Lithium-ion batteries are now extensively being used as the primary storage source. Capacity and power fade, and slow recharging times are key issues that restrict its use in many applications. Battery management systems (BMS) are critical to address these issues, along with ensuring its safety.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 as computationally complicated to be deployable in current BMS.^3-5

In our past work, we have developed physics based fast-solving electrochemical models which are computationally efficient.^6,7 We have also shown the development of optimal charging protocols^8-11 using these models, and have experimentally shown that such an approach to could lead to a reduction in battery footprint by atleast 20%, or an enhancement in cycle life by 40%. We have also demonstrated the fast-solving models and the optimal operation of batteries, on low-cost microcontrollers with a low memory footprint, which 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 significantly reduced for microgrids, making it more attractive to install the batteries.

This talk is aimed at the commercialization pathway of the developed next-generation BMS to the EV, consumer electronics and grid-scale market through a student-founded UW-based startup. 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).

References

1. K. Kelly, M. Mihalic and M. Zolot, in Proceeding of the 17th Annual Battery Conference on Applications and Advances (2002).
2. V. Pop, H. J. Bergveld, D. Danilov, P. P. L. Regtien and P. H. L. Notten, Battery management systems: accurate state-of-charge indication for battery powered applications, Springer Verlag (2008).
3. J. Newman, K. E. Thomas, H. Hafezi, and D. R. Wheeler, J. Electrochem. Soc., 150, A176 (2003).
4. M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc., 140, 1526 (1993).
5. T.F. Fuller, M. Doyle and J. Newman, J. Electrochem. Soc., 141, 1 (1994).
6. P.W.C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158(12), A1461 (2011).
7. P.W.C. Northrop, M. Pathak, D. Rife, S. De, S. Santhanagopalan and V. R. Subramanian, J. Electrochem. Soc., 162(6), A940 (2015).
8. L.T. Biegler, A.M. Cervantes, and A. Wachter, Advances in simultaneous strategies for dynamic process optimization, Chemical Engineering Science, 57(4):575, 2002.
9. B. Suthar, V. Ramadesigan, S. De, R. D. Braatz and V.R. Subramanian, Phy.Chem.Chem. Phy., 16(1), 277 (2014).
10. B. Suthar, P.W.C. Northrop, R.D. Braatz and V.R. Subramanian, J. Electrochem. Soc., 161(11), F3144 (2014).
11. M. Pathak, D. Sonawane, S. Santhanagopalan, R.D. Braatz and V.R. Subramanian, ECS Trans., 75(23), 51 (2017).