(375z) Modeling and Simulation of Lithium Ion Batteries From a Systems Engineering Perspective

Lithium-ion
(Li-ion) batteries are becoming increasingly popular as a reliable source of
energy for portable electronic devices. Compared to alternative battery
technologies, Li-ion batteries provide one of the best energy-to-weight ratios,
exhibit no memory effect, and have low self-discharge when not in use. These
beneficial properties, as well as decreasing costs, have established Li-ion
batteries as a leading candidate for the next generation of automotive and
aerospace applications [1-2]. Li-ion batteries are also a good candidate for
green technology. Problems that persist with lithium-ion batteries include
underutilization, stress-induced material damage, capacity fade, and the
potential for thermal runaway [3].

Systems
engineering can be defined as a robust approach to the design, creation, and
operation of systems. The approach consists of the identification and
quantification of system goals, creation of alternative system design concepts,
analysis of design tradeoffs, selection and implementation of the best design,
verification that the design is properly manufactured and integrated, and
post-implementation assessment of how well the system meets (or met) the goals
[4]. Process systems engineering has been successfully employed for controlling
various engineering processes, and many efforts are working to apply these
methods to Li-ion battery design and operations.

The development
of new materials (including choice of molecular constituents and material nano-
and macro-scale structure), electrolytes, binders, and electrode architecture are
likely to contribute towards improving the performance of batteries. For a
given chemistry, the systems engineering approach can be used to optimize the electrode
architecture, operational strategies, cycle life, and device performance by
maximizing the efficiency and minimizing the potential problems mentioned
above.

The current
state of the art in modeling lithium-ion batteries (from continuum to
multiscale) will be reviewed and analyzed. In particular, the application of systems
engineering to physics-based first-principles models for the following
situations will be discussed:

1.    State
estimation of battery packs in real time,

2.    Parameter
estimation and capacity fade prediction,

3.    Dynamic
optimization of operating conditions (current or potential) to maximize battery
life,

4.    Optimal
spatial distribution of microstructure for enhanced performance,

5.    Model-based
optimal control,

6.    Reformulation
of models and algorithms for systems engineering needs.

Acknowledgements

The authors
acknowledge financial support by the National Science Foundation under grant
numbers CBET-0828002, CBET-0828123, and CBET-1008692 and I-CARES (WUSTL).

References

[1] J. M. Tarascon and M. Armand,
Nature, vol. 414, pp. 359?367, 2001.

[2] M. Armand and J.-M. Tarascon,
Nature, vol. 451, pp. 652?657, 2008.

[3] J. Newman, K. E. Thomas, H.
Hafezi, D. R. Wheeler, J. Electrochem. Soc. 150, A176 (2003).

[4] NASA Systems Engineering
Handbook. NASA. 1995. SP-610S.