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Research Papers

Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle With Consideration of Battery Aging

[+] Author and Article Information
Ming Cheng

Department of Mechanical Engineering—
Engineering Mechanics,
Michigan Technological University,
1400 Townsend Drive,
Houghton, MI 49931
e-mail: mingc@mtu.edu

Bo Chen

Mem. ASME
Department of Mechanical Engineering—
Engineering Mechanics;
Department of Electrical and
Computer Engineering,
Michigan Technological University,
1400 Townsend Drive,
Houghton, MI 49931
e-mail: bochen@mtu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received March 15, 2018; final manuscript received February 19, 2019; published online March 25, 2019. Assoc. Editor: Mahdi Shahbakhti.

J. Dyn. Sys., Meas., Control 141(8), 081008 (Mar 25, 2019) (9 pages) Paper No: DS-18-1126; doi: 10.1115/1.4042954 History: Received March 15, 2018; Revised February 19, 2019

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

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References

Musardo, C. , Rizzoni, G. , and Staccia, B. , 2005, “ A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management,” 44th IEEE Conference on Decision and Control, Seville, Spain, Dec. 12–15, pp. 1816–1823.
Pei, D. , and Leamy, M. J. , 2013, “ Dynamic Programming-Informed Equivalent Cost Minimization Control Strategies for Hybrid-Electric Vehicles,” ASME J. Dyn. Syst., Meas., Control, 135(5), p. 051013. [CrossRef]
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 Conference (VTC Spring), Birmingham, AL, May 6–9.
Li, Y. , and Chen, B. , 2016, “ Development of Integrated Rule-Based Control and Equivalent Consumption Minimization Strategy for HEV Energy Management,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Auckland, New Zealand, Aug. 29–31.
Wang, H. , Sacheva, K. , Tripp, J. , Chen, B. , Robinette, D. , and Shahbakhti, M. , 2018, “ Optimal Map-Based Mode Selection and Powertrain Control for a Multi-Mode Plug-In Hybrid Electric Vehicle,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, Finland, July 2–4.
Wang, R. , and Lukic, S. M. , 2012, “ Dynamic Programming Technique in Hybrid Electric Vehicle Optimization,” IEEE International Electric Vehicle Conference, Greenville, SC, Mar. 4–8.
Wang, X. , He, H. , Sun, F. , and Zhang, J. , 2015, “ Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-In Hybrid Electric Vehicles,” Energies, 8(4), pp. 1–20.
Johannesson, L. , Asbogard, M. , and Egardt, B. , 2007, “ Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming,” IEEE Trans. Intell. Transp. Syst., 8(1), pp. 71–83. [CrossRef]
Lin, C.-C. , Peng, H. , and Grizzle, J. W. , 2004, “ A Stochastic Control Strategy for Hybrid Electric Vehicles,” American Control Conference (ACC), Boston, MA, June 30–July 2, pp. 4710–4715.
Guo, L. L. , Gao, B. Z. , Gao, Y. , and Chen, H. , 2017, “ Optimal Energy Management for HEVs in Eco-Driving Applications Using Bi-Level MPC,” IEEE Trans. Intell. Transp. Syst., 18(8), pp. 2153–2162. [CrossRef]
Feng, L. , Cheng, M. , and Chen, B. , 2015, “ Predictive Control of a Power-Split HEV With Fuel Consumption and SOC Estimation,” SAE Paper No. 2015-01-1161.
Poramapojana, P. , and Chen, B. , 2012, “ Model Predictive Control for Hybrid Electric Vehicle Energy Management,” International Conference on Advanced Vehicle Technologies and Integration, Changchun, China, July 16–19, Paper No. VTI2012A001.
Poramapojana, P. , and Chen, B. , 2012, “ Minimizing HEV Fuel Consumption Using Model Predictive Control,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Suzhou, China, July 8–10.
Cheng, M. , Feng, L. , and Chen, B. , 2017, “ Nonlinear Optimal Control of a Power-Split Hybrid Electric Vehicle With Electrochemical Battery Model,” SAE Paper No. 2017-01-1252.
Ripaccioli, G. , Bernardini, D. , Cairano, S. D. , Bemporad, A. , and Kolmanovsky, I. V. , 2010, “ A Stochastic Model Predictive Control Approach for Series Hybrid Electric Vehicle Power Management,” American Control Conference (ACC), Baltimore, MD, June 30–July 2.
Barik, B. , Bhat, P. K. , Oncken, J. , Chen, B. , Orlando, J. , and Robinette, D. , 2018, “ Optimal Velocity Prediction for Fuel Economy Improvement of Connected Vehicles,” IET Intell. Transp. Syst., 12(10), pp. 1329–1335. [CrossRef]
Chao, S. , Xiaosong, H. , Moura, S. J. , and Fengchun, S. , 2015, “ Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles,” IEEE Trans. Control Syst. Technol., 23(3), pp. 1197–1204. [CrossRef]
Chen, S.-Y. , Hung, Y.-H. , Wu, C.-H. , and Huang, S.-T. , 2015, “ Optimal Energy Management of a Hybrid Electric Powertrain System Using Improved Particle Swarm Optimization,” Appl. Energy, 160, pp. 132–145. [CrossRef]
Chen, Z. , Xiong, R. , and Cao, J. , 2016, “ Particle Swarm Optimization-Based Optimal Power Management of Plug-In Hybrid Electric Vehicles Considering Uncertain Driving Conditions,” Energy, 96, pp. 197–208. [CrossRef]
Chen, Z. , Mi, C. C. , Xiong, R. , Xu, J. , and You, C. , 2014, “ Energy Management of a Power-Split Plug-In Hybrid Electric Vehicle Based on Genetic Algorithm and Quadratic Programming,” J. Power Sources, 248, pp. 416–426. [CrossRef]
Cao, Y. , Kroeze, R. C. , and Krein, P. T. , 2016, “ Multi-Timescale Parametric Electrical Battery Model for Use in Dynamic Electric Vehicle Simulations,” IEEE Trans. Transp. Electrif., 2(4), pp. 432–442. [CrossRef]
Wu, B. , and Chen, B. , 2014, “ Study the Performance of Battery Models for Hybrid Electric Vehicles,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Ancona, Italy, Sept. 10–12.
Cheng, M. , Feng, L. , and Chen, B. , 2015, “ Simulation of Lithium Ion HEV Battery Aging Using Electrochemical Battery Model Under Different Ambient Temperature Conditions,” SAE Paper No. 2015-01-1198.
Prasad, G. K. , and Rahn, C. D. , 2014, “ Reduced Order Impedance Models of Lithium Ion Batteries,” ASME J. Dyn. Syst., Meas., Control, 136(4), p. 041012. [CrossRef]
Hu, X. , Li, S. E. , and Yang, Y. , 2016, “ Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles,” IEEE Trans. Transp. Electrif., 2(2), pp. 140–149. [CrossRef]
Wang, L. , and Chen, B. , 2017, “ Model-Based Analysis of V2G Impact on Battery Degradation,” SAE Paper No. 2017-01-1699.
Tang, L. , Rizzoni, G. , and Onori, S. , 2016, “ Energy Management Strategy for HEVs Including Battery Life Optimization,” IEEE Trans. Transp. Electrif., 1(3), pp. 211–222. [CrossRef]
Serrao, L. , Onori, S. , Sciarretta, A. , Guezennec, Y. , and Rizzoni, G. , 2011, “ Optimal Energy Management of Hybrid Electric Vehicles Including Battery Aging,” American Control Conference (ACC), San Francisco, CA, June 29–July 1.
Ebbesen, S. , Elbert, P. , and Guzzella, L. , 2012, “ Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles,” IEEE Trans. Veh. Technol., 61(7), pp. 2893–2900. [CrossRef]
Moura, S. J. , Stein, J. L. , and Fathy, H. K. , 2013, “ Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles Via Electrochemical Modeling and Stochastic Control,” IEEE Trans. Control Syst. Technol., 21(3), pp. 679–694. [CrossRef]
Argonne National Laboratory, 2019, “ Autonomie—Powertrain and Vehicle Model Architecture and Development Environment,” Argonne National Laboratory, Lemont, IL.
Prada, E. , Di Domenico, D. , Creff, Y. , Bernard, J. , Sauvant-Moynot, V. , and Huet, F. , 2012, “ Simplified Electrochemical and Thermal Model of LiFePO4-Graphite Li-Ion Batteries for Fast Charge Applications,” J. Electrochem. Soc., 159(9), pp. A1508–A1519. [CrossRef]
Prada, E. , Di Domenico, D. , Creff, Y. , Bernard, J. , Sauvant-Moynot, V. , and Huet, F. , 2013, “ A Simplified Electrochemical and Thermal Aging Model of LiFePO4-Graphite Li-Ion Batteries: Power and Capacity Fade Simulations,” J. Electrochem. Soc., 160(4), pp. A616–A628. [CrossRef]
Liu, J. , 2007, Modeling, Configuration and Control Optimization of Power-Split Hybrid Vehicles, Mechanical Engineering, University of Michigan, Ann Arbor, MI.
Moura, S. J. , 2011, Techniques for Battery Health Conscious Power Management Via Electrochemical Modeling and Optimal Control, Mechanical Engineering, University of Michigan, Ann Arbor, MI.
Rahn, C. D. , and Wang, C.-Y. , 2013, Battery Systems Engineering, Wiley, Hoboken, NJ.
Di Domenico, D. , Stefanopoulou, A. , and Fiengo, G. , 2010, “ Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter,” ASME J. Dyn. Syst. Meas. Control-Trans., 132(6), p. 061302. [CrossRef]
Baudry, P. , Neri, M. , Gueguen, M. , and Lonchampt, G. , 1995, “ Electrothermal Modeling of Polymer Lithium Batteries for Starting Period and Pulse Power,” J. Power Sources, 54(2), pp. 393–396. [CrossRef]
Abu-Sharkh, S. , and Doerffel, D. , 2004, “ Rapid Test and Non-Linear Model Characterisation of Solid-State Lithium-Ion Batteries,” J. Power Sources, 130(1–2), pp. 266–274. [CrossRef]
Wang, L. , 2009, Model Predictive Control System Design and Implementation Using MATLAB, Springer, London.

Figures

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Fig. 1

The schematic of a planetary gear

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Fig. 2

The WOT torque of engine at different speed

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Fig. 3

The instant fuel consumption rate map

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Fig. 4

The optimal engine operation speed versus engine power request map

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Fig. 5

The schematic diagram of an averaged single particle lithium ion model

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Fig. 6

The change of the current density of side reaction along with the battery SOC and charge C rate when the cell temperature is 30 °C

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Fig. 7

The change of the current density of side reaction along with the battery SOC and charge C rate when the cell temperature is 45 °C

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Fig. 8

The change of battery capacity with different control strategies ( γ=0.01  for NMPC with battery aging consideration)

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Fig. 9

The change of battery SEI thickness with different control strategies ( γ=0.01 for NMPC with battery aging consideration)

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Fig. 10

The change of battery SOC using the NMPC with/without the consideration of battery aging (γ=0.008)

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Fig. 11

The change of fuel consumption using the NMPC with/without the consideration of battery aging (γ=0.008)

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Fig. 12

The change of SEI thickness for the US06 driving cycle with the change of different weighting factor values in the cost function

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Fig. 13

The change of battery capacity with the change of different weighting factor values in the cost function

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