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Advances in the past decade of the energy and power densities of lithium-ion based batteries for hybrid electric vehicles and various consumer applications have been substantial. Rechargeable high rate lithium-ion batteries are now exceeding 6 kW/kg for short discharge times (<15 seconds). Rechargeable lithium-ion polymer batteries, for applications such as remote-control aircraft, are achieving simultaneously high energy density and high power density (>160 Whr/kg at >1.0 kW/kg). Some preliminary test data on a rechargeable lithium-ion polymer battery is presented. The use of high rate rechargeable lithium-ion batteries as a function of onboard power, electric laser power level, laser duty cycle, and total mission time is presented. A number of thermal management system configurations were examined to determine system level weight impacts. Lightweight configurations would need a regenerative thermal energy storage subsystem.

In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. A LIB is composed of several unit cells. Therefore, one of the most important factors that determine the performance of a LIB are the characteristics of the unit cell. The design of LIB cells is a challenging problem since it involves the evaluation of expensive black-box functions. These functions lack a closed-form expression and require long-running time simulations or expensive physical experiments for their evaluation. Recently, Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition function that guides the optimization.

As part of the DoD/NASA Lithium-Ion and More-Electric Aircraft (MEA) development programs, in-house life-testing and performance characterization of lithium-ion batteries of sizes 1-20 amp-hours (Ah) were performed. Using AC impedance spectroscopy, the impedance behavior of lithium-ion cells with respect to temperature, cycle number, electrode, and state-of-charge was determined. Cell impedance is dominated by the positive (cathode) electrode, increases linearly with cycle number, and exponentially increases with decreasing temperature. From cell performance testing, we have seen the cell behavior is extremely sensitive to the ambient temperature. Preliminary battery performance results as well as AC impedance and life cycle test results are presented below.

The performance and life of lithium-ion batteries is highly dependent on factors such as temperature, charge/ discharge rate, depth-of-discharge (DOD), charge cut-off voltage, and battery design. The purpose of this on-going investigation is to characterize the state-of-the-art in lithium-ion battery performance and life as a function of some of these factors. Cycle life data on 18650 cells as well as a four cell series connected 20 Ahr lithium-ion battery (16.4 volt) is presented. External cell temperatures as a function of discharge rate and location for 20 Ahr lithium-ion cell are given. Preliminary ac impedance results for the 20 Ahr cell are also given.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.