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