- CN：11-2187/TH
- ISSN：0577-6686
机械工程学报 ›› 2023, Vol. 59 ›› Issue (10): 134-151.doi: 10.3901/JME.2023.10.134
杨世春, 周思达, 周新岸, 李强伟, 陈飞, 曹耀光
收稿日期:2022-05-20
修回日期:2022-10-20
出版日期:2023-05-20
发布日期:2023-07-19
通讯作者:
陈飞(通信作者),男,1990年出生,博士,助理研究员。主要研究方向为新能源汽车动力系统控制与管理。E-mail:cf2020@buaa.edu.cn
E-mail:cf2020@buaa.edu.cn
作者简介:杨世春,男,1974年出生,教授,博士研究生导师。主要研究方向为新能源汽车能源动力系统基础理论与优化控制。E-mail:yangshichun@buaa.edu.cn;周思达,男,1996年出生,博士研究生。主要研究方向为新能源汽车 工程。E-mail:zhousida@buaa.edu.cn;周新岸,男,1997年出生,博士研究生。主要研究方向为新能源汽车 工程。E-mail:zhousida@buaa.edu.cn;曹耀光,男,1985年出生,博士,助理研究员。主要研究方向为动力电池云端管理与智能驾驶。E-mail:caoyaoguang@buaa.edu.cn
基金资助:YANG Shichun, ZHOU Sida, ZHOU Xinan, LI Qiangwei, CHEN Fei, CAO Yaoguang
Received:2022-05-20
Revised:2022-10-20
Online:2023-05-20
Published:2023-07-19
摘要： 发展新能源汽车是我国应对国家能源战略、实现碳中和目标的重大战略选择。动力电池是新能源汽车能源系统的核心,电池管理系统实现了动力电池状态估计、一致性管理、热管理、热失控监控等关键功能。动力电池全生命周期性能演变具有微观模糊性、演化复杂性、实际多变性的特点,基于嵌入式系统的电池管理系统受硬件条件限制,存储空间不足,使用简化模型,难以实现精确管理,车云融合的动力电池云端管理是目前电池管理的重要研究方向。围绕动力电池云端管控方法,系统分析电池云端模型构建方法、电池寿命及安全管理策略,并针对先进电子电气架构下的云端管理技术融合进行分析。通过对比分析当前电池管理技术综合优缺点,阐述面向云端管理的可行方案,为动力电池云端管控技术提供理论参考。
中图分类号:
杨世春, 周思达, 周新岸, 李强伟, 陈飞, 曹耀光. 动力电池云端管理关键技术研究综述[J]. 机械工程学报, 2023, 59(10): 134-151.
YANG Shichun, ZHOU Sida, ZHOU Xinan, LI Qiangwei, CHEN Fei, CAO Yaoguang. Research Progress of Cloud Management for Power Batteries on Electric Vehicles[J]. Journal of Mechanical Engineering, 2023, 59(10): 134-151.
| [1] BRUCH M,MILLET L,KOWAL J,et al. Novel method for the parameterization of a reliable equivalent circuit model for the precise simulation of a battery cell's electric behavior[J]. Journal of Power Sources,2021,490:229513. [2] JI H,JF G,RF E,et al. Classical and fractional-order modeling of equivalent electrical circuits for supercapacitors and batteries,energy management strategies for hybrid systems and methods for the state of charge estimation:A state of the art review[J]. Microelectronics Journal,2019,85:109-128. [3] LAI Xin,GAO Wenkai,ZHENG Yuejiu,et al. A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries[J]. Electrochimica Acta,2019,295:1057-1066. [4] LEE J,NAM O,CHO B H. Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering[J]. Journal of Power Sources,2007,174:9-15. [5] XU Jun,MI C C,CAO Binggang,et al. A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model[J]. Journal of Power Sources,2013,233:277-284. [6] Vyroubal P,Kazda T. Equivalent circuit model parameters extraction for lithium ion batteries using electrochemical impedance spectroscopy[J]. Journal of Energy Storage,2018,15:23-31. [7] Zhang Qi,Shang Yunlong,Li Yan,et al. A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries[J]. ISA Trans,2020,97:448-457. [8] Zhou Sida,Liu Xinhua,Hua Yang,et al. Adaptive model parameter identification for lithium-ion batteries based on improved coupling hybrid adaptive particle swarm optimization-simulated annealing method[J]. Journal of Power Sources,2021,482:228951. [9] Tran M K,Mevawala A,Panchal S,et al. Effect of integrating the hysteresis component to the equivalent circuit model of Lithium-ion battery for dynamic and non-dynamic applications[J]. Journal of Energy Storage,2020,32:101785. [10] WANG Shunli,Stroe D,Fernandez C. A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions[J]. Journal of Power Sources,2020,450:227652. [11] GAN Yanhua,WANG Jianqin,LIANG Jialin,et al. Development of thermal equivalent circuit model of heat pipe-based thermal management system for a battery module with cylindrical cells[J]. Applied Thermal Engineering,2020,164:114523. [12] ZHENG Yuejiu,SHI Zhihe,GUO Dongxu,et al. A simplification of the time-domain equivalent circuit model for lithium-ion batteries based on low-frequency electrochemical impedance spectra[J]. Journal of Power Sources,2021,489:229505. [13] Doyle M,Fuller T F,Newman J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society,1993,140:1526-1533. [14] LI Weihan,CAO Decheng,JOEST D,et al. Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries[J]. Applied Energy,2020,269:115104. [15] Haran B S,Popov B N,White R E. Determination of the hydrogen diffusion coefficient in metal hydrides by impedance spectroscopy[J]. Journal of Power Sources,1998,75:56-63. [16] HU Minghui,LI Yunxiao,LI Shuxian,et al. Lithium-ion battery modeling and parameter identification based on fractional theory[J]. Energy,2018,165:153-163. [17] 张立军,李文博,程洪正. 三维锂离子单电池电化学-热耦合模型[J]. 电源技术,2016,40(7):1362-1366,1490. ZHANG Lijun,LI Wenbo,CHENG Hongzheng. Coupled thermal-electrochemical model of 3D lithium-ion battery[J]. Chinese Journal of Power Sources,2016,40(7):1362-1366,1490. [18] CHEN Guangwei,LIU Zhitao,SU Hongye,et al. Electrochemical-distributed thermal coupled model-based state of charge estimation for cylindrical lithium-ion batteries[J]. Control Engineering Practice,2021,109:104734. [19] WU Longxing,LIU Kai,PANG Hui. Evaluation and observability analysis of an improved reduced-order electrochemical model for lithium-ion battery[J]. Electrochimica Acta,2021,368:137604. [20] LIU Yu,TANG Shui,LI Lixiang,et al. Simulation and parameter identification based on electrochemical- thermal coupling model of power lithium ion-battery[J]. Journal of Alloys and Compounds,2020,844:156003. [21] LI Dongdong,YANG Lin,LI Chun. Control-oriented thermal-electrochemical modeling and validation of large size prismatic lithium battery for commercial applications[J]. Energy,2021,214:119057. [22] ZHANG Qi,WANG Dafang,YANG Bowen,et al. Electrochemical model of lithium-ion battery for wide frequency range applications[J]. Electrochimica Acta,2020,343:136094. [23] LIANG Jialin,GAN Yunhua,TAN Meixian,et al. Multilayer electrochemical-thermal coupled modeling of unbalanced discharging in a serially connected lithium-ion battery module[J]. Energy,2020,209:118429. [24] HUA Xiao,ZHANG Cheng,OFFER G. Finding a better fit for lithium ion batteries:A simple,novel,load dependent,modified equivalent circuit model and parameterization method[J]. Journal of Power Sources,2021,484:229117. [25] XIAO Fei,LI Chaoran,FAN Yaxiang,et al. State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel[J]. International Journal of Electrical Power & Energy Systems,2021,124:106369. [26] Parthiban T,Ravi R,Kalaiselvi N. Exploration of artificial neural network[ANN] to predict the electrochemical characteristics of lithium-ion cells[J]. Electrochimica Acta,2007,53:1877-1882. [27] Pattipati B,Pattipati K,Christop-herson J P,et al. Automotive battery management systems[J]. IEEE Autotestcon,2008. [28] Saha B,Goebel K,Christophersen J. Comparison of prognostic algorithms for estimating remaining useful life of batteries[J]. Transactions of the Institute of Measurement and Control,2009,31:293-308. [29] Sánchez L,Couso I,González M. A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries[J]. Applied Soft Computing,2014,14:269-288. [30] WANG Junping,CHEN Quanshi,CAO Binggang. Support vector machine based battery model for electric vehicles[J]. Energy Conversion and Management,2006,47:858-864. [31] Klass V,Behm M,Lindbergh G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation[J]. Journal of Power Sources,2014,270:262-272. [32] YANG Duo,WANG Yujie,PAN Rui. State-of-health estimation for the lithium-ion battery based on support vector regression[J]. Applied Energy,2018,227:273-283. [33] QU X,SONG Y,LIU D. Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model[J]. Microelectronics Reliability,2020,114:113857. [34] LI Weihan,Rentemeister M,Badeda J,et al. Digital twin for battery systems:Cloud battery management system with online state-of-charge and state-of-health estimation[J]. Journal of Energy Storage,2020,30:101557. [35] BIILY W,Widanage D,YANG Shichun,et al. Battery digital twins:Perspectives on the fusion of models,data and artificial intelligence for smart battery management systems[J]. Energy and AI,2020(1):100016. [36] YANG Qingxia,XU Jun,LI Xiuqing,et al. State-of-health estimation of lithium-ion battery based on fractional impedance model and interval capacity[J]. International Journal of Electrical Power & Energy Systems,2020,119:105883. [37] SUN Daoming,YU Xiaoli,WANG Chongming,et al. State of charge estimation for lithium-ion battery based on an Intelligent adaptive extended Kalman Filter with improved noise estimator[J]. Energy,2021,214:119025. [38] JIANG Cong,WANG Shunli,WU Bin,et al. A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter[J]. Energy,2021,219:119603. [39] JIANG Cong,WANG Shunli,WU Bin,et al. Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter[J]. Applied Energy,2020,277:119603. [40] LI Xiaoyu,HUANG Zhijia,TIAN Jindong,et al. State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter[J]. Energy,2021,220:119767. [41] XIONG Rui,LI Linlin,YU Quanqing,et al. A set membership theory based parameter and state of charge co-estimation method for all-climate batteries[J]. Journal of Cleaner Production,2020,249:119380. [42] ZHANG Cheng,Allafi W,Dinh Q,et al. Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique[J]. Energy,2018,142:678-688. [43] YANG Fangfang,ZHANG Shaohui,LI Weihua,et al. State-of-charge estimation of lithium-ion batteries using LSTM and UKF[J]. Energy,2020,201:117664. [44] GUO Yngfang,HUANG Kai,HU Xiaoya. A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve[J]. Journal of Energy Storage,2021,36:102372. [45] XU Zhicheng,WANG Jun,LUND P,et al. Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data[J]. Energy,2021,225:120160. [46] Sandoval-Chileño M A,Castañeda L A,Luviano-Juárez A,et al. Robust state of charge estimation for Li-ion batteries based on extended state observers[J]. Journal of Energy Storage,2020,31:101718. [47] REN Hongbin,ZHANG Hongwei,GAO Zepeng,et al. A robust approach to state of charge assessment based on moving horizon optimal estimation considering battery system uncertainty and aging condition[J]. Journal of Cleaner Production,2020,270:122508. [48] CHEN Zhenggang,ZHOU Jianxiong,ZHOU Fei,et al. State-of-charge estimation of lithium-ion batteries based on improved H infinity filter algorithm and its novel equalization method[J]. Journal of Cleaner Production,2021,290:125180. [49] HU Lin,HU Xiaosong,CHE Yunhong,et al. Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering[J]. Applied Energy,2020,262:114569. [50] Wassiliadis N,Adermann J,Frericks A,et al. Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation:A use-case life cycle analysis[J]. Journal of Energy Storage, 2018,19:73-87. [51] LIU Boyang,TANG Xiaopeng,GAO Furong. Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model[J]. Electrochimica Acta,2020,344:136098. [52] DENG Zhongwei,HU Xiaosong,LIN Xianke,et al. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression[J]. Energy,2020,205:118000. [53] SONG Lingjun,ZHANG Keyao,LIANG Tongyi,et al. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis[J]. Journal of Energy Storage,2020,32:101836. [54] FENG Fei,TENG Sangli,LIU Kailong,et al. Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model[J]. Journal of Power Sources,2020,455:227935. [55] Zahid T,XU Kun,LI Weimin,et al. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles[J]. Energy,2018,162:871-882. [56] Ragone M,Yurkiv V,Ramasubramanian A,et al. Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling[J]. Journal of Power Sources,2021,483:229108. [57] GE Mingfeng,LIU Yiben,JIANG Xingxing,et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries[J]. Measurement,2021,174:109057. [58] 田君,高洪波,张跃强,等. 电动汽车动力锂离子电池寿命预测方法研究[J]. 电源技术,2020,44:767-770. TIAN Jun,GAO Hongbo,ZHANG Yueqiang,et al. Research of life prediction methods for power Li-ion battery in electric vehicles[J]. Chinese Journal of Power Sources,2020,44:767-770. [59] CHEN Lin,AN Jingjing,WANG Huimin,et al. Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model[J]. Energy Reports,2020,6:2086-2093. [60] 何志刚,魏涛,盘朝奉,等. 一种基于粒子滤波和多项式回归的锂离子电池剩余寿命间接预测方法[J]. 重庆理工大学学报(自然科学),2020,34:27-33. HE Zhigang,WEI Tao,PAN Chaofeng,et al. An indirect prediction method for remaining useful life of lithium-ion battery based on particle filter and polynomial regression[J]. Journal of Chongqing University of Technology (Natural Science),2020,34:27-33. [61] SUN Yongquan,HAO Xueling,PECHT M,et al. Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator[J]. Microelectronics Reliability,2018,88-90:1189-1194. [62] CHU A,Allam A,Cordoba A,et al. Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles[J]. Journal of Power Sources,2020,478:228991. [63] 陈亮亮. 基于半经验融合方法的锂离子电池剩余寿命预测[J]. 佳木斯大学学报(自然科学版),2019,37:142-145. CHEN Liangliang. Remaining life prediction of lithium-ion battery based on semi-empirical fusion method[J]. Journal of Jiamusi University(Natural Science Edition),2019,37:142-145. [64] LIN Chunpang,JAVIER C,YANG Fangfang,et al,Battery state of health modeling and remaining useful life prediction through time series model[J]. Applied Energy,2020,275:115338. [65] 马彦,陈阳,张帆,等. 基于扩展H_∞粒子滤波算法的动力电池寿命预测方法[J]. 机械工程学报,2019,55:36-43. MA Yan,CHEN Yang,ZHANG Fan,et al. Remaining useful life prediction of power battery based on extend H∞ particle filter algorithm[J]. Journal of Mechanical Engineering,2019,55:36-43. [66] 姜久春,高洋,张彩萍,等. 电动汽车锂离子动力电池健康状态在线诊断方法[J]. 机械工程学报,2019,55:60-72,84. JIANG Jiuchun,GAO Yang,ZHANG Caiping,et al. Online diagnostic method for health status of lithium-ion battery in electric vehicle[J]. Journal of Mechanical Engineering,2019,55:60-72,84. [67] 黄亮,李建远. 基于单粒子模型与偏微分方程的锂离子电池建模与故障监测[J]. 物理学报,2015,64:346-51. HUANG Liang,LI Jianyuan. Modeling and failure monitor of Li-ion battery based on single particle model and partial difference equations[J]. Acta Physica Sinica,2015,64:346-351. [68] MA Guijun,ZHANG Yong,CHENG Cheng,et al. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network[J]. Applied Energy,2019,253:113626. [69] HONG J,LEE D,JEONG E,et al. Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning[J]. Applied Energy,2020,278:115646. [70] LI Xiaoyu,ZHANG Lei,WANG Zhenpo,et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks[J]. Journal of Energy Storage,2019,21:510-518. [71] MA Jian,SHANG Pengchao,ZOU Xinyu,et al. A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries[J]. Applied Energy,2021,282:116167. [72] ZHANG Yongzhi,XIONG Rui,HE Hongwen,et al. Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries[J]. eTransportation,2019,1:100004. [73] LI Penghua,ZHANG Zijian,XIONG Qingyu,et al. State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network[J]. Journal of Power Sources,2020,459:228069. [74] XUE Zhiwei,ZHANG Yong,CHENG Cheng,et al. Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression[J]. Neurocomputing,2020,376:95-102. [75] Biensan P,Simon B,Peres J P,et al. On safety of lithium-ion cells[J]. Journal of Power Sources,1999,81-82:906-912. [76] 符兴锋,周斯加,龙江启. 电动汽车动力电池安全管理研究及验证[J]. 汽车技术,2013,40:44-49. FU Xingfeng,ZHOU Sijia,LONG Jiangqi. Power battery high voltage safety management for extended ranged electric vehicle[J]. Automobile Technology,2013,40:44-49. [77] 汪伟伟,姚丹,彭文. 锂离子动力电池国内外安全检测标准研究[J]. 金属功能材料. 2020,27:34-39. WANG Weiwei,YAO Dan,PENG Wen. Research on safety inspection standards of Li-ion traction battery[J]. Metallic Functional Materials,2020,27:34-39. [78] REN Dongsheng,FENG Xuning,LIU Lishuo,et al. Investigating the relationship between internal short circuit and thermal runaway of lithium-ion batteries under thermal abuse condition[J]. Energy Storage Materials,2021,34:563-573. [79] FENG Xuning,ZHENG Siqi,REN Dongsheng,et al. Investigating the thermal runaway mechanisms of lithium-ion batteries based on thermal analysis database[J]. Applied Energy,2019,246:53-64. [80] LI Yan,LIU Xiang,WANG Li,et al. Thermal runaway mechanism of lithium-ion battery with LiNi0.8Mn0.1Co0.1O2 cathode materials[J]. Nano Energy,2021,85:105878. [81] 王震坡,袁昌贵,李晓宇. 新能源汽车动力电池安全管理技术挑战与发展趋势分析[J]. 汽车工程,2020,42:1606-1620. WANG Zhenpo,YUAN Changgui,LI Xiaoyu. An analysis on challenge and development trend of safety management technologies for traction battery in new energy vehicles[J]. Automotive Engineering,2020,42:1606-1620. [82] 朱晓庆,王震坡,HSIN W,等. 锂离子动力电池热失控与安全管理研究综述[J]. 机械工程学报,2020,56:91-118. ZHU Xiaoqing,WANG Zhenpo,HSIN W,et al. Review of thermal runaway and safety management for lithium-ion traction batteries in electric vehicles[J],Journal of Mechanical Engineering,2020,56:91-118. [83] 朱贤春,曾超,曹华,等. 电池模组及电池包:中国,201920615109.4[P]. 2019-12-20. ZHU Xianchun,ZENG Chao,CAO Hua,et al. Battery module and battery pack:China,201920615109.4[P]. 2019-12-20. [84] 张涛,郭旭洋,吉壮壮,等. 动力电池热失控预警系统:中国,202021257931.7[P]. 2021-01-26. ZHANG Tao,GUO Xuyang,JI Zhuangzhuang,et al. Power battery thermal runaway warning system:China,202021257931.7[P]. 2021-01-26. [85] 陈泽宇,熊瑞,孙逢春. 电动汽车电池安全事故分析与研究现状[J]. 机械工程学报,2019,55:93-104. CHEN Zeyu,XIONG Rui,SUN Fengchun. Research status and analysis for battery safety accidents in electric vehicles[J]. Journal of Mechanical Engineering,2019,55:93-104. [86] 熊瑞,马骕骁,杨瑞鑫,等. 动力电池外部短路故障热-力影响与分析[J]. 机械工程学报,2019,55:115-125. XIONG Rui,MA Xiaoxiao,YANG Ruixin,et al. Thermo-mechanical influence and analysis of external short circuitfaults in lithium-ion battery[J]. Journal of Mechanical Engineering,2019,55:115-125. [87] 李志杰,陈吉清,兰凤崇,等. 机械外力下动力电池包的系统安全性分析与评价[J]. 机械工程学报,2019,55:137-148. LI Zhijie,CHEN Jiqing,LAN Fengsui,et al. Analysis and evaluation on system safety of power battery pack under mechanical loading[J]. Journal of Mechanical Engineering,2019,55:137-148. [88] 时玉帅,熊金峰,樊海梅. 动力电池常见故障分析与预警方法[J]. 广东化工,2019,46(13):115-116,113. SHI Yushuai,XIONG Jinfeng,FAN Haimei. Common fault analysis and early warning method for power battery[J]. Guangdong Chemical Industry,2019,46(13):115-116,113. [89] Finegan D P,Zhu J,Feng X,et al. The application of data-driven methods and physics-based learning for improving battery safety[J]. Joule,2021(5):316-329. [90] HUANG Wensheng,FENG Xuning,HAN Xuebing,et al. Questions and answers relating to lithium-ion battery safety issues[J]. Cell Reports Physical Science,2021(2):100285. [91] ANSGAR D,BERNARD B,STEFAN K,et al. Structured development and evaluation of electric/electronic- architectures of the electric power train[C]//IEEE Vehicle Power and Propulsion Conference (VPPC),October 15-18,2013,Beijing,China. New York:IEEE,2013:1938-8756. [92] 肖艳军,刘蕊,宋海平,等. 基于单片机的锂电池管理系统研究与试验[J]. 控制工程,2016,23:1001-1005. XIAO Yanjun,LIU Rui,SONG Haiping,et al. Research and testing of lithium battery management system based on microcontroller[J]. Control Engineering,2016,23:1001-1005. [93] 张传伟,李林阳. 电动汽车主从分布式电池管理系统设计[J]. 汽车技术,2017(5):45-50. ZHANG Chuanwei,LI Linyang. Design of master-slave distributed battery management system for electric vehicle[J]. Automobile Technology,2017(5):45-50. [94] WANG Jian,YANG Diange,LIAN Xiaomin. Research on electrical/electronic architecture for connected vehicles[C]//IET International Conference on Intelligent and Connected Vehicles,September 22-23,2016,Chongqing,China. London:IET,2016:131237. [95] 赵洪林,关志伟,杜峰,等. 智能电动汽车电子电气架构的设计与优化措施[J]. 汽车零部件,2019(6):20-23. ZHAO Honglin,GUAN Zhiwei,DU Feng,et al. Design and optimization of electronic and electrical architecture for intelligent electric vehicle[J]. Automobile Parts,2019(6):20-23. [96] 华一丁,龚进峰,戎辉,等. 国外智能汽车电子电气架构综述及分析[J]. 汽车电器,2018(12):6-13. HUA Yiding,GONG Jinfeng,RONG Hui,et al. Summary and analysis of foreign intelligent automotive electronic and electrical architecture[J]. Auto Electric Parts,2018(12):6-13. [97] 贾文伟,徐匡一,王海波,等. 智能汽车电子架构分析与研究[J]. 时代汽车,2020(4):43-46. JIA Wenwei,XU Kuangyi,WANG Haibo,et al. Analysis and research of intelligent vehicle electronic architecture[J]. Auto Time,2020(4):43-46. [98] 刘佳熙,丁锋. 面向未来汽车电子电气架构的域控制器平台[J]. 中国集成电路,2019,28:82-87. LIU Jiaxi,DING Feng. Domain controller platform for next generation automotive E/E architecture[J]. China Integrated Circuit,2019,28:82-87. [99] 匡小军,唐香蕉,周涛,等. 基于PREEvision的汽车电子电气架构工具链研究[J]. 汽车电器,2019(8):62-64. KUANG Xiaojun,TANG Xiangjiao,ZHOU Tao,et al. Research of vehicle electronic and electrical architecture tool chain based on PREEvision[J]. Auto Electric Parts,2019(8):62-64. |
| [1] | 石琴, 蒋正信, 刘翼闻, 魏宇江, 胡晓松, 贺林. 基于分数阶模型的锂离子电池荷电状态估计[J]. 机械工程学报, 2024, 60(8): 224-232,244. |
| [2] | 熊瑞, 李正阳, 燕润博, 杨瑞鑫. 全气候自热锂离子电池及低温极速加热策略研究[J]. 机械工程学报, 2024, 60(18): 247-257. |
| [3] | 蒋帅, 陈铖, 段砚州, 熊瑞. 温度和老化影响下的锂离子电池荷电状态和健康状态联合估计方法研究[J]. 机械工程学报, 2024, 60(18): 266-275. |
| [4] | 刘征宇, 张政, 郭乐凯, 孟辉, 刘项. 基于经验模态分解的锂离子电池健康状态预测[J]. 机械工程学报, 2024, 60(14): 272-281. |
| [5] | 彭纪昌, 刘凯龙, 孟锦豪, 刘海涛. 基于变参数结构的锂离子电池建模方法[J]. 机械工程学报, 2024, 60(14): 298-305. |
| [6] | 武骥, 方雷超, 刘兴涛, 陈佳佳, 刘晓剑, 吕帮. 基于特征优化和随机森林算法的锂离子电池SOH估计[J]. 机械工程学报, 2024, 60(12): 335-343. |
| [7] | 杨续来, 袁帅帅, 杨文静, 刘闯, 杨世春. 锂离子动力电池能量密度特性研究进展[J]. 机械工程学报, 2023, 59(6): 239-254. |
| [8] | 周航, 刘晓龙, 张梦迪, 孙金磊, 程泽. 基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法*[J]. 电气工程学报, 2023, 18(3): 332-340. |
| [9] | 庞晓青, 李佳承, 刘文学, 邓忠伟, 胡晓松. 模型与数据融合的大尺寸快充锂电池分布式温度估计[J]. 机械工程学报, 2023, 59(22): 20-32. |
| [10] | 张广续, 魏学哲, 陈思琦, 朱建功, 戴海峰. 高温循环老化对锂离子电池安全性影响研究[J]. 机械工程学报, 2023, 59(22): 33-45. |
| [11] | 何洪文, 王浩宇, 王勇, 李双歧. 基于实车运行数据的锂离子电池健康状态估计[J]. 机械工程学报, 2023, 59(22): 46-58. |
| [12] | 李毅超, 王楠, 段彬, 康永哲, 张承慧. 基于深度学习和粒子群算法的锂离子电池核温估计方法[J]. 机械工程学报, 2023, 59(22): 69-78. |
| [13] | 郭喆晨, 徐俊, 王行早, 徐梓铭, 梅雪松. 基于一维/三维热模型的平板热管/液冷电池热管理系统优化设计[J]. 机械工程学报, 2023, 59(22): 79-88. |
| [14] | 张言茹, 张珺玮, 王占国, 李劼峰, 张维戈. 基于历史数据的车载锂离子电池异常诊断[J]. 机械工程学报, 2023, 59(22): 89-99. |
| [15] | 谷平维, 段彬, 康永哲, 张承慧, 杜春水. 基于随机充电数据的锂离子电池容量在线估计[J]. 机械工程学报, 2023, 59(22): 100-110. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||
