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Adaptive Control Strategy Extracted from Dynamic Programming and Combined with Driving Pattern Recognition for SPHEB

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Abstract

An appropriate control strategy can play an important role in further improving the fuel economy performance of hybrid electric vehicle (HEV). This research developed a novel adaptive control strategy to achieve optimal power distribution for a series-parallel hybrid electric bus (SPHEB) to adapt driving pattern instantaneously. First, a methodology of extracting mode transition control and power distribution strategy from dynamic programming (DP) solution is proposed for the development of the hierarchical energy management strategy. A SPHEB energy management problem under the Chinese typical bus driving schedule at urban district (CTBDS_UD) is investigated as a case study. Second, an approach of driving pattern recognition (DPR) module is developed. For adaptive learning, four typical driving patterns are selected as the database of driving condition and using the extraction method described above to acquire optimal control strategies for four driving patterns. Third, a framework of adaptive control strategy has been proposed based on the extracted hierarchical energy management strategy from DP and combined with DPR. Finally, the simulation results demonstrate the proposed adaptive strategy can make power distribution proper adjustments in real time and be capable of improving significantly the fuel efficiency of the SPHEB.

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References

  • Bayindir, K. C., Gozukucuk, M. A. and Teke, A. (2011). A comprehensive overview of hybrid electric vehicle: Powertrain configurations, powertrain control techniques and electronic control units. Energy Conversion and Management 52, 2, 1305–1313.

    Article  Google Scholar 

  • Bianchi, D., Rolando, L. and Serrao, L. (2010). A rule-based strategy for a series/parallel hybrid electric vehicle: An approach based on dynamic programming. Proc. ASME Dynamic Systems and Control Conf., Cambridge, Massachusetts, USA.

    Google Scholar 

  • Borhan, H., Vahidi, A., Phillips, A. M., Kuang, M. L., Kolmanovsky, I. V. and Cairano, S. D. (2012). MPC-based energy management of a power-split hybrid electric vehicle. IEEE Trans. Control Systems Technology 20, 3, 593–603.

    Article  Google Scholar 

  • Cairano, S. D., Bernardini, D., Bemporad, A. and Kolmanovsky, I. V. (2014). Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management. IEEE Trans. Control Systems Technology 22, 3, 1018–1031.

    Article  Google Scholar 

  • Chen, B., Wu, Y. and Tsai, H. (2014b). Design and analysis of power management strategy for range extended electric vehicle using dynamic programming. Applied Energy, 113, 1764–1774.

    Article  Google Scholar 

  • Chen, S., Hung, Y., Wu, C. and Huang, S. (2015). Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization. Applied Energy, 160, 132–145.

    Article  Google Scholar 

  • Chen, Z. and Mi, C. C. (2009). An adaptive online energy management controller for power split HEV based on dynamic programming and fuzzy logic. Proc. IEEE Vehicle Power and Propulsion Conf., Dearborn, Michigan, USA.

    Google Scholar 

  • Chen, Z., Mi, C. C., Xiong, R., Xu, J. and You, C. (2014a). Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming. J. Power Sources, 248, 416–426.

    Article  Google Scholar 

  • Chen, Z., Xiong, R. and Cao, J. (2016). Particle swarm optimization-based optimal power management of plugin hybrid electric vehicles considering uncertain driving conditions. Energy, 96, 197–208.

    Article  Google Scholar 

  • Elbert, P., Ebbesen, S. and Guzzella, L. (2013). Implementation of dynamic programming for dimensional optimal control problems with final state constraints. IEEE Trans. Control Systems Technology 21, 3, 924–931.

    Article  Google Scholar 

  • Elbert, P., Flankl, M., Onder, C. and Guzzella, L. (2014). Convex optimization for the energy management of hybrid electric vehicles considering engine start and gearshift costs. Energies 7, 2, 834–856.

    Article  Google Scholar 

  • Fares, D., Chedid, R., Panik, F., Karaki, S. and Jabr, R. (2015). Dynamic programming technique for optimizing fuel cell hybrid vehicles. Int. J. Hydrogen Energy 40, 24, 7777–7790.

    Article  Google Scholar 

  • Geng, B., Mills, J. K. and Sun, D. (2011). Energy management control of microturbine-powered plug-in hybrid electric vehicles using the telemetry equivalent consumption minimization strategy. IEEE Trans. Control Systems Technology 60, 9, 4238–4248.

    Google Scholar 

  • Han, J., Park, Y. and Kum, D. (2014). Optimal adaptation of equivalent factor of equivalent consumption minimization strategy for fuel cell hybrid electric vehicles under active state inequality constraints. J. Power Sources, 267, 491–502.

    Article  Google Scholar 

  • Hemi, H., Ghouili, J. and Cheriti, A. (2014). A real time fuzzy logic power management strategy for a fuel cell vehicle. Energy Conversion and Management, 80, 63–70.

    Article  Google Scholar 

  • Hou, C., Ouyang, M., Xu, L. and Wang, H. (2014). Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles. Applied Energy, 115, 174–189.

    Article  Google Scholar 

  • Jeon, S.-I., Jo, S.-T., Park, Y.-I. and Lee, J.-M. (2002). Multi-mode driving control of a parallel hybrid electric vehicle using driving pattern recognition. J. Dynamic Systems, Measurement, and Control 124, 1, 141–149.

    Article  Google Scholar 

  • Kum, D., Peng, H. and Bucknor, N. K. (2011). Supervisory control of parallel hybrid electric vehicles for fuel and emission reduction. J. Dynamic Systems, Measurement, and Control 133, 6, 061010-1–061010-10.

    Article  Google Scholar 

  • Langari, R. and Won, J.-S. (2005). Intelligent energy management agent for a parallel hybrid vehicle-part I: System architecture and design of the driving situation identification process. IEEE Trans. Vehicular Technology 54, 3, 925–934.

    Article  Google Scholar 

  • Li, C. and Liu, G. (2009). Optimal fuzzy power control and management of fuel cell/battery hybrid vehicles. J. Power Sources 192, 2, 525–533.

    Article  MathSciNet  Google Scholar 

  • Li, L., Yang, C., Zhang, Y., Zhang, L. P. and Song, J. (2015). Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus route. IEEE Trans. Vehicular Technology 64, 7, 2792–2803.

    Article  Google Scholar 

  • Lin, X. and Sun, D. (2012). Driving pattern recognition based on ECMS and its application to control strategy for a series-parallel hybrid electric bus. J. Hunan University (Natural Sciences) 39, 10, 43–49.

    MathSciNet  Google Scholar 

  • Martinez, J. S., Mulot, J., Harel, F., Hissel, D., Pera, M.-C., John, R. I. and Amiet, M. (2013). Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles. Engineering Applications of Artificial Intelligence 26, 7, 1772–1779.

    Article  Google Scholar 

  • Montazeri-Gh, M. and Mahmoodi-K, M. (2016). Optimized predictive energy management of plug-in hybrid electric vehicle based on traffic condition. J. Cleaner Production, 139, 935–948.

    Article  Google Scholar 

  • Murphey, Y. L., Park, J., Kiliaris, L., Kuang, M. L., Masrur, M. A., Phillips, A. M. and Wang, Q. (2013). Intelligent hybrid vehicle power control — Part II: Online intelligent energy management. IEEE Trans. Vehicular Technology 62, 1, 69–79.

    Article  Google Scholar 

  • Nzisabira, J., Louvigny, Y. and Duysinx, P. (2008). Ecoefficiency optimization of hybrid electric vehicle based on response surface method and genetic algorithm. Proc. EET-2008 European Ele-Drive Conf. and Int. Advanced Mobility Forum, Geneva, Switzerland.

    Google Scholar 

  • Onori, S., Serrao, L. and Rizzoni, G. (2010). Adaptive equivalent consumption minimization strategy for hybrid electric vehicles. Proc. ASME Dynamic Systems and Control Conf., Cambridge, Massachusetts, USA.

    Google Scholar 

  • Opila, D. F., Wang, X., McGee, R. and Grizzle, J. W. (2013). Real-time implementation and hardware testing of a hybrid vehicle energy management controller based on stochastic dynamic programming. J. Dynamic Systems, Measurement, and Control 135, 2, 021002–1–021002–11.

    Article  Google Scholar 

  • Overington, S. and Rajakaruna, S. (2015). High-efficiency control of internal combustion engines in blended charge depletion/charge sustenance strategies for plug-in hybrid electric vehicles. IEEE Trans. Vehicular Technology 64, 1, 48–61.

    Article  Google Scholar 

  • Park, J., Chen, Z., Kiliaris, L., Kuang, M. L., Masrur, M. A., Phillips, A. M. and Murphey, Y. L. (2009). Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion. IEEE Trans. Vehicular Technology 58, 9, 4741–4756.

    Article  Google Scholar 

  • Pei, D. and Leamy, M. J. (2013). Dynamic programming-informed equivalent cost minimization control strategies for hybrid-electric vehicles. J. Dynamic Systems, Measurement, and Control 135, 5, 051013-1–051013-12.

    Article  Google Scholar 

  • Peng, J., Fan, H., He, H. and Pan, D. (2015). A rule-based energy management strategy for a plug-in hybrid school bus based on a controller area network bus. Energies 8, 6, 5122–5142.

    Article  Google Scholar 

  • Peng, J., He, H. and Xiong, R. (2017). Rule based energy management strategy for a series-parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy 185, 2, 1633–1643.

    Article  Google Scholar 

  • Poursamad, A. and Montazeri, M. (2008). Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles. Control Engineering Practice 16, 7, 861–873.

    Article  Google Scholar 

  • Sabri, M. F. M., Danapalasingam, K. A. and Rahmat, M. F. (2016). A review on hybrid electric vehicles architecture and energy management strategies. Renewable and Sustainable Energy Reviews, 53, 1433–1442.

    Article  Google Scholar 

  • Silvas, E., Hofman, T., Murgovski, N., Etman, L. F. P. and Steinbuch, M. (2017). Review of optimization strategies for system-level design in hybrid electric vehicles. IEEE Trans. Vehicular Technology 66, 1, 57–70.

    Google Scholar 

  • Sorrentino, M., Rizzo, G. and Arsie, I. (2011). Analysis of a rule-based control strategy for onboard energy management of series hybrid vehicles. Control Engineering Practice 19, 12, 1433–1441.

    Article  Google Scholar 

  • Sun, D., Lin, X., Qin, D. and Deng, T. (2012). Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus. Chinese J. Mechanical Engineering 25, 6, 1161–1170.

    Article  Google Scholar 

  • Taghavipour, A., Azad, N. L. and McPhee, J. (2015). Real-time predictive control strategy for a plug-in hybrid electric powertrain. Mechatronics, 29, 13–27.

    Article  Google Scholar 

  • Vagg, C., Akehurst, S. and Brace, C. J. (2016). Stochastic dynamic programming in the real-world control of hybrid electric vehicles. IEEE Trans. Control Systems Technology 24, 3, 853–866.

    Article  Google Scholar 

  • Wang, J. and Wan, W. (2009). Optimization of fermentative hydrogen production process using genetic algorithm based on neural network and response surface methodology. Int. J. Hydrogen Energy 34, 1, 255–261.

    Article  MathSciNet  Google Scholar 

  • Wu, J., Zhang, C. and Cu, N. (2012). Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition. Int. J. Automotive Technology 13, 7, 1159–1167.

    Article  Google Scholar 

  • Xiong, W., Zhang, Y. and Yin, C. (2009). Optimal energy management for a series-parallel hybrid electric bus. Energy Conversion and Management 50, 7, 1730–1738.

    Article  Google Scholar 

  • Yin, H., Zhou, W., Li, M., Ma, C. and Zhao, C. (2016). An adaptive fuzzy logic based energy management strategy on battery/ultracapacitor hybrid electric vehicles. IEEE Trans. Transportation Electrification 2, 3, 300–311.

    Article  Google Scholar 

  • Zhang, J. and Shen, T. (2016). Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Trans. Control Systems Technology 24, 6, 2167–2175.

    Article  Google Scholar 

  • Zhang, P., Yan, F. and Du, C. (2015). A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics. Renewable and Sustainable Energy Reviews, 48, 88–104.

    Article  Google Scholar 

  • Zhang, S. and Xiong, R. (2015). Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Applied Energy, 155, 68–78.

    Article  Google Scholar 

  • Zou, Y., Liu, T., Fengchun, S. and Peng, H. (2013). Comparative study of dynamic programming and Pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle. Energies 6, 4, 2305–2318.

    Article  Google Scholar 

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Acknowledgement

The authors greatly appreciate the financial supported both from the National Natural Science Fund of China (Grant No. 51505086) and China Scholarship Council. This work also has been developed as Visiting Scholar at Center for Alternative Fuels, Engines and Emissions of the West Virginia University.

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Correspondence to Xinyou Lin.

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Lin, X., Li, H. Adaptive Control Strategy Extracted from Dynamic Programming and Combined with Driving Pattern Recognition for SPHEB. Int.J Automot. Technol. 20, 1009–1022 (2019). https://doi.org/10.1007/s12239-019-0095-7

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  • DOI: https://doi.org/10.1007/s12239-019-0095-7

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