CAREER: Enabling High Performance Battery Charging Systems: Adaptive and Optimal Charging Algorithms Based on Dynamic Battery Characteristics

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Lithium-ion batteries are excellent energy storage devices due to their high energy density. Lithium-ion batteries are rather costly, and it is desirable to extend their cycle life as much as possible. However, conventional battery charging algorithms do not consider the time-varying electrochemical properties of batteries because the theory behind electrochemical behavior is complex and computationally challenging to incorporate within charging algorithms. The primary target of this project is to provide a new, innovative approach enabling high charging efficiency and long cycle life in electric vehicles, renewable energy storage, and building back-up power applications. Adaptive charging algorithms based on electrochemical parameters will enhance charging efficiency and reduce thermal stress by adjusting the charging current with respect to the internal state of the batteries. As a result, this project will not only provide a method for improved charging efficiency, which will enable increased cycle life, energy savings, and reduced maintenance costs; but also power electronics technologies will now be able to embed control capability with respect to time-varying electrochemical behavior in their charging algorithms without extra cost. It is our expectation that we will gain a profound understanding of the complex behavior occurring within high power battery stacks as a result of this research, and from this we may significantly improve the performance and affordability of such systems. Thus, the outcomes of this project are not only expected to fundamentally advance the field of battery charging algorithms, but also to have broad and highly positive societal impact. More specifically, the objective of this project is to investigate a novel framework that integrates dynamic battery impedance measurements into battery charging algorithms in order to enhance the charging efficiency, reliability, and cycle life of batteries. The variations in impedance are correlated with changes in the electrochemical state, and, particularly, the degree of conductivity within the battery. The research thrusts will be: 1) effective and efficient on-line measurement of the time-varying electrochemical impedance, 2) development of an adaptive sinusoidal charging algorithm, and 3) construction of a real-time hardware-in-the-loop test-bed for battery life cycle testing and validation of the concomitant adaptive charging algorithms.A fundamental understanding of battery charging and advanced energy interfaces will provide a significant leap in the development of charging algorithms supporting energy efficient, reliable, and long cycle life batteries. Moreover, results of this research will not only improve the capabilities of today's state-of-the-art energy storage technologies, but will be applicable to all battery chemistries because the internal phenomena of other electrochemical devices exhibit similarities. This ensures that this research will benefit energy storage system designers, regardless of their choice of materials and design. This also has the secondary benefit of improving the efficiency and lifespan of integrated renewable energy sources featuring electrochemical storage as a constituent component. The results of this research will be integrated into existing graduate and undergraduate courses related to power electronics and power systems. Beyond graduate/undergraduate student education, project based learning programs will be developed and disseminated via technical school presentations, K-12 student demonstrations, and summer school teacher workshops.