融合整车质量估计的电动汽车坡道识别

doi:10.13229/j.cnki.jdxbgxb.20230625

摘要/Abstract

摘要：

针对现有坡道识别算法工况适应性差，无法满足量产车应用要求等问题，提出了一种融合整车质量估计的电动汽车坡道识别方法。建立了车辆纵向动力学模型，分析了加速度传感器在实际车载条件下的信号特征。构建了带遗忘因子的最小二乘整车质量估计策略，实现了在起步工况下直接获取整车质量。针对静态驻车和动态行车两种驾驶场景分别设计了坡道识别算法，静态场景下采用滤波锁存策略应对车内活动等干扰因素，动态场景下设计了基于量测噪声自适应的Kalman滤波算法，实现了针对坡道的动力学观测和运动学观测的融合估计。通过Simulink-Carsim联合仿真验证了该方法的有效性。最后，在奇瑞新能源的量产纯电动汽车平台和域控制器上完成了实车测试，道路实验结果表明：该方法得到的整车质量估计误差在±10 kg以内，坡道静态误差小于0.001 rad，动态误差在0.005 rad以内，估计准确性和稳定性大幅提升，有效保障了电动汽车智能驾驶功能的环境适应性。

关键词: 电动汽车, 坡道识别, 质量估计, 自适应滤波

Abstract:

Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to satisfy the application requirements of mass production vehicles， an electric vehicle slope recognition method based on vehicle mass estimation was proposed. Firstly， the vehicle longitudinal dynamics model was established， and the signal characteristics of the acceleration sensor under the actual vehicle conditions were analyzed. The least square vehicle mass estimation strategy with forgetting factor was constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms were designed for static parking and dynamic driving scenarios. In the static scenario， the filter latch strategy was used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario， the Kalman filter algorithm based on measurement noise adaptation was designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method was verified by Simulink-CarSim joint simulation. Finally， the real vehicle test was completed on Chery new energy's mass production electric vehicle platform and domain controller. The road test results show that the mass estimation error is less than ±10 kg； the static estimation error of the slope is less than 0.001 rad， and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved， which ensures the environmental adaptability of the intelligent electric vehicles.

Key words: electric vehicle, slope recognition, mass estimation, adaptive filter

中图分类号:

U461.72

刘琳,任彦君,沈童,殷国栋,章友京. 融合整车质量估计的电动汽车坡道识别[J]. 吉林大学学报(工学版), 2025, 55(3): 846-856.

Lin LIU,Yan-jun REN,Tong SHEN,Guo-dong YIN,You-jing ZHANG. Electric vehicle ramp recognition based on fusion of vehicle mass estimation[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 846-856.

图/表 27

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表1

图11

图12

图13

图14

图15

图16

图17

图18

图19

图20

图21

图22

图23

图24

图25

图26

参考文献 13

1	林楠, 施树明, 马力, 等. 含坡度变化率信息的道路坡度估计[J]. 吉林大学学报: 工学版, 2016, 46(6):1845-1850.
	Lin Nan, Shi Shu-ming, Ma Li, et al. Road slope estimation with slope change rate information[J]. Journal of Jilin University (Engineering and Techgnology Edition), 2016,46(6): 1845-1850.
2	曾小华, 钱琦峰, 宋大凤, 等. 基于加速度校正的坡度估计算法与实现[J]. 汽车工程, 2020, 42(10):1369-1377.
	Zeng Xiao-hua, Qian Qi-feng, Song Da-feng, et al. Slope estimation algorithm and implementation based on acceleration correction[J]. Automotive Engineering, 2020, 42 (10):1369-1377.
3	Li E H, He W P, Yu H L. Model-based embedded road grade estimation using quaternion unscented Kalman filter[J]. IEEE Transactions on Vehicular Technology, 2022,71(4):3704-3714.
4	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 Wen-liang, Guan Xin, Wang Bo, et al. Longitudinal slope recognition algorithm based on multi-sensor data fusion filtering[J]. Journal of Mechanical Engineering, 2018,54(14): 116-124.
5	Guo J W, He C, Li J Q, et al. Slope estimation method of electric vehicles based on improved sage-husa adaptive Kalman filter[J]. Energies, 2022, 15(11): No.4126.
6	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: 自然科学版, 2014, 54(6): 724-728.
	Chu Wen-bo, Luo Yu-gong, Luo Jian, et al. Estimation of Vehicle mass and road slope for electric drive vehicles[J]. Journal of Tsinghua University (Natural Science Edition), 2014, 54(6):724-728.
7	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.
8	Li X, Ma J, Zhao X, et al. Intelligent Two-step estimation approach for vehicle mass and road grade[J]. IEEE Access, 2020, 8:218853-218862.
9	孙恩鑫, 殷玉明, 辛喆, 等. 微小加速度下汽车质量-道路坡度自适应估计[J]. 清华大学学报:自然科学版, 2022, 62(1): 125-132.
	Sun En-xin, Yin Yu-ming, Xin Zhe, et al. Adaptive estimation of vehicle mass road slope under small acceleration[J]. Journal of Tsinghua University (Natural Science Edition), 2022, 62(1): 125-132.
10	郝胜强, 罗培培, 席军强. 基于稳态卡尔曼滤波的车辆质量与道路坡度估计[J]. 汽车工程, 2018, 40(9): 1062-1067, 1075.
	Hao Sheng-qiang, Luo Pei-pei, Xi Jun-qiang. Vehicle mass and road slope estimation based on steady-state Kalman filtering[J] Automotive Engineering, 2018, 40(9):1062-1067, 1075.
11	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: No.102663.
12	王凡勋, 殷国栋, 沈童, 等. 四轮驱动电动汽车质心侧偏角与轮胎侧向力非线性鲁棒融合估计[J]. 中国机械工程,2022(22): 2673-2683.
	Wang Fan-xun, Yin Guo-dong, Shen Tong, et al. Nonlinear robust fusion estimation of center of mass slip angle and tire lateral force for four wheel drive electric vehicles[J]. China Mechanical Engineering,2022(22): 2673-2683.
13	任彦君, 殷国栋, 沙文瀚, 等. 基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法[J]. 机械工程学报, 2021,57(8): 184-194.
	Ren Yan-jun, Yin Guo-dong, Sha Wen-han, et al. Adaptive estimation method for longitudinal speed of four-wheel drive vehicles based on kinematic information fusion[J]. Journal of Mechanical Engineering, 2021,57(8): 184-194.

相关文章 15

[1]	李艳波,柳柏松,姚博彬,陈俊硕,渠开发,武奇生,曹洁宁. 考虑路网随机特性的高速公路换电站选址[J]. 吉林大学学报(工学版), 2023, 53(5): 1364-1371.
[2]	葛动元,向文江,李健,刘恩辰,姚锡凡. 基于机器视觉的电动汽车充电自动定位方法[J]. 吉林大学学报(工学版), 2023, 53(12): 3465-3471.
[3]	李杰,贾长旺,成林海,赵旗. 轮毂电机电动汽车主动悬架约束状态反馈H_∞控制[J]. 吉林大学学报(工学版), 2023, 53(1): 41-49.
[4]	庄伟超,丁昊楠,董昊轩,殷国栋,王茜,周朝宾,徐利伟. 信号交叉口网联电动汽车自适应学习生态驾驶策略[J]. 吉林大学学报(工学版), 2023, 53(1): 82-93.
[5]	刘兴涛,林思源,武骥,何耀,刘新天. 计及电池功率状态的再生制动优化策略[J]. 吉林大学学报(工学版), 2022, 52(12): 2796-2805.
[6]	李翠玉,胡雅梦,康亚伟,张德良. 应用自适应遗传算法的电动汽车充放电协同调度[J]. 吉林大学学报(工学版), 2022, 52(11): 2508-2513.
[7]	张必达,闫强,张琳,张海瑞. 基于实时交通信息的电动汽车充换电路径规划方法[J]. 吉林大学学报(工学版), 2022, 52(10): 2333-2342.
[8]	赵又群,李宇昊,邓汇凡,林涛,林棻. 基于Popov超稳定性的分布式电动汽车稳定性控制[J]. 吉林大学学报(工学版), 2022, 52(10): 2225-2233.
[9]	李明,薛庆峰,张可欣,吕然,韦长华. 电动汽车热泵空调系统性能分析[J]. 吉林大学学报(工学版), 2021, 51(6): 1943-1952.
[10]	李浩,陈浩. 考虑充电排队时间的电动汽车混合交通路网均衡[J]. 吉林大学学报(工学版), 2021, 51(5): 1684-1691.
[11]	王聪,马彦,王国光. 电动汽车充电站内的实时最优功率分配[J]. 吉林大学学报(工学版), 2021, 51(4): 1490-1495.
[12]	宋强,孙丹婷,章伟. 纯电动车机械式自动变速器换挡非线性建模及控制[J]. 吉林大学学报(工学版), 2021, 51(3): 810-819.
[13]	马苗苗,潘军军,刘向杰. 含电动汽车的微电网模型预测负荷频率控制[J]. 吉林大学学报(工学版), 2019, 49(5): 1644-1652.
[14]	席利贺,张欣,孙传扬,王泽兴,姜涛. 增程式电动汽车自适应能量管理策略[J]. 吉林大学学报(工学版), 2018, 48(6): 1636-1644.
[15]	常成,宋传学,张雅歌,邵玉龙,周放. 双馈电机驱动电动汽车变频器容量最小化[J]. 吉林大学学报(工学版), 2018, 48(6): 1629-1635.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

物理量	符号	数值
整备质量/kg	M	1 673
风阻系数	C_d	0.71
空气密度/［N·（s²·m^-4）］	ρ	1.2
车轮滚动半径/m	R_w	0.316
滚动阻力系数	f₀	0.008
旋转质量系数	δ	1.03