基于分层控制的混合动力车辆实时能量管理策略

doi:10.3969/j.issn.1000-1093.2021.08.002

摘要/Abstract

摘要： 针对混合动力车辆中多动力源协调控制困难、燃油经济性不佳的问题，提出了一种基于分层控制的实时能量管理策略。通过分析车辆混合动力系统的部件特性，对不同动力源进行了数学建模；采用小波滤波层将负载需求功率分频为高、低两个部分，并将高频功率分配给超级电容等功率型动力源，将低频功率作为模型预测控制层的参考输入，以燃油经济性、电池荷电状态和母线电压为优化目标，求解柴油发电机组和动力电池组等能量型动力源的功率指令，依托dSPACE和RTLAB硬件在环仿真平台对能量管理策略进行验证。结果表明：在复合城市排放循环测试工况下，所提出的分层能量管理策略比基于模糊规则的能量管理策略燃油经济性提升了13.05%，比基于单一模型预测控制的能量管理策略燃油经济性提升了5.79%；分层能量管理策略下，能量型动力源的目标功率变化更加平缓，功率型动力源介入工作更加频繁，证明了该能量管理策略在提升燃油经济性和发挥动力源特性方面的有效性。

关键词: 混合动力车辆, 小波滤波, 模型预测控制, 能量管理策略

Abstract: A real-time energy management strategy based on hierarchical control is proposed for the coordinated control of multiple power sources and low fuel economy of hybrid electric vehicles (HEVs). The mathematical models of different power sources are established by analyzing the topology of whole vehicle and the component characteristics of hybrid electric system. The wavelet filter layer is used to divide the full load power into high and low parts, and the high-frequency power is allocated to power-type power sources such as supercapacitors. The low-frequency power is used as the reference input of the model predictive control (MPC) layer. The fuel economy, power battery state of charge (SOC), and DC bus voltage are the optimization items to obtain the optimal control instructions of energy-type power sources such as diesel engine-generator sets and power battery packs. Some representative simulation experiments were performed on dSPACE and RTLAB hardware-in-loop (HIL) simulation platform to verify the effectiveness of the proposed energy management strategy. The results show that, under the condition of CUEDC cycle driving, the fuel economy of the proposed hierarchical energy management strategy is increased by 13.05% compared with the energy management strategy based on fuzzy rules, and the fuel economy is improved by 5.79% compared with the energy management strategy based on a single MPC. Besides, the target power of each energy-type power source under the hierarchical energy management strategy changes more gently, while the power-type power source is involved in the power adjustment process more frequently, which proves the effectiveness of the proposed energy management strategy in improving fuel economy and exerting power source characteristics.

Key words: hybridelectricvehicle, waveletfiltering, modelpredictivecontrol, energymanagementstrategy

中图分类号:

TP27

陈路明，廖自力，马晓军，刘春光. 基于分层控制的混合动力车辆实时能量管理策略[J]. 兵工学报, 2021, 42(8): 1580-1591.

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.

参考文献

［1］臧克茂. 陆战平台全电化技术研究综述［J］. 装甲兵工程学院学报, 2011, 25(1): 1-7.
ZANG K M. Study on the all-electric technology of land warfare platform ［J］. Journal of Academy of Armored Force Engineering, 2011, 25(1): 1-7. (in Chinese)
［2］ SINGH K V, BANSAL H O, SINGH D. A comprehensive review on hybrid electric vehicles: architectures and components ［J］. Journal of Modern Transportation, 2019, 27(2): 77-107.
［3］王骞, 李顶根, 苗华春. 基于模糊逻辑控制的燃料电池汽车能量管理控制策略研究［J］. 汽车工程, 2019, 41(12): 1347- 1355.
WANG Q, LI D G, MIAO H C. Research on energy management strategy of fuel cell vehicle based on fuzzy logic control ［J］. Automotive Engineering, 2019, 41(12): 1347-1355. (in Chinese)
［4］江冬冬, 李道飞, 俞小莉. 基于混杂模型预测控制的混合动力车辆能量管理［J］. 吉林大学学报(工学版), 2020, 50(4): 1217-1226.
JIANG D D, LI D F, YU X L. Energy management of HEV based on hybrid model predictive control ［J］. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(4): 1217-1226. (in Chinese)
［5］陈征, 刘亚辉, 杨芳. 基于进化-增强学习方法的插电式混合动力公交车能量管理策略［J］.机械工程学报, 2017, 53(16): 86-93.
CHEN Z, LIU Y H, YANG F. Energy management strategy for plug-in hybrid electric bus with evolutionary-reinforcement learning method ［J］. Journal of Mechanical Engineering, 2017, 53(16): 86-93. (in Chinese)
［6］ WANG W W, YANG J, LIANG J Y, et al. Implementation of velocity optimization strategy based on preview road information to trade off transport time and fuel consumption for hybrid mining trucks ［J］. IET Intelligent Transport Systems, 2019, 13(1): 194-200.
［7］付立军, 刘鲁锋, 王刚, 等. 我国舰船中压直流综合电力系统研究进展［J］. 中国舰船研究, 2016, 11(1): 72-79.
FU L J, LIU L F, WANG G, et al. The research progress of the medium voltage DC integrated power system in China ［J］. Chinese Journal of Ship Research, 2016, 11(1): 72-79. (in Chinese)
［8］王守相, 孟子涵. 舰船综合电力系统分析技术研究现状与展望［J］. 中国舰船研究, 2019, 14(2): 107-117.
WANG S X，MENG Z H. Current status and prospects of analysis technologies of shipboard integrated power system ［J］. Chinese Journal of Ship Research, 2019, 14(2): 107-117. (in Chinese)
［9］马晓军, 袁东, 项宇, 等. 陆战平台综合电力系统及其关键技术研究［J］. 兵工学报, 2017, 38(2): 396-406.
MA X J, YUAN D, XIANG Y, et al. Research on integrated po-wer system and its key techniques of ground combat platform ［J］. Acta Armamentarii, 2017, 38(2): 396-406. (in Chinese)
［10］曾庆含, 马晓军, 袁东, 等. 双侧电驱动履带车辆运动解耦与变结构控制［J］. 控制理论与应用, 2015, 32(8): 1080-1089.
ZENG Q H, MA X J, YUAN D, et al. Motion decoupling and variable structure control of dual-motor electric drive tracked vehicle ［J］. Control Theory & Applications, 2015, 32(8): 1080- 1089. (in Chinese)
［11］王发成, 王子冬, 胡道中, 等. 车用动力电池组集总参数换热模型［J］. 兵工学报, 2014, 35(2): 145-151.
WANG F C, WANG Z D, HU D Z, et al. A lumped-parameter model of battery pack heat transfer in electric vehicles ［J］. Acta Armamentarii, 2014, 35(2): 145-151. (in Chinese)
［12］ RIBOLDI C E D. Energy-optimal off-design power management for hybrid-electric aircraft ［J］. Aerospace Science and Technology, 2019, 95: 105507.
［13］张卓然, 于立, 李进才, 等. 飞机电气化背景下的先进航空电机系统［J］. 南京航空航天大学学报, 2017, 49(5): 622-634.
ZHANG Z R, YU L, LI J C, et al. Key technologies of advanced aircraft electrical machine system for aviation electrification ［J］. Journal of Nanjing University of Aeronautics & Astronautics, 2017, 49(5): 622-634. (in Chinese)
［14］张征, 马晓军, 刘春光, 等. 基于分层模型的轮毂电机驱动车辆直接横摆力矩控制［J］. 农业机械学报, 2019, 50(12): 387-394.
ZHANG Z, MA X J, LIU C G, et al. Direct yaw moment control based on hierarchical model for in-wheel motor drive vehicles ［J］. Transactions of the Chinese Society for Agricultural Machi- nery, 2019, 50(12): 387-394. (in Chinese)
［15］ HASELTALAB A, NEGENBORN R R. Model predictive maneuvering control and energy management for all-electric autonomous ships ［J］. Applied Energy, 2019, 251: 113308.
［16］廖自力, 项宇, 刘春光, 等.电传动装甲车辆混合动力系统功率流控制策略［J］. 兵工学报, 2017, 38(12): 2289-2300.
LIAO Z L, XIANG Y, LIU C G, et al. Power flow control strategy of hybrid power system of electric drive armored vehicle ［J］. Acta Armamentarii, 2017, 38(12): 2289-2300. (in Chinese)
［17］ RIZOUG N, MESBAHI T, SADOUN R, et al. Development of new improved energy management strategies for electric vehicle battery/supercapacitor hybrid energy storage system ［J］. Energy Efficiency, 2018, 11(4): 823-843.
［18］ FENG Y B, DONG Z M. Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement ［J］. Energy, 2019, 189: 116276.
［19］张风奇, 胡晓松, 许康辉, 等.混合动力汽车模型预测能量管理研究现状与展望［J］. 机械工程学报, 2019, 55(10): 86-108.
ZHANG F Q, HU X S, XU K H, et al. Current status and prospects for model predictive energy management in hybrid electric vehicles ［J］. Journal of Mechanical Engineering, 2019, 55(10): 86-108. (in Chinese)
［20］ LARSSON V, JOHANNESSON L, EGARDT B. Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management ［J］. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1458-1467.
［21］ FRIES M, KRUTTSCHNITT M, LIENKAMP M. Operational strategy of hybrid heavy-duty trucks by utilizing a genetic algorithm to optimize the fuel economy multiobjective criteria ［J］. IEEE Transactions on Industry Applications, 2018, 54(4): 3668-3675.
［22］赵治国, 王茂垚, 郭元强, 等. 新型功率分流混合动力系统能量管理预测优化［J］. 西安交通大学学报, 2019, 53(1): 52-61,85.
ZHAO Z G, WANG M Y, GUO Y Q, et al. Predictive optimization of energy management strategy for new type power-split hybrid driveline system ［J］. Journal of Xi'an Jiaotong University, 2019, 53(1): 52-61,85. (in Chinese)
［23］房丽爽, 郭洪艳, 陈虹. 汽车动力学HiL仿真实验平台的搭建及应用［J］. 控制工程, 2015, 22(3): 375-381.
FANG L S, GUO H Y, CHEN H. Establishment and application of vehicle dynamic HiL simulation test platform ［J］. Control Engineering of China, 2015, 22(3): 375-381. (in Chinese)
［24］解少博, 刘通, 李会灵, 等. 基于马尔科夫链的并联PHEB预测型能量管理策略研究［J］. 汽车工程, 2018, 40(8): 871- 877,911.
XIE S B, LIU T, LI H L, et al. A study on predictive energy management strategy for a plug-in hybrid electric bus based on Markov chain ［J］. Automotive Engineering, 2018, 40(8): 871- 877,911. (in Chinese)
［25］ CHEN L M, LIAO Z L, XU H L. Hierarchical control strategy of on-board DC microgrid ［C］∥Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation. Changsha, China: Springer, 2017: 621-630.

[1]	马晓军，徐浩轩，刘春光. 串联式混合动力车辆发电机组协调控制策略[J]. 兵工学报, 2021, 42(10): 2075-2081.
[2]	刘聪，刘辉，韩立金，陈科. 分布式电驱动车辆极限越野环境下高速避障与稳定性控制[J]. 兵工学报, 2021, 42(10): 2102-2113.
[3]	邹渊，张彬，张旭东，赵志颖，康铁宇，郭玉枫，吴喆. 基于归一化优势函数的强化学习混合动力履带车辆能量管理[J]. 兵工学报, 2021, 42(10): 2159-2169.
[4]	王志福，白金，黄康伦，王军，王阳，梁常春，王瑞. 基于模型预测控制的轮毂电机驱动车辆防侧翻控制[J]. 兵工学报, 2021, 42(1): 11-25.
[5]	邓海鹏，麻斌，赵海光，吕良，刘宇. 自主驾驶车辆紧急避障的路径规划与轨迹跟踪控制[J]. 兵工学报, 2020, 41(3): 585-594.
[6]	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.
[7]	胡家铭，胡宇辉，陈慧岩，刘凯. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3): 456-463.
[8]	王威，陈慧岩，马建昊，刘凯，龚建伟. 基于Frenet坐标系和控制延时补偿的智能车辆路径跟踪[J]. 兵工学报, 2019, 40(11): 2336-2351.
[9]	黄大山，张进秋，刘义乐，张建. 车辆馈能悬挂系统滑模控制及能量管理策略研究[J]. 兵工学报, 2016, 37(12): 2185-2195.
[10]	王瑞，王义春，冯朝卿，张西龙. 混合动力履带车辆电机加热低温预热系统设计[J]. 兵工学报, 2015, 36(3): 398-404.
[11]	修观，王良明₁，孙瑞胜₁，杨荣军₁. 一种快速非线性模型预测控制律研究[J]. 兵工学报, 2011, 32(10): 1189-1194.