Hybrid Electric Vehicle Speed Profile Prediction Based on Data Fusion

doi:10.3901/JME.2024.06.342

Abstract

Abstract: Energy Management Strategy (EMS) can optimize energy consumption, reduce emissions and improve fuel economy of hybrid electric vehicles (HEV) in different scenarios. One of the keys of optimization is the prediction of vehicle speed profile： predict the speed profile of the future interval, and then calculate the power demand, and optimize the power distribution of the engine and motor. Therefore, an intelligent prediction method of vehicle velocity profile based on driving behavior and lidar data is proposed, which could be applied to energy optimization of lidar equipped hybrid electric vehicle to satisfy driving intention. Firstly, a driving intention recognition model based on gated recurrent unit (GRU) network is constructed to identify driving intentions from vehicle states, and driver's driving needs are considered in real time. Secondly, in the traffic scene where intelligent vehicles and traditional vehicles are mixed, the speed profile prediction method relying on vehicle communication may not be available. In this study, the traffic flow speed is estimated by lidar equipped on vehicles. The algorithm used the Joint Probability Data Association tracker and Interactive Multipul Model method to get the speed of the vehicle in front relative to the vehicle in real time, without prior knowledge of road and condition. Finally, the driver's expected speed curve in the next second is predicted by integrating the driving intention and the relative speed of the vehicle ahead. Experimental results show that the training accuracy of GRU model is between 85%-95%, and the recognition accuracy can reach 88%, which can effectively identify the driver's driving intention. The proposed method has good velocity measurement accuracy. Prediction speed and actual speed of the gap in a smaller range, can be used for EMS, then for power battery management system (BMS) provides the basis for reasonable control of power battery energy output.

Key words: energy management, velocity profile prediction, data fusion, driving intention, lidar

CLC Number:

U461

GAO Kai, LUO Pan, XIE Jin, HU Lin, CHEN Bin, DU Ronghua. Hybrid Electric Vehicle Speed Profile Prediction Based on Data Fusion[J]. Journal of Mechanical Engineering, 2024, 60(6): 342-353.

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

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2024.06.342

http://www.cjmenet.com.cn/EN/Y2024/V60/I6/342

References

[1] 熊瑞. 动力电池管理系统核心算法[M]. 北京：机械工业出版社， 2018. XIONG Rui. Core algorithm of battery management system for EVs[M]. Beijing：China Machine Press，2018.
[2] 胡林，田庆韬，黄晶，等. 电动汽车锂离子电池-超级电容混合储能系统能量分配与参数匹配研究综述[J].机械工程学报，2022，58(16)：224-237. HU Lin，TIAN Qingtao，HUANG Jing，et al. Review on energy distribution and parameter matching of lithium-ion battery-super capacitor hybrid energy storage system for electric vehicles[J]. Journal of Mechanical Engineering，2022，58(16)： 224-237.
[3] GAO K，LUO P，XIE J，et al. Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR[J]. Energy，2023，284：128535.
[4] SUN C，MOURA S J，HU X，et al. Dynamic traffic feedback data enabled energy management in plug-in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology，2015，23(3)：1075-1086.
[5] 高建平，赵金宝，杨松，等. 基于驾驶意图的插电式混合动力公交车控制策略[J]. 机械工程学报，2016，52(24)：107-114. GAO Jianping，ZHAO Jinbao，YANG Song，et al. Control strategy of plug-in hybrid electric bus based on driver intention[J]. Journal of Mechanical Engineering，2016，52(24)：107-114．
[6] BENDER F A，KASZYNSKI M，SAWODNY O. Drive cycle prediction and energy management optimization for hybrid hydraulic vehicles[J]. IEEE Transactions on vehicular technology，2013，62(8)：3581-3592.
[7] LIU T，HU X，LI S E，et al. Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle[J]. IEEE/ASME transactions on mechatronics，2017，22(4)：1497-1507.
[8] XIE S，HU X，XIN Z，et al. Time-efficient stochastic model predictive energy management for a plug-in hybrid electric bus with an adaptive reference state-of-charge advisory[J]. IEEE Transactions on Vehicular Technology，2018，67(7)：5671-5682.
[9] 杨超，焦晓红，李亮，等. 面向公交客车应用的插电式混合动力实时优化策略研究[J]. 机械工程学报， 2015，51(22)：111-119. YANG Chao，JIAO Xiaohong，LI Liang，et al. Research on city-bus application-oriented real-time optimal energy management strategy for plug-in hybrid electric bus[J]. Journal of Mechanical Engineering， 2015，51(22)：111-119.
[10] LI G，ZHU F，QU X，et al. Driving style classification based on driving operational pictures[J]. IEEE Access，2019，7：90180-90189.
[11] WANG R，LUKIC S M. Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles[C]// 2011 IEEE Vehicle power and propulsion conference. IEEE，2011：1-7.
[12] LÜ C，HU X，SANGIOVANNI-VINCENTELLI A，et al. Driving-style-based codesign optimization of an automated electric vehicle：A cyber-physical system approach[J]. IEEE Transactions on Industrial Electronics，2018，66(4)：2965-2975.
[13] CHEN C，LIU L，QIU T，et al. Driver’s intention identification and risk evaluation at intersections in the Internet of vehicles[J]. IEEE Internet of Things Journal，2018，5(3)：1575-1587.
[14] LI M，CAO H，SONG X，et al. Shared control driver assistance system based on driving intention and situation assessment[J]. IEEE Transactions on Industrial Informatics，2018，14(11)：4982-4994.
[15] LEI Y，ZHANG Y，FU Y，et al. Research on adaptive gearshift decision method based on driving intention recognition[J]. Advances in Mechanical Engineering，2018，10(10)：1-12.
[16] YI D，SU J，LIU C，et al. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles[J]. IEEE Transactions on Industrial Informatics，2018，15(6)：3693-3702.
[17] PENG B，ZHANG H，XUAN F，et al. Torque distribution strategy of electric vehicle with in-wheel motors based on the identification of driving intention[J]. Automotive Innovation，2018，1：140-146.
[18] SALEH K，HOSSNY M，NAHAVANDI S. Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks[C]// 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE，2017：1-6.
[19] LIU Y，ZHAO P，QIN D，et al. Driving intention identification based on long short-term memory and a case study in shifting strategy optimization[J]. IEEE Access，2019，7：1-12.
[20] YUAN J，YANG L. Predictive energy management strategy for connected 48V hybrid electric vehicles[J]. Energy，2019，187：115952.
[21] 张利鹏，贾启康，刘威，等. 基于驾驶意图识别的多模耦合驱动系统能量管理[J]. 机械工程学报，2020，55(18)：112-124. ZHANG Lipeng，JIA Qikang，LIU Wei，et al. Energy management of multi-mode coupling drive system based on driver intention recognition[J]. Journal of Mechanical Engineering，2019，55(18)：112-124.
[22] 邓涛，罗远平. 基于驾驶意图云模型识别的混合动力汽车A-ECMS的构建[J]. 汽车安全与节能学报，2020，11(3)：305. DENG Tao，LUO Yuanping. Construction of hybrid electric vehicle a-ecms based on cloud model recognition of driving intention[J]. Journal of Automotive Safety and Energy，2020，11(3)：305-313.
[23] 季学武，费聪，何祥坤，等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报，2019，32(6)：34-42. JI Xuewu，FEI Chong，HE Xiangkun，et al. Driving intention identification and vehicle trajectory prediction based on lstm network[J]. China Journal of Highways，2019，32(6)：34-42.
[24] 宋晓琳，曾艳兵，曹昊天，等. 基于长短期记忆网络的换道意图识别方法[J]. 中国公路学报，2021，34(11)：236-245. SONG Xiaolin，ZENG Yanbing，CAO Hao-tian，et al， Lane change intention recongnition method based on LSTM network[J]. China Journal of Highways，2021，34(11)：236-245.
[25] YANG J Y，CHOU L D，TUNG C F，et al. Average-speed forecast and adjustment via VANETs[J]. IEEE Transactions on Vehicular Technology，2013，62(9)：4318-4327.
[26] ZHANG F，XI J，LANGARI R. Real-time energy management strategy based on velocity forecasts using V2V and V2I communications[J]. IEEE Transactions on Intelligent Transportation Systems，2016，18(2)： 416-430.
[27] JIANG B，FEI Y. Traffic and vehicle speed prediction with neural network and hidden markov model in vehicular networks[C]// 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE，2015：1082-1087.
[28] SUN C，HU X，MOURA S J，et al. Velocity predictors for predictive energy management in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology，2014，23(3)：1197-1204.
[29] WOJKE N，HÄSELICH M. Moving vehicle detection and tracking in unstructured environments[C]// 2012 IEEE International Conference on Robotics and Automation. IEEE，2012：3082-3087.
[30] DEWAN A，CASELITZ T，TIPALDI G D， et al. Motion-based detection and tracking in 3d lidar scans[C]// 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE，2016：4508-4513.
[31] LUO Z，HABIBI S，MOHRENSCHILDT M V. LiDAR based real time multiple vehicle detection and tracking[J]. International Journal of Computer and Information Engineering，2016，10(6)：1125-1132.