural network is trained with samples from experienced drivers. Field test results of the adaptive system are introduced to verify the effectiveness of the approach mentioned above.

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Authors and Affiliations

  1. State Key Laboratory Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai, 200240, China

    Yao Li  (李 尧) & Fan Yu  (喻 凡)

  2. National Automotive Electronics Engineering Laboratory, Shanghai Jiaotong University, Shanghai, 200240, China

    Chen Wu  (吴 晨)

Authors
  1. Yao Li  (李 尧)
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  2. Fan Yu  (喻 凡)
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  3. Chen Wu  (吴 晨)
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Corresponding author

Correspondence to Yao Li  (李 尧).

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Li, Y., Yu, F. & Wu, C. Application of neural network to downhill shift strategy for automatic transmission. J. Shanghai Jiaotong Univ. (Sci.) 15, 498–503 (2010). https://doi.org/10.1007/s12204-010-1039-6

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  • DOI: https://doi.org/10.1007/s12204-010-1039-6

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