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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.

Entertainment is THE killer application of consumer electronics in vehicles. Ever since the first AM vacuum tube radio was factory installed in a luxury vehicle in the early 1930s, entertainment has led the introduction of consumer electronics (CE) in the transportation industry. For example, over the last 68 years the auto industry has adopted FM reception; transistor radios; electronic tuning; cassette and CD playbacks; and multi-speaker, multi-channel-amplifier premium audio systems. During the “technology bubble” period (1998-2001), many industry experts predicted the next CE “killer app” in vehicles to be mobile Internet. Although mobile Internet is still a dream, significant new entertainment applications such as rear seat DVD video, satellite digital audio broadcast (XM & Sirius), and MP3 radio have exploded in the automotive and commercial vehicle markets.

The evolution of car radio in the past seven decades is a perfect illustration of the convergence of diverse technical fields: RF electronics, mobile wireless communications, the Internet, personal computers, consumer electronics, and automotive human machine interfaces. The early part of the radio evolution was driven by the need to improve the received audio signal quality while in the past two decades the driver has been to increase the channel capacity and to enhance the degree of personalization. Besides traditional AM/FM programming, today's radios also play a variety of media such as cassette tape, CD, MP3, DVD-A etc. as well as over 100 channels of satellite digital audio programs. Going forward, we believe that the radio will continue to be the entertainment center of the vehicle, and that the consumers are expecting to have access to personalized information anywhere and anytime.

Today’s automobiles include more electronics features and functions than at any time in history. From engine controller to crash sensing and passenger protection, all the way to automated driving, a complex network of electronic sensors and controls is being integrated into most of the vehicles. While many of these are necessary for increased comfort, convenience and safety, they must also be designed for the stringent quality requirements compared to standard consumer electronics. The business driven need for miniaturization with increased functionality but at reduced cost necessitates use of high density interconnection with advanced electronics components like Ball Grid Array (BGA) instead of many chip scale packages, which are potentially susceptible to failure while handling and shipping of the components. With the reduced mass of the component, accidental drop from the hand level would experience higher impact loading on the component to create significant damage.

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.