DOI: 10.3724/SP.J.1249.2018.05473

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2018/35:5 PP.473-479

The master control software for power lithium battery management system based on MCS D2P development platform

The traditional battery management system (BMS) development method not only is not conducive to the software implementation of advanced algorithms, but also has the disadvantages of long development cycle and high cost. To overcome these problems, the BMS hardware is built based on the MCS D2P rapid control prototype development platform. The power lithium battery is made up of multiple battery cells in series. In view of this characteristic, ① the BMS real-time measurements of battery voltage, current, cell voltage and temperature, ② the BMS real-time evaluation of battery residual power, ③ the main hardware structure composed of main control module, detection module and equalization management module are analyzed. On this basis, the data flow of master control software, the main program flow chart, the data acquisition flow chart and the state of charge (SOC) estimation flow chart are designed. Taking a 48 V, 50 Ah lithium iron phosphate battery pack consisting of 15 cells as the test object, the periodic charging and discharging test is designed. The test results show that both the measurement errors of the battery total voltage and current are less than ±0.5%, the measurement error of temperature is less than ±0.5℃, the difference between the measured value of SOC and the theoretical value of SOC is also within 8%, which verifies the feasibility and the correctness of the design of the master control software.

Key words:information processing technology,MCS D2P development platform,power lithium battery,battery management system,data acquisition,state of charge (SOC) estimation

ReleaseDate:2018-12-14 06:52:39

[1] 王秋霞.基于MCS 112PIN开发平台的锂动力电池管理系统设计[J].重庆理工大学学报自然科学版,2017,31(10):204-210. WANG Qiuxia. Li-ion power battery management system design based on the MCS 112PIN Development platform[J]. Journal of Chongqing Institute of Technology, 2017, 31(10):204-210.(in Chinese)

[2] 邓金伟.电动汽车用锂电池高效运行管理技术研究[D].淮南:安徽理工大学,2014. DENG Jinwei. Research of efficient operation management techniques of electric vehicle lithium battery[D]. Huainan:Anhui University of Science and Technology, 2014.(in Chinese)

[3] 王秋霞.电动汽车锂电池管理系统的设计研究[C]//福建省科协第15届学术年会:交通运输分会场.福州:福建省交通运输协会,2015:35-38. WANG Qiuxia. Research on the design of Li-ion battery management system for EV[C]//The 15th Annual Conference of Fujian Science and Technology Association:Compilation of Papers for Traffic and Transportation Conference. Fuzhou:Fujian Association for Traffic and Transportation, 2015:35-38.(in Chinese)

[4] Wood Ward. MotoHawk development and prototyping system resource guide & product manual 36333(Revision A)[M]. Fort Collins:Wood Ward, 2012.

[5] Texas Instruments Incorporated. Bq76PL455A-Q116-Cell EV/HEV integrated battery monitor and protector[M]. Dallas:Texas Instruments Incorporated, 2016.

[6] 詹从来, 龙伟, 丁远超, 等. 基于FPGA的多路数据采集与处理系统设计[J]. 深圳大学学报理工版, 2016, 33(2):127-133. ZHAN Conglai, LONG Wei, DING Yuanchao, et al. Design of multi-channel data collection and processing system based on FPGA[J]. Journal of Shenzhen University Science and Engineering, 2016, 33(2):127-133.(in Chinese)

[7] Feder D O, Hlavac M J. Analysis and interpretation of conductance measurements used to assess the state-of-health of valve regulated lead acid batteries[C]//Proceedings of the 16th International Telecommunications Energy Conference.[S.l.:s.n.], 1994:282-291.

[8] 季迎旭, 杜海江, 孙航. 蓄电池SOC估算方法综述[J]. 电测与仪表,2014,51(4):18-22. JI Yingxu, DU Haijiang, SUN Hang. A survey of state of charge estimation methods[J]. Electrical Measurement & Instrumentation, 2014, 51(4):18-22.(in Chinese)

[9] 肖雪峰, 肖伸平, 彭琼林. 基于μC/OS-Ⅱ操作系统纯电动汽车锂电池管理系统[J]. 湖南工业大学学报, 2013, 27(5):72-75. XIAO Xuefeng, XIAO Shenping, PENG Qionglin. Lithium battery management system for pure electric vehicle based on μC/OS-Ⅱ operating system[J]. Journal of Hunan University of Technology, 2013, 27(5):72-75.(in Chinese)

[10] 杨阳. 电动汽车用磷酸铁锂电池模型研究[D]. 西安:西安电子科技大学, 2015. YANG Yang. Study on model of LiFePO4 batteries used by electric vehicles[D]. Xi'an:Xidian University, 2015.(in Chinese)

[11] 陈岚,张谦,万国春,等.一种多采样率EKF的锂电池SOC估计[J].电源技术,2015,39(7):1381-1410. CHEN Lan, ZHANG Qian, WAN Guochun, et al. Stage-of-charge estimation of LiFePO4 battery using multirate extended Kalman filter[J]. Chinese Journal of Power Sources, 2015, 39(7):1381-1410.(in Chinese)

[12] 袁学庆, 张阳, 赵林, 基于EKF的锂电池SOC估算与试验研究[J]. 电源技术, 2015,39(12):2587-2615. YUAN Xueqing, ZHANG Yang, ZHAO Lin, et al. Li-ion battery SOC estimation and test research based on EKF[J]. Chinese Journal of Power Sources, 2015, 39(12):2587-2615.(in Chinese)

[13] 谭晓军. 电池管理系统深度理论研究——面向大功率电池组的应用技术[M]. 广州:中山大学出版社, 2014:5-6. TAN Xiaojun. Battery management systems on power batteries:applied technology and advanced theories[M]. Guangzhou:Publishing House of Zhongshan University, 2014:5-6.(in Chinese)

[14] 黄小平, 王岩. 卡尔曼滤波原理及应用——Matlab仿真[M]. 北京:电子工业出版社, 2015. HUANG Xiaoping, WANG Yan. The principle and application of Kalman filter:Matlab simulation[M]. Beijing:Publishing House of Electronics Industry, 2015.(in Chinese)