Abstract:Hydraulic control units (HCUs) are widely used as precise pressure regulators for the composite brakes in electric vehicles. The braking comfort can be improved by appropriate braking intention classification and identification. A braking intention classification method is developed to improve braking comfort that classifies the braking intention as normal deceleration, emergency braking and a pressure following pattern. The pressure control method is then based on the classification results. The on-line braking intention identification method uses multiple sensors and a neural network. Simulations and tests show that the braking comfort and safety are improved by this method.
潘宁, 于良耀, 宋健. 考虑舒适性的电动汽车制动意图分类与识别方法[J]. 清华大学学报(自然科学版), 2016, 56(10): 1097-1103.
PAN Ning, YU Liangyao, SONG Jian. Braking intention classification and identification considering braking comfort for electric vehicles. Journal of Tsinghua University(Science and Technology), 2016, 56(10): 1097-1103.
[1] Nakamura E, Soga M, Sakai A, et al. Development of Electronically Controlled Brake System for Hybrid Vehicle [R]. SAE Technical Paper 2002-01-0300, 2002.
[2] Ahn J K, Jung K H, Kim D H. Analysis of a regenerative braking system for hybrid electric vehicles using an electro-mechanical brake [J]. International Journal of Automotive Technology, 2009, 10(2): 229-234.
[3] 王猛, 孙泽昌, 卓桂荣, 等. 电动汽车制动能量回收系统研究[J]. 农业机械学报, 2012, 43(2): 6-10.WANG Meng, SUN Zechang, ZHUO Guirong, et al. Braking energy recovery system for electric vehicle [J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(2): 6-10. (in Chinese)
[4] Albrichsfeld C V, Karner J. Brake System for Hybrid and Electric Vehicles [R]. SAE Technical Paper 2009-01-1217, 2009.
[5] Oshima T, Fujiki N, Nakao S, et al. Development of an Electrically Driven Intelligent Brake System [R]. SAE Technical Paper 2011-01-0568, 2011.
[6] Kunz M, Willmann K H, Wienken H, et al. Modular brake system approach for automated parking and automated driving [C]//5th International Munich Chassis Symposium 2014. Wiesbaden, Germany, 2014: 633-646.
[7] 张彪, 张俊智, 李守波. 基于ESP压力调节器制动能量回馈系统[J]. 清华大学学报: 自然科学版, 2011, 51(5): 710-714.ZHANG Biao, ZHANG Junzhi, LI Shoubo. Regenerative braking system based on ESP pressure modulator [J]. J Tsinghua Univ: Sci and Tech, 2011, 51(5): 710-714.(in Chinese)
[8] Cantoni C, Cesarini R, Mastinu G, et al. Brake comfort: A review [J]. Vehicle System Dynamics, 2009, 47(8): 901-947.
[9] Yoshida H, Sugitani T, Ohta M, et al. Development of the Brake Assist System [R]. SAE Technical Paper 980601, 1998.
[10] Donges E. A conceptual framework for active safety in road traffic [J]. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 1999, 32(2/3): 113-128.
[11] 孙磊. HEV驾驶员制动意图识别及控制算法研究[D]. 长春: 吉林大学, 2012.SUN Lei. Study on Driver Braking Intention Identification and Control Algorithm for HEV [D]. Changchun: Jilin University, 2012. (in Chinese)
[12] 张元才, 余卓平, 徐乐, 等. 基于制动意图的电动汽车复合制动系统制动力分配策略研究[J]. 汽车工程, 2009, 31(3):244-249.ZHANG Yuancai, YU Zhuoping, XU Le, et al. A study on the strategy of braking force distribution for the hybrid braking system in electric vehicles based on braking intention [J]. Automotive Engineering, 2009, 31(3): 244-249. (in Chinese)
[13] GAO Yimin, CHEN Liping, Ehsani M. Investigation of the Effectiveness of Regenerative Braking for EV and HEV [R]. SAE Technical Paper 1999-01-2910, 1999.
[14] 姚亮. 混合动力轿车再生制动与液压制动协调控制策略研究[D]. 长春: 吉林大学, 2009.YAO Liang. Study on the Integrative Control Strategy of Regenerative Braking and Hydraulic Braking for Hybrid Electric Vehicle [D]. Changchun: Jilin University, 2009. (in Chinese)
[15] 李寿涛, 郭立书, 徐辉, 等. 基于模糊逻辑的驾驶员紧急制动意图识别[J]. 仪器仪表学报, 2009, 30(6): 218-221.LI Shoutao, GUO Lishu, XU Hui, et al. Identification of drivers' emergency braking intentions based on fuzzy logic [J]. Chinese Journal of Scientific Instrument, 2009, 30(6): 218-221. (in Chinese)
[16] XU Guoqing, LI Weimin, XU Kun, et al. An intelligent regenerative braking strategy for electric vehicles [J]. Energies, 2011, 4: 1461-1477.
[17] Roody S S. Modeling Drivers' Behavior during Panic Braking for Brake Assist Application, Using Neural Networks and Logistic Regression and a Comparison [R]. SAE Technical Paper 2011-01-2384, 2011.
[18] GAO Yimin, Ehsani M. Electronic Braking System of EV and HEV: Integration of Regenerative Braking, Automatic Braking Force Control and ABS [R]. SAE Technical Paper 2001-01-2478, 2001.
[19] Jones S, Kural E, Knodler K, et al. Optimal energy efficiency, vehicle stability and safety on the OpEneR EV with electrified front and rear axles [M]//Advanced Microsystems for Automotive Applications 2013. Berlin: Springer International Publishing, 2013: 269-284.
[20] 史春朝. BP神经网络算法的改进及其在PID控制中的应用研究[D]. 天津: 天津大学, 2006.SHI Chunzhao. Study on Algorithm Improvement of BP Neural Networks and Its Application in PID Control [D]. Tianjin: Tianjin University, 2006. (in Chinese)
[21] 宗长富, 杨肖, 王畅, 等. 汽车转向时驾驶员驾驶意图辨识与行为预测[J]. 吉林大学学报: 工学版, 2009, 39(1): 27-32.ZONG Changfu, YANG Xiao, WANG Chang, et al. Driving intentions identification and behaviors prediction in car lane change [J]. Journal of Jilin University: Engineering and Technology Edition, 2009, 39(1):27-32. (in Chinese)
[22] 李亚秋, 吴超仲, 马晓凤, 等. 基于EKF学习方法的BP神经网络汽车换道意图识别模型研究[J]. 武汉理工大学学报: 交通科学与工程版, 2013, 37(4): 843-847.LI Yaqiu, WU Chaozhong, MA Xiaofeng, et al. A recognition model for lane change intention based on neural network with EKF algorithm [J]. Journal of Wuhan University of Technology: Transportation Science & Engineering, 2013, 37(4): 843-847. (in Chinese)
[23] Pacejka H B, Bakker E. The magic formula tyre model [J]. Vehicle System Dynamics, 1992, 21(S1): 1-18.
[24] 王伟玮. ESC液压执行单元的动态特性分析与综合仿真平台的建立[D]. 北京: 清华大学, 2011.WANG Weiwei. Dynamics Analysis on Electronic Stability Control System Hydraulic Control Unit and Establishing an Integrated Simulation Platform [D]. Beijing: Tsinghua University, 2011. (in Chinese)