陈龙,朱斌,孙晓东,汪少华.基于模型预测控制的多电机驱动系统能量最优分配策略[J].农业机械学报,2018,49(10):403-409.
CHEN Long,ZHU Bin,SUN Xiaodong,WANG Shaohua.Optimal Allocation Strategy for Multi-motor Drive System Based on Model Predictive Control[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):403-409.
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基于模型预测控制的多电机驱动系统能量最优分配策略   [下载全文]
Optimal Allocation Strategy for Multi-motor Drive System Based on Model Predictive Control   [Download Pdf][in English]
投稿时间:2018-05-25  
DOI:10.6041/j.issn.1000-1298.2018.10.047
中文关键词:  纯电动汽车  永磁同步电机  多电机驱动系统  能量最优  扭矩分配
基金项目:国家自然科学基金项目(U1564201、51875261)和江苏省杰出青年基金项目(BK20180046、BK20170071)
作者单位
陈龙 江苏大学 
朱斌 江苏大学 
孙晓东 江苏大学 
汪少华 江苏大学 
中文摘要:为了解决传统电动汽车单电机驱动系统高效区无法覆盖汽车行驶工况点的问题,提出了一种基于模型预测控制的纯电动汽车多电机驱动系统能量最优分配策略。首先,以整个多电机驱动系统为研究对象,建立了电机模型和汽车纵向动力学模型,并讨论了采用高效区不同的前后轴电机时提高整车效率的方法。其次,通过台架实验标定出电机在特定转速-转矩工作点的效率,通过引入前后轮驱动力分配比α,将两张电机的效率图转换为整车的车速-驱动力效率图。再次,对模型预测转矩控制下的永磁同步电机系统进行了理论分析与仿真验证。最后,通过硬件在环实验,验证了能量最优分配策略对整车效率以及续航里程提升的有效性。
CHEN Long  ZHU Bin  SUN Xiaodong  WANG Shaohua
Jiangsu University,Jiangsu University,Jiangsu University and Jiangsu University
Key Words:electric vehicle  permanent magnet synchronous motor  multi-motor drive system  energy optimization  torque distribution
Abstract:In order to solve the problem that the driving condition of the electric vehicle (EV) cannot be covered by the high efficiency area of traditional single motor drive system, an optimal allocation strategy for the multi-motor drive system of EV was presented based on model predictive control. Firstly, taking the whole multi-motor drive system as research object, the model of motor and longitudinal dynamic model of automobile were established. The method to improve the efficiency of the vehicle with different front and rear axle motors in the high efficiency area was discussed. Secondly, the efficiency of the motor at specific speed and torque operation points was calibrated by the bench test. It was found that the high efficiency ranges of the two motors covered the working operation points of high speed cruising and low speed startup conditions, respectively. By introducing the driving force distribution ratio of front and rear motors, the efficiency diagram of the two motors was transformed into the vehicle driving efficiency diagram. Thirdly, theoretical analysis and simulation verification of permanent magnet synchronous motor (PMSM) system with model predictive torque control were carried out, showing that the method can ensure the rapid response of torque. Finally, the validity of the range improvement and vehicle efficiency were verified by the hardware in the loop (HIL) experiments. The results showed that the driving force distribution can integrate the high efficiency range of two motors and improve the efficiency of vehicle. Moreover, the model predictive torque control (MPTC) based on the motor benchs parameters can suppress the torque ripple in the low speed range and improve the accuracy of the output torque. The results can provide theoretical basis for the design of distributed driving system.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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