Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.
BANVAIT, H., ANWAR, S., CHEN, Y. A rule-based ener-gy management strategy for plug-in hybrid electric vehicle (PHEV). 2009 American Control Conference. 3938-3943.
CIEŚLIK, W, SZWAJCA, F., GOLIMOWSKI, J. The pos-sibility of energy consumption reduction using the ECO driving mode based on the RDC test. Combustion Engines. 2020, 182(3), 59-69.
DADAM, S.R., JENTZ, R., MEISSNER, H. Diagnostic evaluation of exhaust gas recirculation (EGR) system on gasoline electric hybrid vehicle. SAE Technical Paper 2020-01-0902. 2020.
FLUDER, K., PIELECHA, I., CIEŚLIK, W. The impact of drive mode of a hybrid drive system on the energy flow in-dicators in the RDE test. Combustion Engines. 2018, 175(4), 18-25.
GENG, B., MILLS, J.K., SUN, D. Energy management control of mictroturbine-powered plug-in hybrid electric ve-hicles using the telemetry equivalent consumption minimi-zation strategy. IEEE Transactions on Vehicular Technology. 2011, 60(9), 4238-4248.
HAN, J., PARK, Y., KUM, D. Optimal adaptation of equiv-alent factor of equivalent consumption minimization strategy for fuel cell hybrid electric vehicles under active state in-equality constraints. Journal of Power Sources. 2014, 267, 491-502.
HYBRID-EV COMMITTEE. J1711: Recommended practice for measuring the exhaust emissions and fuel economy of hybrid-electric vehicles, including plug-in hybrid vehicles. Proceedings of the SAE Std. J1711_SAE World Congress. 2010, 13-15.
JAMMOUSSI, H., MAKKI, I., KLUZNER, M. et al. Diag-nostics of individual air fuel ratio cylinder imbalance. SAE International Journal of Passenger Cars-Electronic and Electrical Systems. 2017, 10(2017-01-1684), pp. 400-404.
LIU, J., CHEN, Y., ZHAN, J. et al. Heuristic dynamic pro-gramming based online energy management strategy for plug-in hybrid electric vehicles. IEEE Transactions on Vehicular Technology. 2019, 68(5), 4479-4393.
ONORI, S., SERRAO, L., RIZZONI, G. Adaptive equiva-lent consumption minimization strategy for hybrid electric vehicles. Dynamic Systems and Control Conference vol. 44175, pp. 499-505. 2010.
PADMARAJAN, B.V., MCGORDON, A., JENNINGS, P.A. Blended rule-based energy management for PHEV: System structure and strategy. IEEE Transactions on Vehic-ular Technology. 2015, 65(10), 8757-8762.
PAN, C., LIANG, Y., CHEN, L. et al. Optimal control for hybrid energy storage electric vehicle to achieve energy sav-ing using dynamic programming. Energies. 2019, 12(4), 588.
PARK, J., PARK, J.H. Development of equivalent fuel consumption minimization strategy for hybrid electric vehi-cles. International Journal of Automotive Technology. 2012, 13(5), 835-843.
PENG, J., FAN, H., HE, H. et al. A rule-based energy man-agement strategy for a plug-in hybrid school bus based on a control area network bus. Energies. 2015, 8(6), 5122-5142.
PENG, J., HE, H., XIONG, R. Rule based energy manage-ment strategy for a series-parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy. 2017, 185, 1633-1643.
PIELECHA, I., CIEŚLIK, W., FLUDER, K. Analysis of energy management strategies for hybrid electric vehicles in urban driving conditions. Combustion Engine. 2018, 173(2), 14-18.
PRITCHARD, E., MACKEY, L., ZHU, D. et al. Modular electric generator rapid deployment dc microgrid. In 2017 IEEE Second International Conference on DC Microgrids (ICDCM). 2017, pp. 106-110.
SAE INTERNATIONAL HYBRID-EV COMMITTEE. J2841: Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using Travel Survey Data-SAE International. SAE International Hybrid-EV Committee. 2010.
SONNENBERG, M., PRITCHARD, E., ZHU, D. Microgrid development using model-based design. In 2018 IEEE Green Technologies Conference (GreenTech). 2018, 57-60.
WEGMANN, R., DÖGE, V., BECKER, J. et al. Optimized operation of hybrid battery systems for electric vehicles using deterministic and stochastic dynamic programming. Journal of Energy Storage. 2017, 14, 22-38.
ZHU, D., PRITCHARD, E. NCSU year three final technical report. SAE Technical Paper 2014-01-2907. 2014.
ZHU, D., PRITCHARD, E.G., SILVERBERG, L.M. A new system development framework driven by a model-based testing approach bridged by information flow. IEEE Systems Journal. 2016, 12(3), pp. 2917-2924.