Joint Estimation of Freight Vehicle Mass and Road Slope under Complex Conditions

doi:10.3901/JME.2025.02.281

Abstract

Abstract: Freight vehicle mass and road slope estimation are the keys to improving vehicle control and energy conservation. Most of the existing road slope estimation studies focus on the longitudinal slope. There is a lack of research on slope estimation in the coupling scenario of longitudinal road slopes and continuous turning uphill and downhill. Therefore, aiming at the problem of mass estimation stability in the acceleration process of freight vehicles, the influence of different acceleration types on mass estimation is explored. A joint estimation method of M-estimation and forgetting factor recursive least square (FFRLS) is proposed to realize the robust estimation of freight vehicle mass. Furthermore, the problem of road slope estimation under complex conditions is analyzed based on the mass estimation results. Considering the coupled complex conditions, such as continuous turning uphill and downhill, longitudinal uphill and downhill, an adaptive multi-model fusion framework is proposed. By designing the Minimum Model Error (MME) criterion based on the vehicle longitudinal dynamics model, a freight vehicle error compensation model is built to solve the problems of transverse tire force and model error under a large longitudinal slope of freight vehicles under continuous turning uphill and downhill. A robust cubature Kalman filter (RCKF) estimation method based on Chi-square distribution multi-quantile data classification detection is proposed, which can solve the problem of sensor abnormal error interference in road slope estimation and realize the robustness of the road slope estimation algorithm. The results show that the proposed method has high recognition accuracy and satisfying robustness and can achieve a robust estimation of vehicle mass and road slope under complex working conditions.

Key words: freight vehicle, abnormal error, minimum model error criterion, M-estimate, adaptive multi-model fusion strategy, robust cubature Kalman filter

CLC Number:

U469

GAO Lin, WU Qing, HE Yi. Joint Estimation of Freight Vehicle Mass and Road Slope under Complex Conditions[J]. Journal of Mechanical Engineering, 2025, 61(2): 281-295.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.02.281

http://www.cjmenet.com.cn/EN/Y2025/V61/I2/281

References

[1] RITTER A，WIDMER F，VETTERLI B，et al. Optimization-based online estimation of vehicle mass and road grade：Theoretical analysis and experimental validation[J]. Mechatronics，2021，80：102663.
[2] ZHAO X，ZHAO X，YU Q，et al. Development of a representative urban driving cycle construction methodology for electric vehicles：A case study in Xi’an[J]. Transportation Research Part D：Transport and Environment，2020，81：102279.
[3] 张志勇，张淑芝，黄彩霞，等. 基于自适应扩展卡尔曼滤波的分布式驱动电动汽车状态估计[J]. 机械工程学报，2019，55(6)：156-165. ZHANG Zhiyong，ZHANG Shuzhi，HUANG Caixia，et al. State estimation of distributed drive electric vehicle based on adaptive extended kalman filter[J]. Journal of Mechanical Engineering，2019，55(6)：156-165.
[4] DRUZHININA M，STEFANOPOULOU A，MOKLEGAARD L. Adaptive continuously variable compression braking control for heavy-duty vehicles[J]. Journal of Dynamic Systems，Measurement，and Control，2002，124(3)：406-414.
[5] 雍文亮，管欣，王博，等. 基于多传感数据融合滤波的纵向坡度识别算法[J]. 机械工程学报，2018，54(14)：116-124. YONG Wenliang，GUAN Xin，WANG Bo，et al. Identification algorithm of longitudinal road slope based on multi-sensor data fusion filtering[J]. Journal of Mechanical Engineering，2018，54(14)：116-124.
[6] 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.
[7] 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.
[8] SUN Y，LI L，YAN B，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，2016，68：416-430.
[9] ZHANG Y，ZHANG Y，AI Z，et al. A cross iteration estimator with base vector for estimation of electric mining haul truck’s mass and road grade[J]. IEEE Transactions on Industrial Informatics，2018，14(9)：4138-4148.
[10] CAI L，WANG H，JIA T，et al. Two-layer structure algorithm for estimation of commercial vehicle mass[J]. Proceedings of the Institution of Mechanical Engineers，Part D：Journal of Automobile Engineering，2020，234(2-3)：378-389.
[11] JIANG S，WANG C，ZHANG C，et al. Adaptive estimation of road slope and vehicle mass of fuel cell vehicle[J]. Etransportation，201(2)：100023.
[12] HAO S，LUO P，XI J. Estimation of vehicle mass and road slope based on steady-state Kalman filter[C]//Proceedings of 2017 IEEE International Conference on Unmanned Systems，2017：582-587.
[13] 赵健，李至轩，朱冰，等. 基于交互多模型的车辆质量与道路坡度估计[J]. 中国公路学报，2019，32(12)：58-65. ZHAO Jian，LI Zhixuan，ZHU Bing，et al. Vehicle mass and road slope estimation based on interactive multi-model[J]. China Journal of Highway and Transport，2019，32(12)：58-65.
[14] JAUCH J，MASINO J，STAIGER T，et al. Road grade estimation with vehicle-based inertial measurement unit and orientation filter[J]. IEEE Sensors Journal，2018，18(2)：781-789.
[15] Li E，He W，Yu H，et al. Model-based embedded road grade estimation using quaternion unscented kalman filter[J]. IEEE Transactions on Vehicular Technology，2022，71(4)：3704-3714.
[16] 张志达，郑玲，李以农，等. 基于鲁棒自适应SCKF的智能汽车目标状态跟踪研究[J]. 机械工程学报，2021，57(20)：181-193. ZHANG Zhida，ZHENG Ling，LI Yinong，et al. Research on intelligent vehicle target state tracking based on robust adaptive SCKF[J]. Journal of Mechanical Engineering，2021，57(20)：181-193.
[17] 吴昊，陈树新，杨宾峰，等. 基于广义M估计的鲁棒容积卡尔曼滤波目标跟踪算法[J]. 物理学报，2015，64(21)：456-463. WU Hao，CHEN Shuxin，YANG Binfeng，et al. Robust cubature Kalman filter target tracking algorithm based on genernalized M-estiamtion[J]. Acta Physica Sinica，2015，64(21)：456-463.
[18] HE Y，YAN X，CHU D，et al. A probabilistic prediction model for the safety assessment of HDVs under complex driving environments[J]. IEEE Transactions on Intelligent Transportation Systems，2017，18(4)：858-868.
[19] 贺宜，LU Xiaoyun，褚端峰，等. 车路环境耦合作用下侧向动力学模型可靠性估计[J]. 汽车工程，2019，41(7)：800-806. HE Yi，LU Xiaoyun，CHU Duanfeng，et al. Reliability estimation of vehicle lateral dynamics under vehicle-road-environment coupling actions[J]. Automotive Engineering，2019，41(7)：800-806.
[20] 雷雨龙，付尧，刘科，等. 基于扩展卡尔曼滤波的车辆质量与道路坡度估计[J]. 农业机械学报，2014，45(11)：9-13，8. LEI Yulong，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)：9-14，8
[21] SONG R，FANG Y. Vehicle state estimation for INS/GPS aided by sensors fusion and SCKF-based algorithm[J]. Mechanical Systems and Signal Processing，2021，150：107315.
[22] 孙媛媛，尹志明. 基于M估计稳健回归的多谐波源责任区分[J]. 中国电机工程学报，2012，32(31)：166-173，233. SUN Yuanyuan，YIN Zhiming. Quantifying harmonic responsibilities of multiple harmonic sources based on m-estimation robust regression[J]. Proceedings of the CSEE，2012，32(31)：166-173，233.
[23] YANG X，WU C，HE Y，et al. A dynamic rollover prediction index of heavy-duty vehicles with a real-time parameter estimation algorithm using NLMS method[J]. IEEE Transactions on Vehicular Technology，2022，71(3)：2734-2748.
[24] Zhao J，Mili L. A robust generalized-maximum likelihood unscented Kalman filter for power system dynamic state estimation[J]. IEEE Journal of Selected Topics in Signal Processing，2018，12(4)：578-592.
[25] Pacejka H. Tire and vehicle dynamics[M]. New York，USA：Elsevier，2005.
[26] GUO K，LU D. UniTire：unified tire model for vehicle dynamic simulation[J]. Vehicle System Dynamics，2007，45：79-99.
[27] 任玥，冀杰，赵颖，等. 基于最小模型误差估计的智能汽车路径跟踪控制[J]. 汽车工程，2021，43(4)：580-587. REN Yue，JI Jie，Zhao Ying，et al. Path tracking control of intelligent vehicle based on minimal model error estimation[J]. Automotive Engineering，2021，43(4)：580-587.
[28] LIU W，HE H，SUN F. Vehicle state estimation based on minimum model error criterion combining with extended Kalman Filter[J]. Journal of the Franklin Institute，2016，353(4)：834-856.
[29] ARASARATNAM I，HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control，2009，54(6)：1254-1269.
[30] 汪䶮，殷国栋，耿可可，等. 基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计[J]. 机械工程学报，2019，55(22)：103-112. WANG Yan，YIN Guodong，GENG Keke，et al. Tire tateral forces and sideslip angle estimation for distributed drive electric vehicle using noise adaptive cubature Kalman filter[J]. Journal of Mechanical Engineering，2019，55(22)：103-112.
[31] 李开龙，胡柏青，高敬东，等. 基于M估计的非线性鲁棒检测卡尔曼滤波算法[J]. 计算机应用，2014，34(11)：3214-3217. LI Kailong，HU Baiqing，GAO Jingdong，et al. Nonlinear robust detection Kalman filter algorithm based on M-estimation[J]. Journal of Computer Applications，2014，34(11)：3214-3217.
[32] 张磊. 一类组合导航系统的非线性鲁棒滤波算法研究[D]. 南京：南京理工大学，2020. ZHANG Lei. Research on a class of nonlinear robust filter algorithms for integrated navigation sysytem[D]. Nanjing：Nanjing University of Science and Technology，2020.
[33] 褚文博，罗禹贡，罗剑，等. 电驱动车辆的整车质量与路面坡度估计[J]. 清华大学学报，2014，54(6)：724-728. CHU Wenbo，LUO Yugong，LUO Jian，et al. Vehicle mass and road slope estimates for electric vehicles[J]. Journal of Tsinghua University，2014，54(6)：724-728.
[34] ALTMANNSHOFER S，ENDISCH C. Robust vehicle mass and driving resistance estimation[C]//2016 American Control Conference (ACC). Boston，MA，USA：IEEE，2016：6869-6874.