The race is on to develop and introduce alternative energy technologies. Despite the recent relaxation of oil prices (due to the global recession), the thrust toward adopting new energy sources continues and is about to receive a further boost from Congress. Yet, while the opportunities abound, there will be winners and losers. Only those companies quickest to deliver reliable and high performing batteries utilizing cost-effective manufacturing will thrive.
The challenges in lithium-ion battery development include finding the best electrolyte and electrode materials that boost capacity. Formulation problems for the electrolyte are challenging—although optimization methods for experimenting with mixtures do exist.
These materials must also behave safely in a wide variety of environments. The notorious safety problem involving laptop batteries is still fresh in consumers’ minds. A better job in robust design and/or manufacturing—or possibly the consideration of better separator materials could have prevented these problems.
Material expansion and contraction must be managed. The efficiency of cell charging must be optimized. Potential for cell damage must be minimized. The ability to manage internal and external heat sources must be refined. Aging and its consequences must be considered. Electrical and mechanical interfacing must be managed. Consumer misuse and abuse must be accounted for. A host of additional regulatory requirements will also constrain development.
Once the product has been successfully designed, another set of challenges emerges—notably, the ability to manufacture the product in large quantities or scale.
Many scientists and engineers are working feverishly to overcome the numerous obstacles inherent in alternative energy technologies and applications. But, just as with any product and process development plan, many steps are required to conceptualize, design, develop, test, and launch products.
Scientists and engineers spend years studying physical sciences, life sciences, and engineering disciplines such as chemistry, physics, mechanics, biology, biochemistry, thermodynamics, fluid mechanics, material science, etc. The underlying knowledge of these disciplines and their experience guides their research and development efforts.
However, few scientists and engineers specialize in optimization techniques that drastically reduce the time to develop optimal products (optimal in the sense of performance, reliability, and safety). These methods are found in the fields of Mathematics, Statistics, and Operations Research. Researchers should leverage the expertise in these areas by including mathematicians who apply optimization methods to engineering and product development challenges. Unfortunately, partnerships between researchers and optimization specialists are rare.
There are many quantitative methods that not only shorten the research & development process, but also result in optimal solutions (best performance, cost-effective, safe, reliable, and robust products). These methods can minimize the development time by strategically hunting down potentially fruitful combinations to experiment with. Rather than trying dozens or even hundreds of design/material combinations, these optimization methods identify just a few calculated combinations. From the data acquired through shortened experimentation, mathematical models are produced. These math models indicate solutions or an improved direction to proceed along.
Optimal solutions are found only by quantitatively understanding how design or process factors (that the designer has control over) impact the ultimate performance requirements. More importantly, design and process factors interact with each other to impact performance and safety, but many types of experiments do not reveal these critical interactions.
Ultimately, mathematical models that predict performance, reliability, and safety, are vital to the success of the development process. With these math models, all aspects of the design can be refined so that the best performance is achieved—while meeting all other safety and regulatory requirements.
Without these mathematical models, optimizing a single performance requirement (such as maximizing the energy density of a lithium-ion battery) is a challenge. To make matters worse, most products must meet multiple performance requirements, and the ability to simultaneously optimize over a large number of performance requirements is critical.
In addition to meeting a host of performance and regulatory requirements, the products must be safe, affordable, “manufacturable,” reliable, serviceable, etc. Tradeoffs between features and costs are inevitable. Optimizing one feature (such as minimizing lithium-ion battery charging time) may result in degradation of another performance characteristic (such as energy density of the cell).
Multi-response optimization is a technique commonly used to identify materials, dimensions, geometries, compositions, and manufacturing settings that will satisfy a large number of performance and cost requirements simultaneously. Essentially, it ensures feasibility and manages the inherent “tradeoffs” or compromises prevalent in the design and manufacture of products.
Hopefully, engineers and researchers in alternative energy development will consider the advantages of including mathematicians who specialize in product optimization on their teams. The potential gains include drastic reductions in development time and risk associated with safety and reliability.