Overcoming the difference between the usable output of any energy storage systems and its theoretical energy content remains a critical challenge. The inefficiency typically results in the production of waste heat (q) rather than useful work (w). Minimization of inefficiency can be approached through optimization at the macro level, where bulk parameters are identified and varied. One approach to analyze multivariate systems is through optimization, often involving statistical methods such as design of experiments. This is an effective tool when the needed information regarding the parameters affecting the system is known. However, the diversity and intricacies of new and modified battery materials inherently reduce the probability of optimization experiments resulting in marketable products because the detailed operation mechanisms are unknown. Further, optimization will likely not yield insight into the complexities of electric energy storage systems, especially when considering any heterogeneity of ion and electron flux at the numerous interfaces over several scale lengths within a battery. This would require multiple characterization approaches used in concert, to assemble information gathered at the local or atomic level and link it to mesoscale characterization and finally system level performance. Thus, the ability to control and ultimately predict the behavior of complex systems demands not just parameterization at the bulk scale, but rather specific experimentation and understanding over multiple length scales within the same battery system, from the molecular- to the meso-scale through understanding the associated basic science. This presentation will examine insights and implications from multiscale investigations of energy storage related materials.