Modeling and Simulation of Battery Systems

Partha P. Mukherjee, Sreekanth Pannala, and John A. Turner 

Modeling and simulation can play an important role in understanding and designing battery systems. Predictive simulations can complement experiments in accelerating and deployment of new battery designs and generally have the biggest impact where  

Over the decades, a wide range of cell chemistries and additives has been developed to optimize ceU performance for different apphcations. The selection of materials is targeted to improve a variety of performance characteristics energy/power density (see, for example. Ref. [1]), reduce impedance, reduce self-discharge, increase voltage, improve efficiency, reduce cost, increase cycle 

Handbook of Battery Materials, Second Edition. Edited by Claus Daniel and Jurgen O. Besenhard. 

For a typical LIB, for example, with graphite anode and LiCo02 cathode, the electrochemical reactions are as follows  

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Source Facinelli, W. A., Modeling and simulation of lead-acid batteries for photovoltaic systems. In Proc. 18th Intersociety Energy Conversion Engineering Conf., pp. 1582-1588. Orlando, FL, 1983. 

A thorough understanding of the microstructure of separators would be beneficial for modeling studies and optimization of electrochemical systems. This will help in making the battery model predictions more practical and reliable. The separator pore structure is usually very complex. It consists of a porous network of interconnected pores, which are filled with liquid electrolyte. A complete description of the pore structure would require a very intricate model. Simulations are only practically possible if a simplified quasi-continuum model involving a few parameters represents the strucmre. In such an approach, the effective electrolytic conductivity, Qgff, is often defined by [93]... 

In addition, a neuroconteoller based on a BP NN and modified proportional-integral-derivative (PID) controller was used to determine the ceU SOC. This algorithm was applied for the SOC determination of a LAB and validated with tests performed on actual cells. Results showed that the ANN-based battery model can adaptively simulate a battery system with high accuracy, and the predicted SOC quickly converges to the real value within a relative error of 1%. 

Figure 25.1 gives an overview of aU the computational tools available to model and simulate batteries, electrochemical systems, and materials at various length and time scales. The models used at various scales can be summarized as ... 

There are many reviews on mathematical models for hthium ion batteries. Botte et al. presented an extensive review on mathematical modeling of rechargeable lithium batteries. A review of mathematical models of lithium and nickel battery systems is discussed in hterature." " Experimental developments in the field can be found in a recent review article that describes new solutions, new measurement procedures and new materials for Li-ion batteries. " Apart from the enormous body of work on modehng of Li-ion batteries, efforts have also been made in making these continuum models more computationally efficient to simulate." Computationally efficient models can not only be used to predict battery behavior but can also be used in situations where real-time parameter estimation is needed, for example, situations where super accurate determination of State of Health (SOH) of a battery is critical, adding a new dimension to the capabilities of continuum models. 

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