Energy Management Optimization for Hybrid Electric Unmanned Tracked Vehicles Based on Path Planning UsingCNN-LSTM Prediction

doi:10.12382/bgxb.2021.0679

Abstract

Abstract: As one of the key technologies of hybrid electric vehicles, energy management is critical to the entire efficiency and fuel economy. As the driving cycle of unmanned tracked vehicles is uncertain, conventional energy management strategies must deal with new challenges. To improve the prediction accuracy, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed for processing both planned and historical velocity series. An optimal forward dynamic programming algorithm is proposed to solve the optimal control problem of energy management. Based on the prediction results, a model predictive control algorithm is adopted to realize real-time optimization of energy management. The effectiveness of the method is proved by using collected data from actual field experiments of unmanned tracked vehicles. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves prediction accuracy by 3%. The energy management strategy based on model predictive control reduces fuel consumption by 3.9% compared to the traditional regular energy management strategy.

Key words: unmannedtrackedvehicle, hybridelectricvehicles, energymanagementstrategy, CNN-LSTM

CLC Number:

TJ810.2

TAN Yingqi, XU Jingyi, XIONG Guangming, LI Zirui, CHEN Huiyan. Energy Management Optimization for Hybrid Electric Unmanned Tracked Vehicles Based on Path Planning UsingCNN-LSTM Prediction[J]. Acta Armamentarii, 2022, 43(11): 2738-2748.

References

［1］陈慧岩, 张玉. 军用地面无人机动平台技术发展综述[J]. 兵工学报，2014, 35(10):1696-1706.
CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicle.[J]. Acta Armamentarii, 2014, 35(10): 1696-1706.(in Chinese)
[2] 孙逢春，张承宁. 装甲车辆混合动力电传动技术［M］. 北京：国防工业出版社，2016．
SUN F C，ZHANG C N．Technologies for the hybrid electric driver system of armored vehicle［M］．Beijing: National Defense Industry Press, 2016. (in Chinese)
[3] DU G D, ZOU Y, ZHANG X D, et al.Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning[J]. Applied Energy, 2019, 251: 113388.
[4] XIE S B, HU X S, XIN Z K, et al. Pontryagin's minimum principle based model predictive control of energy management for a plug-in hybrid electric bus[J]. Applied Energy, 2019, 236: 893-905.
[5] ZHOU Y, RAVEY A, PERA M C. A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles[J]. Journal of Power Sources, 2019, 412: 480- 495.
[6] ZENG X R, WANG J M. Optimizing the energy management strategy for plug-in hybrid electric vehicles with multiple frequent routes[J]. IEEE Transactions on Control Systems Technology, 2019, 27(1): 394-400.
[7] XING X W, XIE L L, MENG H M. Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community[J]. International Journal of Electrical Power & Energy Systems, 2019, 107: 186-199.
[8] REZAEI A, BURL J B, ZHOU B, et al. A new real-time optimal energy management strategy for parallel hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology, 2019, 27(2): 830-837.
[9] NGUYEN B H, GERMAN R, TROVAO J P F, et al. Real-time energy management of battery/supercapacitor electric vehicles based on an adaptation of pontryagin's minimum principle[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 203- 212.
[10] PAGANELLI G, DELPRAT S, GUERRA T M, et al. Equivalent consumption minimization strategy for parallel hybrid powertrains[J]. IEEE Vehicular Technology Conference, 2002，4: 2076-2081.
[11] CHASSE A, SCIARRETTA A. Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management[J]. Control Engineering Practice, 2011, 19(11): 1253-1265.
[12] 徐彩丽, 黄宏成, 朱海健. 基于ECMS算法的能量管理策略研究[J]. 传动技术, 2015, 29(2): 21-25,38.
XU C, HUANG H C, ZHU H J. Research of energy management strategy based on ecms algorithm[J]. Drive System Technique, 2015, 29(2):21-25,38.(in Chinese)
[13] MURPHEY Y L, PARK J, ABUL MASRUR M. Intelligent energy management in a low cost hybrid electric vehicle power system[C]∥Proceedings of 2013 IEEE 78th Vehicular Technology Conference. Las Vegas, NV, US: IEEE, 2013: 6692245.
[14] MURPHEY Y L, PARK J, KILIARIS L, et al. Intelligent hybrid vehicle power control—Part Ⅱ: online intelligent energy management[J]. IEEE Transactions on Vehicular Technology, 2013, 62(1): 69-79.
[15] XIANG C L, DING F, WANG W D, et al. MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle[J]. Science China Technological Sciences, 2017, 60(5): 737-748.
[16] SUN C, HU X S, MOURA S J, et al. Velocity predictors for predictive energy management in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology, 2015, 23(3): 1197-1204.
[17] BORHAN H, VAHIDI A, PHILLIPS A M, et al. MPC-based energy management of a power split hybrid electric vehicle[J]. IEEE Transactions on Control Systems Technology, 2012, 20(3): 593-603.
[18] 许绍航.无人履带式装甲输送车混合动力能量管理研究[D]. 北京：北京理工大学，2020.
XU S H, Research on Energy Management for unmanned hybrid electric tracked vehicle[D]. Beijing： Beijing Institute of Technology, 2020. (in Chinese)
[19] 龚建伟，姜岩，徐威著. 无人驾驶车辆模型预测控制[M].北京：北京理工大学出版社，2014.
GONG J W, JIANG Y, XU W. Model predictive control for self-driving vehicles[M]. Beijing: Beijing Institute of Technology Press, 2014. (in Chinese)
[20] 许绍航, 席军强，陈慧岩. 基于越野工况预测的混合动力履带车辆能量管理策略[J]. 兵工学报，2019, 40(8): 1572-1579.
XU S H, XI J Q, CHEN H Y. Energy management of hybrid electric tracked vehicle based on off-road condition prediction.[J]. Acta Armamentarii, 2019, 40(8):1572-1579. (in Chinese)
[21] 张玉.自动驾驶车辆混合运动规划研究[D]. 北京：北京理工大学，2019.
ZHANG Y. Hybrid motion planning for autonomous vehicles[D]. Beijing： Beijing Institute of Technology, 2019. (in Chinese)
[22] LI L, YANG C, ZHANG Y H, et al.Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus route[J]. IEEE Transactions on Vehicular Technology, 2015, 64(7): 2792-2803.

[1]	ZHENG Zhiwei, GUAN Xueyuan, FU Jian, MA Xunqiong, YIN Shang. Projectile Trajectory Prediction Based on CNN-LSTM Model [J]. Acta Armamentarii, 2023, 44(10): 2975-2983.
[2]	MA Guangyuan, CHEN Huiyan. Engine Control Technology of Unmanned Track Platforms [J]. Acta Armamentarii, 2022, 43(11): 2717-2726.
[3]	CHEN Luming, LIAO Zili, MA Xiaojun, LIU Chunguang. Hierarchical Control-based Real-time Energy Management Strategy for Hybrid Electric Vehicles [J]. Acta Armamentarii, 2021, 42(8): 1580-1591.
[4]	ZOU Yuan, ZHANG Bin, ZHANG Xudong, ZHAO Zhiying, KANG Tieyu, GUO Yufeng, WU Zhe. Energy Management of Hybrid Tracked Vehicle Based on Reinforcement Learning with Normalized Advantage Function [J]. Acta Armamentarii, 2021, 42(10): 2159-2169.
[5]	ZHANG Ruizeng, GONG Jianwei, CHEN Huiyan, WANG Boyang, LIU Haiou. Hill-start Control of a Medium Unmanned Tracked Vehicle with Single Circulating Moving [J]. Acta Armamentarii, 2020, 41(8): 1590-1599.
[6]	ZHAO Ziye, LIU Haiou, CHEN Huiyan. Research on Trajectory Prediction Method of Distributed High Speed Electric Drive Unmanned Tracked Vehicle in Off-roadConditions [J]. Acta Armamentarii, 2019, 40(4): 680-688.
[7]	HU Jiaming, HU Yuhui, CHEN Huiyan, LIU Kai. Research on Trajectory Tracking of Unmanned Tracked Vehicles Based on Model Predictive Control [J]. Acta Armamentarii, 2019, 40(3): 456-463.