Model Based Battery Management Systems (BMS) - from Theory to Commercialization
Authors: Pathak Manan , Elli Neil-Dawson , Jang Tae-Jin , Pathak Chintan , Subramanian Venkat R. 
Published: February 2020

Battery Management Systems (BMS) are critical to the performance of lithium-ion batteries in any application, ranging from cell phones to EVs to giant batteries in an electric grid.1 An efficient BMS should be able to accurately predict the internal states of the battery such as the Sate-of-Charge (SoC), State-of-Health (SoH) using the voltage and current measurements from the battery, along with maintaining safe operations of the battery.2 The accurate determination of these internal states holds the key for the optimal performance of batteries. The effect of inaccurate state estimation leads to conservative use of batteries and at worst, may cause potentially unsafe situations leading to thermal runaway.2,3 This necessitates the development of accurate models that could predict different states of the battery more accurately, which at the same time are not computationally as complicated to be deployed in current BMS.3-5

In our past work, we have developed physics based reformulated models which are computationally extremely efficient.6,7 We have also shown the development of optimal charging protocols8-10using these models, and have experimentally shown that such an approach could lead to a reduction in battery footprint by atleast 20%, or an enhancement in cycle life by 40%. We have also demonstrated the reformulated models and the optimal operation of batteries on low-cost microcontrollers that could control the batteries in real time, mimicking an actual BMS.
Based on a detailed technology to market analysis, this decrease in footprint by 20% has significant impact in EVs, HEVs and grid applications. CAPEX cost can be reduced for microgrids.
This talk is aimed at the commercialization pathway of the development of next-generation BMS for grid-scale and EV Li-ion battery deployments through a student-founded start-up named Battery Informatics, Inc (Bii). The interplay between the fundamental depth in modeling, choice of numerical algorithms, application driven problem formulation and impact in industries will be presented.

Acknowledgements

The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 along with the Clean Energy Institute (CEI) at the University of Washington (UW) and also the Washington Research Foundation.

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