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Control Strategy for Shift Schedule Correction Based on Driving Habits for Vehicles with Automatic Transmission

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Abstract

A shift schedule modification program is an intelligent system for automatic transmission. This program can adjust shift points to cater to drivers with different driving habits. An important prerequisite in designing a personalized shift schedule is identifying the driving habits of drivers. In this study, we developed an identification algorithm based on wavelet neural network and Bayesian fusion decision-making. First, a system for identifying driving styles was established based on the wavelet neural network. Second, the results were integrated by Bayesian fusion decision-making to obtain the driving habits. Finally, different correction coefficients were selected based on driving habits to satisfy the requirements of drivers. Experimental results show that the driving habits can be accurately identified based on wavelet neural network and Bayesian fusion decision-making, and the correction control strategy can rectify the shift schedule effectively. The correction control strategy satisfies the requirements of different drivers for vehicle performance and enhances the intelligence of automatic transmission.

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Abbreviations

M :

number of input layer nodes

L :

number of hidden layer nodes

N :

number of output layer nodes

K :

type of driving style

ωmn :

n-th identification result in m-th effective working condition

γ :

style tolerance

ε :

probability tolerance

l s :

normalization coefficient

V :

vehicle speed at shift, km/h

v d :

power shift speed, km/h

v e :

economic shift speed, km/h

a :

dynamic coefficient

b :

economic coefficient

T ed :

engine dynamic output torque, N.m

T e :

engine steady-state output torque, N.m

λ :

decreasing coefficient

W e :

angular speed of the engine output crankshaft, rad/s

T out :

transmission output torque, N.m

i g :

transmission ratio

η T :

efficiency of the transmission system

F f :

rolling resistance, N.m

F w :

air resistance, N.m

F i :

slope resistance, N.m

F j :

acceleration resistance, N.m

C D :

air resistance coefficient

A :

vehicle frontal area, m2

v a :

vehicle speed, km/h

f 0 :

rolling resistance coefficient

σ :

mass conversion factor

i 0 :

transmission ratio of the main reducer

F t :

driving force, N.m

R :

effective rolling radius, m

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Acknowledgement

This research was supported by the National Natural Science Foundation of China (Grant No. 51875151), Key R&D projects of Anhui Province (Grant No. 2017030701043 and 1804a09020017) and National Key R&D Program of China (Grant No. 2016YFD0700604).

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Authors and Affiliations

  1. Automotive Research Institute of Hefei University of Technology, Hefei University of Technology, Hefei, 230009, China

    Guang Xia, Jun Gao, Shaojie Wang & Baoqun Sun

  2. The Collegy of Electronic Engineering, National University of Defense Technology, Hefei, 230037, China

    Xiwen Tang

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  1. Guang Xia
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  2. Jun Gao
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  3. Xiwen Tang
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  4. Shaojie Wang
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Correspondence to Guang Xia.

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Xia, G., Gao, J., Tang, X. et al. Control Strategy for Shift Schedule Correction Based on Driving Habits for Vehicles with Automatic Transmission. Int.J Automot. Technol. 21, 407–418 (2020). https://doi.org/10.1007/s12239-020-0038-3

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  • DOI: https://doi.org/10.1007/s12239-020-0038-3

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