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Range-extender In-the-loop Method for Fuel Consumption Prediction of Hybrid Electric Vehicles

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Abstract

In the development of a range-extended electric vehicle, it is necessary to determine the control strategy of the range extender (REX) and evaluate the influence of its performance and operating conditions. In this paper, we propose a range-extender in-the-loop (REIL) method that is used to predict the vehicle fuel consumption more accurately. To develop the REIL method and perform experimental verification, the vehicle plant model, electric-drive control strategy, and REX control strategy are integrated into a vehicle control unit (VCU) for REX control, bench data acquisition, and bench monitoring. The VCU controls the REX to meet the power requirements of the simulated vehicle operations under a given driving cycle. The fuel consumed by the engine and power generated by the integrated starter-and-generator are measured during the tests, which are used in the vehicle fuel consumption calculation. We first describe the methodology and implementation of the REIL, and subsequently discuss the predicted results of a range-extended electric vehicle equipped with a 30 kW REX under the WLTC driving cycle. Comparing the predicted results of the REIL and software simulation methods, it is demonstrated that the REIL method improves the prediction accuracy of vehicle fuel consumption.

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Abbreviations

BMS:

battery management system

CCP:

CAN calibration protocol

CD:

charge depleting

CS:

charge maintaining

ECMS:

equivalent consumption minimization strategy

ECU:

engine control unit

ESFC:

electric-power specific fuel consumption

GCU:

generator control unit

HIL:

hardware-in-loop

ISG:

integrated starter-and-generator

TM:

traction motor

MCU:

motor control unit

MIL:

model-in-loop

PHEV:

plug-in hybrid electric vehicle

PID:

proportion integration differentiation

REEV:

range-extended electric vehicle

REIL:

range extender in-loop

REX:

range extender

SOC:

state of charge

VCU:

vehicle control unit

VFC:

vehicle fuel consumption

WLTC:

worldwide harmonized light vehicles test cycle

References

  • Bassett, M., Hall, J., Kennedy, G., Cains, T., Powell, J. and Warth, M. (2013). The development of a range extender electric vehicle demonstrator. SAE Paper No. 2013-01-1469.

  • Gao, J. P., Zhu, G. G., Strangas, E. G. and Sun, F. C. (2009). Equivalent fuel consumption optimal control of a series hybrid electric vehicle. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 223, 8, 1003–1018.

    Google Scholar 

  • Guan, J. C., Chen, B. C. and Wu, Y. Y. (2019). Design of an adaptive power management strategy for range extended electric vehicles. Energies 12, 9, 1610.

    Article  Google Scholar 

  • Han, Z. Y., Wu, Z. K. and Gao, X. J. (2019). Development trend of internal combustion engines in the revolution of automotive powertrain. J. Automotive Safety and Energy 10, 2, 146–160.

    Google Scholar 

  • Ide, H., Sunaga, Y. and Higuchi, N. (2013). Development of SPORT HYBRID i-MMD control system for 2014 model year accord. Honda R&D Technical Review 25, 2, 33–41.

    Google Scholar 

  • Jiang, D. D., Li, D. F. and Yu, X. L. (2020). Energy management of HEV based on hybrid model predictive control. J. Jilin University. Engineering and Technology Edition, 50, 1217–1226.

    Google Scholar 

  • Kalia, A. V. and Fabien, B. C. (2020). On implementing optimal energy management for EREV using distance constrained adaptive real-time dynamic programming. Electronics 9, 2, 228.

    Article  Google Scholar 

  • Li, J., Zhou, Q., Williams, H. and Xu, H. (2019). Back-to-back competitive learning mechanism for fuzzy logic based supervisory control system of hybrid electric vehicles. IEEE Trans. Industrial Electronics 67, 10, 8900–8909.

    Article  Google Scholar 

  • Lin, X., Feng, Q. and Zhang, S. (2016). Global optimal discrete equivalent factor of equivalent fuel consumption minimization strategy based energy management strategy for a series-parallel plug-in hybrid electric vehicle. J. Mechanical Engineering 52, 20, 102–110.

    Article  Google Scholar 

  • Liu, H., Li, X., Wang, W., Han, L. and Xiang, C. (2018). Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles. Energy, 152, 427–444.

    Article  Google Scholar 

  • Liu, T., Tang, X., Wang, H., Yu, H. and Hu, X. (2019). Adaptive hierarchical energy management design for a plug-in hybrid electric vehicle. IEEE Trans. Vehicular Technology 68, 12, 11513–11522.

    Article  Google Scholar 

  • Marinescu, A., Taylor, A., Clarke, S., Serban, I. and Marinescu, C. (2019). Optimising residential electric vehicle charging under renewable energy: Multi-agent learning in software simulation and hardware-in-the-loop evaluation. Int. J. Energy Research 43, 8, 3853–3868.

    Article  Google Scholar 

  • Paganelli, G., Delprat, S., Guerra, T. M., Rimaux, J. and Santin, J. J. (2002). Equivalent consumption minimization strategy for parallel hybrid powertrains. IEEE 55th Vehicular Technology Conf. (VTC), Birmingham, Alabama, USA.

  • Raikwar, S., Wani, L. J., Kumar, S. A. and Rao, M. S. (2019). Hardware-in-the-Loop test automation of embedded systems for agricultural tractors. Measurement, 133, 271–280.

    Article  Google Scholar 

  • Rezaei, A., Burl, J. B., Solouk, A., Zhou, B., Rezaei, M. and Shahbakhti, M. (2017). Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles. Applied Energy, 208, 655–665.

    Article  Google Scholar 

  • Shi, D., Wang, S., Cai, Y. and Chen, L. (2018). Stochastic predictive energy management of power split hybrid electric bus for real-world driving cycles. IEEE Access, 6, 61700–61713.

    Article  Google Scholar 

  • Solouk, A., Tripp, J., Shakiba-Herfeh, M. and Shahbakhti, M. (2017). Fuel consumption assessment of a multi-mode low temperature combustion engine as range extender for an electric vehicle. Energy Conversion and Management, 148, 1478–1496.

    Article  Google Scholar 

  • Sun, C., Hu, X., Moura, S. J. and Sun, F. (2014). Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Trans. Control Systems Technology 23, 3, 1197–1204.

    Google Scholar 

  • Wang, Y. (2019). A study of regenerative braking force distribution strategy for battery electric vehicle based on ECE R13. Mechanical Engineering & Automation, 212, 21–23.

    Google Scholar 

  • Yang, C., Jiao, X., Li, L., Zhang, Y. and Chen, Z. (2018). A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle. Mechanical Systems and Signal Processing, 99, 326–344.

    Article  Google Scholar 

  • Zhao, M., Shi, J. and Lin, C. (2019). Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus. Applied Energy, 243, 21–34.

    Article  Google Scholar 

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Acknowledgement

This work is supported by funding from Tongji University (No. 17092450110) and from Nanchang Automotive Innovation Institute (No. 17002380039).

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Authors and Affiliations

  1. School of Automotive Studies, Tongji University, Shanghai, 201804, China

    Yongzheng Sun, Zhiyu Han, Jian Feng & Zhenkuo Wu

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  1. Yongzheng Sun
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  2. Zhiyu Han
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  3. Jian Feng
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  4. Zhenkuo Wu
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Correspondence to Zhiyu Han.

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Sun, Y., Han, Z., Feng, J. et al. Range-extender In-the-loop Method for Fuel Consumption Prediction of Hybrid Electric Vehicles. Int.J Automot. Technol. 24, 91–103 (2023). https://doi.org/10.1007/s12239-023-0009-6

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  • DOI: https://doi.org/10.1007/s12239-023-0009-6

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