WLTC measuring driving cycle (power reserve measurement procedure for hybrids and electric vehicles)
DOI:
https://doi.org/10.30977/VEIT.2022.22.0.9Keywords:
HEV, WLTC, Google Maps traffic levels, driving cycles, hybrid car, electric car, numerical modelAbstract
Problem. The most effective energy management strategies for hybrid vehicles and electric vehicles are optimization-based strategies. These strategies require prior knowledge of the driving cycle, which is not easy to predict. Goal. The goal is to combine the Worldwide harmonized light vehicles test cycle (WLTC) with short trips on small sections with real traffic levels to predict the energy and fuel consumption of hybrid vehicles and electric vehicles. Methodology. Research methods are experimental and mathematical. First of all, eight characteristic parameters are extracted from real speed profiles used on urban road sections in the city of Kharkiv under various road conditions, as well as on short WLTC trips. The minimum distance algorithm is used to compare parameters and determine three traffic levels (heavy, medium, and low traffic) for short WLTC trips. Thus, for each route determined using Google Maps, the energy and fuel consumption of hybrid vehicles and electric vehicles are estimated using short trips by the WLTC, adjusted for distances and traffic levels. In addition, a numerical model of the vehicle was implemented. It was used to test the accuracy of predicting fuel and energy consumption in accordance with the proposed methodology. Originality. For the methodology using only GM information is required as input data; no other device or software is required. This aspect makes the methodology extremely economical. Then, the algorithm regulating traffic levels shown by GM is unique and valid in all urban centers. This aspect makes the methodology universal. WLTC takes into account the driving styles of drivers around the world, so the methodology can be applied to any car driver. Prediction accuracy can be increased by taking into account other input information, such as the distribution of traffic light signals or the driver's typical gear shifting style. Results. The results are promising, as the average absolute percentage error between experimental driving cycles and projected ones is 3.89 % for fuel consumption, increasing to 6.80 % for energy consumption. Practical value. The possibility of energy forecasting and fuel consumption for a hybrid vehicle and an electric vehicle makes it possible to develop energy consumption management systems for HEVs that can manage the energy reserve to ensure full travel by electric traction in limited traffic zone (LTZ) or minimize local air pollution; increase the service life of energy reserves (usually batteries) by maintenance costs and disposal problems reducing; optimize the transmission-use efficiency due to fuel consumption and pollutants emissions reduction.
References
Zhou, Y.; Ravey, A.; Pera, M. A (2019). Survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles. J. Power Sour. 412. 480-495. doi:10.1016/j.jpowsour.2018.11.085.
Sciarretta, A.; Guzzella, L. (2005). Vehicle Propulsion Systems: Introduction to Modeling and Optimization, 3rd ed. Berlin/Heidelberg: Springer.
Bagwe, R.M.; Byerly, A.; dos Santos, E.C., Jr.; Ben-Miled, Z. (2019). Adaptive Rule-Based Energy Management Strategy for a Parallel HEV. Energies. 12, 4472. doi:10.3390/en12234472.
Wu, J.; Wei, Z.; Liu, K.; Quan, Z.; Li, Y. (2020). Battery-Involved Energy Management for Hybrid Electric Bus Based on Expert-Assistance Deep Deterministic Policy Gradient Algorithm. IEEE Trans. Veh. Technol. 69. 12786-12796. doi: 10.1109/TVT.2020.3025627.
Fang, L.; Qin, S.; Xu, G.; Li, T.; Zhu, K. (2011). Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms. Energies. 4. 532-544. doi:10.3390/en4030532.
Wu, J.; Wei, Z.; Li, W.; Wang, Y.; Li, Y.; Sauer, D. (2020). Battery Thermal- and Health-Constrained Energy Management for Hybrid Electric Bus Based on Soft Actor-Critic DRL Algorithm. IEEE Trans. Ind. Inform. doi:10.1109/TII.2020.3014599.
Wang, R.; Lukic, S.M. (2011) Review of Driving Conditions Prediction and Driving Style Recognition Based Control Algorithms for Hybrid Electric Vehicle. In Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference. Chicago, IL, USA, 6-9 September 2011. https://doi.org/10.1109/VPPC.2011.6043061.
Ericson, E. (2001). Independent Driving Pattern Factors and Their Influence on Fuel-Use and Exhaust Emission Factors. Transp. Res. Part D Transp. Environ. 6. 325-345. doi:10.1016/S1361-9209(01)00003-7.
Xi, H.; Ying, T.; Xingui, H. (2009) An intelligent multi-feature statistical approach for discrimination of driving conditions of hybrid electric vehicle. In Proceedings of the International. Joint Conference on Neural Networks. Atlanta, GA, USA, 14-19 June 2009, doi:10.1109/IJCNN.2009.5178645.
Chen, Z.; Kiliaris, L.; Murphey, Y.L.; Masrur, A. (2009). Intelligent power management in SHEV based on roadway type and traffic congestion levels. In Proceedings of the Vehicle Power and Propulsion Conference. Dearborn, MI, USA, 7-10 September 2009. doi:10.1109/VPPC.2009.5289748.
Langari, R.; Won, J. (2005) Intelligent Energy Management Agent for a Parallel Hybrid Vehicle - Part II: System Architecture and Design of the Driving Situation Identification Process. IEEE Trans. Veh. Technol. 54. 925-934. doi:10.1109/TVT.2005.844685.
Jeon, S.; Park, Y.; Lee, J. (2002). Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition. J. Dyn. Sys. Meas. Control. 124. 141-149. doi:10.1115/1.1434264.
Han, L.; Jiao, X.; Zhang, Z. (2020). Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging. Energies. 13, 202. doi:10.3390/en13010202.
Liu, H.; Wang, C.; Zhao, X.; Guo, C. (2018). An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation. Energies. 11, 1805. doi:10.3390/en11071805.
Johannesson, L.; Asbogard, M.; Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. IEEE Trans. Veh. Technol. 8. 71-83. doi:10.1109/TITS.2006.884887.
Lin, C.; Peng, H.; Grizzle, J. (2004). Stochastic Control Strategy for Hybrid Electric Vehicles. The 2004 American Control Conference. Boston, MA, USA, 30 June-2 July 2004. 5. 4710-4715. doi:10.23919/ACC.2004.1384056.
Chaofeng, P.; Wei, W.; Liao, C.; Long, C.; Limei, W. (2017). Driving range estimation for electric vehicles based on driving condition identification and forecast. AIP Adv. 7, 105206. doi:10.1063/1.4993945.
Shen, P.; Zhao, Z.; Zhan, X.; Li, J.; Guo, Q. (2018). Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction. Energy. 155. 838-852. doi:10.1016/j.energy.2018.05.064.
Zhang, C.; Vahidi, A.; Pisu, P.; Li, X.; Tennant, K. (2010). Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles. IEEE Trans. Veh. Technol. 59. 1139-1147. doi:10.1109/TVT.2009.2038707.
Qiuming, G., Yaoyu, L., Zhong-Ren, P. (2008). Optimal power management of plug-in hybrid electric vehicles. IEEE Trans. Veh. Technol. 57. 3393-3401. doi:10.1109/TVT.2008.921622.
He, H., Guo, J., Zhou, N., Sun, C., Peng, J. (2017). Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles. Energies. 10, 1796. doi:10.3390/en10111796.
Zhang, F., Xi, J., Langari, R. (2017). Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications. IEEE Trans. Veh. Technol. 18. 416-430. doi:10.1109/TITS.2016.2580318.
Hu, J., Jiang, X., Jia, M., Zheng, Y. (2018). Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information. Appl. Sci. 1266, doi:10.3390/app8081266.
Yavasoglu, Y.A., Tetik, Y., Gokce, K. (2019). Implementation of machine learning based real time range estimation method without destination knowledge for BEVs. Energy. 172. 1179-1186. doi:10.1016/j.energy.2019.02.032.
Douglas, K.; Milovanovic, N.; Turner, J.; Blundell, D. Fuel Economy Improvement Using Combined CAI and Cylinder Deactivation (CDA) - An Initial Study. SAE Technical Paper. SAE: Warrendale, PA, USA. 1. 0110. doi:10.4271/2005-01-0110.
Cossalter, V. (2006). Motorcycle Dynamics, 2nd ed.; Lulu.com: Morrisville, NC, USA.
Previti, U., Brusca, S., Galvagno, A. (2020). Passenger Car Energy Demand Assessment: A New Approach Based on Road Traffic Data. In Proceedings of the 75th National ATI Congress-#7 Clean Energy for all (ATI 2020), E3S Web Conference, Rome, Italy, 15-16 September 2020. 197. 05006. doi:10.1051/e3sconf/20201970500.
Tutuianu, M., Bonnel, P., Ciuffo, B., Haniu, T., Ichikawa, N., Marotta, A., Pavlovic, J., Steven, H. (2015). Development of the Worldwide harmonized Light duty Test Cycle (WLTC) and a possible pathway for its introduction in the European legislation. Trans. Res. Part D Transp. Environ. 40. 61-75. doi:10.1016/j.trd.2015.07.011.
Brusca, S., Famoso, F., Lanzafame, R., Galvagno, A., Mauro, S., Messina, M. (2019). Wind farm power forecasting: New algorithms with simplified mathematical structure. AIP Conf. Proc. 2191. 020028, doi:10.1063/1.5138761.
Oglieve, C.J., Mohammadpour, M., Rahnejat, H. (2017). Optimization of the vehicle transmission and the gear-shifting strategy for the minimum fuel consumption and the minimum nitrogen oxide emissions. Proc. Inst. Mech. Eng. Part D. 231. 883-899. doi:10.1177/0954407017702985.
Будніченко В.Б., Гордієнко М.М. (2019). Аналіз показника енерговитрат транспортних засобів з електричним двигуном. Комунальне господарство міст. Серія: Технічні науки та архітектура. 3. 149. 158-163 [Електронний ресурс]. – Режим доступу: http://nbuv.gov.ua/UJRN, doi:10.33042/2522-1809-2019-3-149-158-163. Budnichenko, V.B., Hordiienko, M.M. (2019). Analiz pokaznyka enerhovytrat transportnykh zasobiv z elektrychnym dvyhunom [Analysis of energy indicators of vehicles with electric motor]. Municipal economy of cities. Technical science and architecture. 3. 149. 158-163 [in Ukrainian]. URL: http://nbuv.gov.ua/UJRN. doi: 10.33042/2522-1809-2019-3-149-158-163.
Козьяр Б.О., Рец Р.А., Сичевський М.І. (2015). Аналіз небезпек, що створюються гібридним транспортним засобом у разі дорожньо-транспортної пригоди після його участі. Проблеми та перспективи розвитку системи безпеки життєдіяльності. Львів: ЛДУ БЖД. 127-128. Koziar, B.O., Rets, R.A., Sychevskyi, M.I. (2015). Analiz nebezpek, shcho stvoriuiutsia hibrydnym transportnym zasobom u razi dorozhno-transportnoi pryhody pislia yoho uchasti [Analysis of the problems that are created by a hybrid transport system in different road and transport suits after its participation]. Problems and Prospects of Life Safety System Development. Lviv: LSULS. Pp. 127-128.
Ружицький Д.В., Лоїк В.Б. (2017). Організація аварійно-рятувальних робіт з використанням гібридних транспортних засобів. Проблеми та перспективи розвитку безпеки життєдіяльності. Львів: ЛДУ БЖД. C. 88-90. Ruzhytskyi, D.V., Loik, V.B. (2017) Orhanizatsiia avariino-riatuvalnykh robit z vykorystanniam hibrydnykh transportnykh zasobiv [Organization of emergency rescue operations using hybrid vehicles]. Problems and Prospects of Life Safety System Development. Lviv: LSULS. 88-90. [in Ukrainian].
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Copyright (c) 2022 Микола Гордієнко, Олександр Пархоменко, Vladyslav Podpisnov
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