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Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

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Abstract

This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.

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Abbreviations

b e :

brake specific fuel consumption of engine, g/kWh

k hi :

coefficient of upper limit of engine optimal torque, %

k low :

coefficient of lower limit of engine optimal torque, %

SOC tar :

target value of SOC, %

SOC max :

allowable upper limit of SOC tar range, %

SOC min :

allowable lower limit of SOC tar range, %

T e :

engine torque, Nm

T m :

electric motor torque, Nm

T req :

torque requirement of tire, Nm

T lo_optT :

torque of engine optimal lower limit, Nm

T hi_optT :

torque of engine optimal upper limit, Nm

v r :

velocity of entering the regenerative braking mode, km/h

v 0 :

velocity of activating engine, km/h

w e :

engine speed, rpm

w m :

electric motor speed, rpm

η 0 :

transmission efficiency of i 0, %

η m :

transmission efficiency of i m, %

η e :

transmission efficiency of i e, %

η 0 :

transmission efficiency of i 0, %

ΔSOC :

SOC gap between SOC tar and SOC max (SOC min), %

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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China

    B. M. Duan, Q. N. Wang, J. N. Wang & T. Ba

  2. College of Communication Engineering, Jilin University, Changchun, 130022, China

    X. N. Li

Authors
  1. B. M. Duan
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  2. Q. N. Wang
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  3. J. N. Wang
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  4. X. N. Li
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  5. T. Ba
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Correspondence to J. N. Wang.

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Duan, B.M., Wang, Q.N., Wang, J.N. et al. Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle. Int.J Automot. Technol. 18, 335–344 (2017). https://doi.org/10.1007/s12239-017-0034-4

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  • DOI: https://doi.org/10.1007/s12239-017-0034-4

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