Research and development of automatic optimal calibration system for new energy vehicles based on target working conditions

doi:10.16731/j.cnki.1671-3133.2025.07.007

Abstract

Abstract: In order to solve the current problem that most studies focus on the development of related hardware,communication protocol and upper computer software,too reliance on manual calibration parameters,the lack of automatic optimization calibration of the research problems, first by using an orthogonal trial design,the control parameter with great influence on the test index is selected as the input factor for the subsequent calibration. Secondly,a combined optimization algorithm is constructed to optimize the key parameters,with the combination of hardware and software,such as MicroAutoBox,Isight and MATLAB/Simulink,the automatic optimization calibration platform is designed. Finally,through the bench test verification,the automatic optimization calibration bench of new energy vehicles is built, the calibration time is reduced by 74.86 % compared with the manual calibration,realizing the high efficiency and automation of the calibration process. Under the target working condition,the power consumption of 100 kilometers is reduced by 4.3 % compared with that before the automatic optimization calibration.

Key words: new energy vehicle, bench model, orthogonal trial design, automatic optimization calibration system

CLC Number:

U462

REN Kefeng, XI Jianguo, XU Xianya, GAO Jianping. Research and development of automatic optimal calibration system for new energy vehicles based on target working conditions[J]. Modern Manufacturing Engineering, 2025, 538(7): 57-65.

References

[1] DUAN B,WANG Q,ZENG X,et al.Calibration methodology for energy management system of a plug-in hybrid electric vehicle[J]. Energy conversion and management,2017,136:240-248.
[2] BOZZA F,DE B V,MALFI E,et al.Optimal calibration strategy of a hybrid electric vehicle equipped with an ultra-lean pre-chamber SI engine for the minimization of CO₂ and pollutant emissions[J]. Energies,2020,13(15):4008.
[3] SAITEJA P,ASHOK B.Critical review on structural architecture,energy control strategies and development process towards optimal energy management in hybrid vehicles[J]. Renewable and Sustainable Energy Reviews,2022,157:112038.
[4] 高建平.基于目标工况的电动汽车自动优化标定技术研究[R]. 洛阳:河南科技大学,2021.
[5] 张俊峰.新能源汽车控制器标定系统的设计与实现[D]. 广州:华南理工大学,2016.
[6] 谢钊.基于CAN和OBD-Ⅱ的汽车数据采集标定及监控服务云平台系统研发[D]. 重庆:重庆大学,2023.
[7] 唐恒飞.基于CCP协议的新能源汽车控制器标定系统研究[D]. 上海:上海工程技术大学,2021.
[8] LI S,LIANG W,JIA S,et al.Optimization Method of Calibration Parameters for Hybrid Electric Vehicle[C]//2021 33rd Chinese Control and Decision Conference:CCDC 2021.Kunming,China:IEEE,2021.
[9] WANG Z,LV H,ZHOU X,et al.Design and modeling of a test bench for dual-motor electric drive tracked vehicles based on a dynamic load emulation method[J]. Sensors,2018,18(7):1993.
[10] 贺微.基于一维标识的汽车车轮定位参数视觉检测及线光场全局标定方法研究[D]. 长春:吉林大学,2023.
[11] 张栋江,于阳,焦鹏,等.电动汽车电动真空泵高原标定研究[J]. 时代汽车,2022(13):94-96.
[12] 张志强.面向智能汽车的多传感器联合标定算法研究[D]. 重庆:重庆大学,2022.
[13] 何洪文,韩陌,曹剑飞,等.双电机两挡驱动系统协同控制策略研究[J]. 重庆理工大学学报(自然科学版),2021,35(2):81-89.
[14] 赵剑飞,向茜,王爽.电动车用模块化轴向磁通永磁同步电机宽转速范围效率优化控制方法[J]. 中国电机工程学报,2023,43(14):5634-5643.
[15] 漆星,郑常宝,张倩.基于深度确信策略梯度的电动汽车异步电机参数标定方法[J]. 电工技术学报,2020,35(20):4266-4277.
[16] 高建平,赵金宝,杨松,等.基于驾驶意图的插电式混合动力公交车控制策略[J]. 机械工程学报,2016,52(24):107-114.
[17] 余佳衡,程相,孟垚,等.功率分流式混合动力系统参数匹配优化研究[J]. 现代制造工程,2021(9):33-39.
[18] 祁瑁富,孙远韬,田春雷,等.基于正交试验的850吨冷剪机剪切失效机理分析及其优化设计[J]. 中国机械工程,2024,35(4):752-759.
[19] 庞超明,黄弘.试验方案优化设计与数据分析[M]. 南京:东南大学出版社.
[20] 高建平,余佳衡,孟垚,等.电动车辆控制参数自动优化标定系统的研究及验证[J]. 中国机械工程,2022,33(1):118-125.
[21] 周珍林,苏鹏,刘卫洋,等.高集成、高柔性新能源汽车智驾产线标定工艺开发[J]. 汽车制造业,2024(4):47-49.