[et_pb_section bb_built=”1″ inner_width=”auto” inner_max_width=”960px”][et_pb_row][et_pb_column type=”4_4″][et_pb_post_title admin_label=”SBG” _builder_version=”3.21″ meta=”off” author=”off” date=”off” \/][et_pb_video admin_label=”SBG Video” _builder_version=”3.25.1″ src=”https:\/\/youtu.be\/jXhTi8n5Yik” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” z_index_tablet=”500″ \/][et_pb_text admin_label=”Personnel” _builder_version=”3.21″]<\/p>\n
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Mo-Yuen Chow (PI), Bharat Balagopal, Cong-Sheng Huang, Habiballah Rahimi-Eichi, Hanlei Zhang<\/p>\n
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To develop a Smart Battery Gauge (SBG) that can continuously<\/strong><\/em> and accurately<\/strong><\/em> assess the State of Charge (SOC), State of Health (SOH) and Remaining Useful Life (RUL) of the battery when in use<\/strong><\/em>.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Funding” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_image admin_label=”Funding Image” _builder_version=”3.21″ src=”https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/Screen-Shot-2019-03-18-at-4.10.15-PM.png” max_width=”60%” align_last_edited=”on|desktop” align_tablet=”center” \/][et_pb_text admin_label=”Summary” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Summary text” _builder_version=”3.21″]<\/p>\n The SBG uses the Co-Estimation Algorithm, a combination of an adaptive parameter identification technique and an observer-based estimation method. Using these techniques, the Co-Estimation algorithm is able to acquire the terminal voltage, current and temperature of operation and identify the SOC and SOH in real-time and provide feedback on the RUL of the battery. Figure 1 shows the block diagram of the SBG.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=”4_4″][et_pb_text admin_label=”Timeline” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_image admin_label=”Timeline Image” _builder_version=”3.21″ src=”https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/timeline.png” align_last_edited=”on|desktop” align_tablet=”center” \/][et_pb_text admin_label=”Results” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Results text” _builder_version=”3.21″]<\/p>\n The SBG is able to adapt to real world operating conditions and battery aging and provide live and accurate assessment of the SOC of the battery within an error margin of 5%. The SBG has been successfully implemented in a Raspberry Pi and deployed at a working microgrid in North Carolina. Figure 2 shows the pilot implementation of the SBG. The innovation of the SBG is discussed in 25 journal and conference papers and 3 patents. The SBG has also generated 4 simulation software products and 3 hardware products.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=”1_2″][et_pb_text admin_label=”SBG Schematic Heading” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_image admin_label=”SBG schematic” _builder_version=”3.21″ src=”https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/sbg_schematic.png” align_last_edited=”on|desktop” align_tablet=”center” \/][\/et_pb_column][et_pb_column type=”1_2″][et_pb_text admin_label=”SBG Site Implementation Heading” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_image admin_label=”SBG Site Implementation” _builder_version=”3.21″ src=”https:\/\/research.ece.ncsu.edu\/wp-content\/uploads\/sites\/3\/2019\/03\/SBG-implementation.png” align_last_edited=”on|desktop” align_tablet=”center” \/][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=”4_4″][et_pb_text admin_label=”Impacts” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Impacts text” _builder_version=”3.21″]<\/p>\n The value proposition for stationary energy storage vendors is lowering the total cost of ownership by maximizing the useful life of batteries, increasing battery system uptime, reducing required maintenance and improving the reliability and safety of their products. Potential markets include utility scale storage, microgrids, solar + storage installations and electric vehicles. The SBG has been deployed in a pilot project and has a Technology Readiness Level of 8. ADAC has also partnered with multiple battery manufacturers to develop custom battery monitoring solutions. The SBG is in the technology transfer stage and is ready for commercialization.<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=”4_4″][et_pb_text admin_label=”Key References” _builder_version=”3.21″]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Key References text” _builder_version=”3.21″]<\/p>\n \n[1] C.-S. Huang, B. Balagopal, and M.-Y. Chow, \u201cEstimating Battery Pack SOC Using A Cell-to-Pack Gain Updating Algorithm,\u201d in IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society<\/em>, 2018, pp. 1807\u20131812.<\/p>\n [2] B. Balagopal and M. Y. Chow, \u201cThe state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries,\u201d in Proceeding – 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, Cambridge, UK, 2015, pp. 1302\u20131307.<\/p>\n [3] H. Rahimi-Eichi, F. Baronti, and M. Y. Chow, \u201cOnline adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells,\u201d IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 2053\u20132061, 2014.<\/p>\n [4] H. Rahimi-Eichi, U. Ojha, F. Baronti, and M. Chow, \u201cBattery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles,\u201d Ind. Electron. Mag. IEEE, vol. 7, no. 2, pp. 4\u201316, 2013.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Funding:<\/h3>\n
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