Research on Eco-driving Control Strategy of Connected Electric Vehicle Based on Learning-MPC

doi:10.3901/JME.2024.10.453

Abstract

Abstract: Eco-driving is an important way to achieve sustainable mobility and sustainable urban transport development. To improve the energy efficiency of connected electric vehicles, a two-stage non-conservative eco-driving control strategy combining learning-based model predictive control and fast interior point method is proposed for complex and variable urban signalized intersection scenarios, taking into full consideration constraints such as signal phase and timing information of real traffic and the limited predictive capability of vehicles for future information. Before vehicle departure, the energy-efficient optimal control problem is constructed based on passenger destination and road speed limit information, while the band-stop function is introduced to improve the computational efficiency to transform the speed constraint into a part of the objective function, and the interior point method coarse planning solves the vehicle energy-efficient optimal reference speed trajectory; At departure, the vehicle predicts the dynamic signal phase and timing information, and the Learning-MPC strategy learns the state prediction model of the vehicle online through Gaussian process to realize the tracking control of the vehicle energy-efficient optimal reference speed trajectory. The simulation shows that the proposed method can achieve 9.7% energy saving compared with the classical acceleration-uniformity-deceleration strategy, and indicates better energy saving potential as the length of the predicted field of view increases. It is further verified that the error accumulation problem caused by the discretization of the traditional MPC non-flexible conservative system state prediction model is solved by machine learning, and the optimal effect of vehicle eco-driving control is improved to a higher degree.

Key words: connected vehicle, eco-driving, model predictive control, Gaussian process, signalized intersections

CLC Number:

U461

LI Bingbing, ZHUANG Weichao, LIU Haoji, ZHANG Hao, YIN Guodong, ZHANG Jianrun. Research on Eco-driving Control Strategy of Connected Electric Vehicle Based on Learning-MPC[J]. Journal of Mechanical Engineering, 2024, 60(10): 453-462.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2024.10.453

http://www.cjmenet.com.cn/EN/Y2024/V60/I10/453

References

[1] SHI Tianze，ZHAO Fuquan，HAO Han，et al. Development trends of transmissions for hybrid electric vehicles using an optimized energy management strategy[J]. Automotive Innovation，2018，1(4):291-299.
[2] TANG Xingwang，GUO Qin，LI Ming，et al. Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning[J]. Journal of Power Sources，2021，494(1):229727.
[3] HE Yinglong，ZHOU Quan，MAKRIDIS M，et al. Multi-objective Co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs[J]. IEEE Transactions on Transportation Electrification，2020，6(1):346-355.
[4] XIA Haitao，BORIBOONSOMSIN K，SCHWEIZER F，et al. Field operational testing of ECO-approach technology at a fixed-time signalized intersection[C]//Institute of Electrical and Electronics Engineers. IEEE Intelligent Transportation Systems Society. International IEEE Conference on Intelligent Transportation Systems，September 16-19，2012，Anchorage，Alaska，USA. IEEE，2012:188-193.
[5] CHEN L，SHARMA P，TSENG Y. Eco-sign:A load-based traffic light control system for environmental protection with vehicular communications[J]. Computer Communication Review，2011，41(4):438-439.
[6] LI Guoqiang，GRGES D，WANG Meng. Online optimization of gear shift and velocity for eco-driving using adaptive dynamic programming[J]. IEEE transactions on intelligent vehicles，2022，7(1):123-132.
[7] SUN Chao，SHEN Xinwei，MOURA S. Robust optimal eco-driving control with uncertain traffic signal timing[C]//Institute of Electrical and Electronics Engineers. ACC Operating Committee. Annual American Control Conference (ACC)，June 27-29，2018，Milwaukee，Wisconsin，USA. IEEE，2018:5548-5553.
[8] ZHAO Shauidong，ZHANG Kuilin. Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions[J]. Transportation Research Part B:Methodological，2021，145:80-117.
[9] DURBIN T，MILLER J，JOHNSON K. Draft Final memorandum for CE-CERT engine testing portion CARB assessment emissions from use biodiesel as a motor vehicle fuel California:Biodiesel characterization and NOx mitigation study[EB/OL]. CARB，Sacramento，CA，USA，2009. https://ww2.arb.ca.gov/resources/document-s/biodieseltest-reports.
[10] BAKIBILLAH A，KAMAL M，TAN C，et al. Event-driven stochastic eco-driving strategy at signalized intersections from self-driving data[J]. IEEE Transactions on Vehicular Technology，2019，68(99):8557-8569.
[11] ZHANG Xudong，ZHANG Tao，ZOU Yuan，et al. Predictive eco-driving application considering real-world traffic flow[J]. IEEE Access，2020(8):82187-82200.
[12] LI Shenbo，XU Shaobing，HUANG Xiaoyu，et al. Eco-departure of connected vehicles with V2X communication at signalized intersections[J]. IEEE Transactions on Vehicular Technology，2015，64(12):5439-5449.
[13] YE Fei，HAO Peng，QI Xuewei，et al. Prediction-based eco-approach and departure at signalized intersections with speed forecasting on preceding vehicles[J]. IEEE Transactions on Intelligent Transportation Systems，2019，20(4):1378-1389.
[14] NGUYEN V，KIM O，DANG T，et al. An efficient and reliable green light optimal speed advisory system for autonomous cars[C]//Institute of Electrical and Electronics Engineers. IEICE Technical Committee on Information and Communication Management (ICM) and the KICS Committee on Korean Network Operations and Management. 18 th Asia-Pacific Network Operations and Management Symposium (APNOMS)，October 05-07，2016，Kanazawa，Japan. IEEE. DOI:10.1109/APNO-MS.2016.7737260.
[15] OZATAY E，ONORI S，WOLLAEGER J，et al. Cloud-based velocity profile optimization for everyday driving:A dynamic-programming-based solution[J]. IEEE Transactions on Intelligent Transportation Systems，2014，15(6):2491-2505.
[16] MAHLER G，VAHIDI A. An optimal velocity-planning scheme for vehicle energy efficiency through probabilistic prediction of traffic-signal timing[J]. IEEE Transactions on Intelligent Transportation Systems，2014，15(6):2516-2523.
[17] HUANG Xianan，PENG H. Speed trajectory planning at signalized intersections using sequential convex optimization[C]//Institute of Electrical and Electronics Engineers. ACC Operating Committee. Annual American Control Conference (ACC)，May 24-26，2017，Seattle，WA，USA. IEEE. 2017:1-11.
[18] HU Jia，SHAO Yunli，SUN Zongxuan，et al. Integrated optimal eco-driving on rolling terrain for hybrid electric vehicle with vehicle-infrastructure communication[J]. Transportation Research Part C Emerging Technologies，2016，68(1):228-244.
[19] NUNZIO G，WIT C，MOULIN P，et al. Eco-driving in urban traffic networks using traffic signals information:Eco-driving in urban traffic networks using traffic signals information[J]. International Journal of Robust and Nonlinear Control，2016，26(6):1307-1324.
[20] KAMAL M，MUKAI M，MURATA J，et al. Model predictive control of vehicles on urban roads for improved fuel economy[J]. IEEE transactions on control systems technology:A publication of the IEEE Control Systems Society，2013，21(3):831-841.
[21] SUN Chao，LENG Jianghao，SUN Fengchun. A fast optimal speed planning system in arterial roads for intelligent and connected vehicles[J]. IEEE Internet of Things Journal，2022，9(20):20295-20307.
[22] WU Dongmei，LI Yue，DU Changqing，et al. Fast velocity trajectory planning and control algorithm of intelligent 4WD electric vehicle for energy saving using time-based MPC[J]. IET Intelligent Transport Systems，2019，13(1):153-159.
[23] HE Defeng，ZHOU Long，SUN Zhe. Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information[J]. Control Theory and Technology:English edition，2020，18(2):204-216.
[24] HAN J，VAHIDI A，SCIARRETTA A. Fundamentals of energy efficient driving for combustion engine and electric vehicles:An optimal control perspective[J]. Automatica. 2019，103:558-572.