Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm
朱宗铠1,2, 何超1,2, 李加强1,2, 刘学渊1,2
1. School of Mechanics and Transport, Southwest Forestry University, Kunming Yunnan 650224, China; 2. Key Laboratory of Vehicle Environmental Protection and Safety in Plateau Mountain Area of Yunnan Provincial Colleges, Kunming Yunnan 650224, China
Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm
ZHU Zong-kai1,2, HE Chao1,2, LI Jia-qiang1,2, LIU Xue-yuan1,2
1. School of Mechanics and Transport, Southwest Forestry University, Kunming Yunnan 650224, China; 2. Key Laboratory of Vehicle Environmental Protection and Safety in Plateau Mountain Area of Yunnan Provincial Colleges, Kunming Yunnan 650224, China
摘要In view of the parameter estimation of gross vehicle mass and road gradient during the driving of battery electric vehicles, according to the characteristics of the vehicle mass with steady variability and road gradient with time variability, on the basis of vehicle longitudinal dynamics, an method for estimating gross vehicle mass and road gradient based on a hybrid algorithm is proposed, and the method is applied to the starting stage of battery electric vehicles. The gross vehicle mass is estimated by using the forgetting factor recursive least square algorithm with good estimation efficacy for steady variables, and the output result of mass is regarded as one of the input parameters for gradient estimation. The road gradient is estimated with adaptive Kalman filtering, and the noise influence that cannot be concluded with external noise statistical characteristics is reduced by introducing a noise estimator with forgetting factor to improve the estimation accuracy of road gradient. The electric vehicle starting test is carried out on the selected flat ground and micro-slope sections, and air resistance is ignored based on the characteristics of low speed during vehicle starting. The required data are collected with the Global Navigation Satellite System (GNSS) terminal and vehicle controller area network (CAN) bus to perform offline calculations. The result shows that (1) a road section with variable gradient is selected to verify the validity of the algorithm, and the gross vehicle mass estimation result of the section is converged within 10 kg of the true value eventually with small error in the gradient estimation result; (2) the estimations of gross vehicle mass and changing of road gradient can be accurately estimated by using hybrid algorithm; (3) the mass estimation result of the twice starting test has the same convergence trend and the mass error is less than 2%, although the gradient estimation error is increased slightly, it is still less than 0.6%, which indicates the high estimation accuracy at starting stage of battery electric vehicle by using the hybrid algorithm.
Abstract：In view of the parameter estimation of gross vehicle mass and road gradient during the driving of battery electric vehicles, according to the characteristics of the vehicle mass with steady variability and road gradient with time variability, on the basis of vehicle longitudinal dynamics, an method for estimating gross vehicle mass and road gradient based on a hybrid algorithm is proposed, and the method is applied to the starting stage of battery electric vehicles. The gross vehicle mass is estimated by using the forgetting factor recursive least square algorithm with good estimation efficacy for steady variables, and the output result of mass is regarded as one of the input parameters for gradient estimation. The road gradient is estimated with adaptive Kalman filtering, and the noise influence that cannot be concluded with external noise statistical characteristics is reduced by introducing a noise estimator with forgetting factor to improve the estimation accuracy of road gradient. The electric vehicle starting test is carried out on the selected flat ground and micro-slope sections, and air resistance is ignored based on the characteristics of low speed during vehicle starting. The required data are collected with the Global Navigation Satellite System (GNSS) terminal and vehicle controller area network (CAN) bus to perform offline calculations. The result shows that (1) a road section with variable gradient is selected to verify the validity of the algorithm, and the gross vehicle mass estimation result of the section is converged within 10 kg of the true value eventually with small error in the gradient estimation result; (2) the estimations of gross vehicle mass and changing of road gradient can be accurately estimated by using hybrid algorithm; (3) the mass estimation result of the twice starting test has the same convergence trend and the mass error is less than 2%, although the gradient estimation error is increased slightly, it is still less than 0.6%, which indicates the high estimation accuracy at starting stage of battery electric vehicle by using the hybrid algorithm.
基金资助:Supported by the National Natural Science Foundation of China (No.51968065)
通讯作者:
ZHU Zong-kai, HE Chao
引用本文:
朱宗铠, 何超, 李加强, 刘学渊. Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm[J]. Journal of Highway and Transportation Research and Development, 2023, 17(2): 48-55.
ZHU Zong-kai, HE Chao, LI Jia-qiang, LIU Xue-yuan. Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm. Journal of Highway and Transportation Research and Development, 2023, 17(2): 48-55.
[1] YANG Xiao-juan, YANG Dong-bo, JIA Hong, et al. Study on Reliability Analysis Technology for New Energy Vehicle Based on Big Data[J]. Journal of Highway and Transportation Research and Development, 2017,34(S2):123-127. (in Chinese) [2] BAE H S, RYU J, GERDES J C. Road Grade and Vehicle Parameter Estimation for Longitudinal Control Using GPS [C]// Proceedings of 2001 IEEE Conference on Intelligent Transportation Systems. Oakland: IEEE, 2001. [3] SAHLHOLM P, JOHANSSON K H. Road Grade Estimation for Look-ahead Vehicle Control Using Multiple Measurement Runs[J]. IFAC Proceedings Volumes, 2008, 41(2):3380-3385. [4] MANGAN S, WANG J, WU Q H. Measurement of the Road Gradient Using an Inclinometer Mounted on a Moving Vehicle[C]//Proceedings of 2002 IEEE International Symposium on Computer Aided Control System Design. Glasgow: IEEE, 2002. [5] LEI Yu-long, FU Yao, LIU Ke, et al. Vehicle Mass and Road Grade Estimation Based on Extended Kalman Filter [J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(11):9-13,8. (in Chinese) [6] HAO Sheng-qiang, LUO Pei-pei, XI Jun-qiang. Estimation of Vehicle Mass and Road Slope Based on Steady-s tate Kalman Filter[J]. Automotive Engineering, 2018,40(9):1062-1067,1075. (in Chinese) [7] LIU Ping, LAN Yu, LUO Li-quan, et al. Influence Factor Analysis of Kalman Filtering Slope Estimation for Electric Vehicle Starting Control[J]. Electronic Measurement Technology, 2020,43(24):11-17. (in Chinese) [8] VAHIDI A, STEFANOPOULOU A, PENG H. Recursive Least Squares with Forgetting for Online Estimation of Vehicle Mass and Road Grade: Theory and Experiments[J]. Vehicle System Dynamics, 2005, 43(1):31-55. [9] CHU Wen-bo, LUO Yu-gong, LUO Jian, et al. Vehicle Mass and Road Slope Estimates for Electric Vehicles[J]. Journal of Tsinghua University (Science and Technology), 2014,54(6):724-728. (in Chinese) [10] CHEN Hao, YUAN Liang-xin, SUN Tao, et al. Estimation of In-wheel Motor Driven Electric Vehicle Speed and Road Gradient[J]. Automotive Engineering, 2020,42(2):199-205,256. (in Chinese) [11] DAI Zhuo, WU Guang-qiang. Estimation Method of Road Slope and Vehicle Mass Based on Vehicle Dynamics [J]. Automobile Technology,2018(1):20-24. (in Chinese) [12] KIM S, SHIN K, YOO C, et al. Development of Algorithms for Commercial Vehicle Mass and Road Grade Estimation[J]. International Journal of Automotive Technology, 2017,18(6):1077-1083. [13] SUN Y, LI L, YAN B J, et al. A Hybrid Algorithm Combining EKF and RLS in Synchronous Estimation of Road Grade and Vehicle’Mass for a Hybrid Electric Bus[J]. Mechanical Systems and Signal Processing, 2016,68/69:416-430. [14] BITTANTI S, BOLZERN P, CAMPI M, et al. Deterministic Convergence Analysis of RLS Estimators with Different Forgetting Factors[C]//Proceedings of the 27th IEEE Conference on Decision and Control. Austin:IEEE, 1988. [15] REN Zhi-ying, SHEN Liang-liang, HUANG Wei, et al. Real Time Estimation of Vehicle Quality and Road Slope Based on Adaptive Extended Kalman Filter[J]. Journal of Vibration, Measurement and Diagnosis,2020,40(4):758-764,827. (in Chinese) [16] SUN En-xin, YIN Yu-ming, XIN Zhe, et al. Adaptive Joint Estimates of Vehicle Mass and Road Grades for Small Acceleration Driving Scenarios[J]. Journal of Tsinghua University (Science and Technology), 2022,62(1):125-132. (in Chinese) [17] DENG Zi-li. Theory and Application of Self-tuning Filtering: Modern Time Series Analysis Method[M]. Harbin: Harbin Institute of Technology Press, 2003. (in Chinese) [18] KRISHNA E H, SIVANI K, REDDY K A. On the Use of EMD Based Adaptive Filtering for OFDM Channel Estimation[J]. AEU-International Journal of Electronics and Communications, 2018,83:492-500. [19] WANG Cheng, ZHANG Cai-zhi. Road Grade Estimation Using Intelligent Algorithms for Fuel Cell Vehicles[J]. Journal of Chongqing University, 2021,44(4):10-18. (in Chinese) [20] LI B, ZHANG J, DU H, et al. Two-layer Structure Based Adaptive Estimation for Vehicle Mass and Road Slope under Longitudinal Motion[J]. Measurement,2017,95:439-455. [21] MCINTYRE M L, GHOTIKAR T J, VAHIDI A, et al. A Two-stage Lyapunov-based Estimator for Estimation of Vehicle Mass and Road Grade[J]. IEEE Transactions on Vehicular Technology,2009,58(7):3177-3185. [22] LIAO X, HUANG Q, SUN D, et al. Real-time Road Slope Estimation Based on Adaptive Extended Kalman Filter Algorithm with In-vehicle Data[C]//201729th Chinese Control and Decision Conference. Chongqing: IEEE,2017.
2023, Vol. 17 
