Power vs. Volumetric Density
Power solutions are notorious for being the long pole in the tent—for driving overall system size, volumetric efficiency, system bill-of-materials costs, and power density. In general, these are broken down into the common figures of merit (FOM) for a system, such as its size, weight, and power (a.k.a. – SWaP factors) characteristics. When combined with a cost metric, this can also be referred to as SWaP-C factors [1]. Power density is typically a function of total available power versus overall solution volume, which is why component size tends to have an inverse relationship with power density. The power density metric is taken a step further when combined with overall solution mass (typically translated to weight on Earth), which can be a critical FOM in nontethered applications, as is reviewed from many perspectives in the content that follows.
It is also good to differentiate power density from volumetric density, where the power density can be characterized specifically in the context of the power solution, which is a subset of the overall system volume. In general, power density ALWAYS goes up, where volumetric density can decrease as major system loads shrink in size (and perhaps in power requirements) and/or increase their functionality to perform more work in the same volume from generation to generation, lending themselves to a different trend than is seen directly for power solutions. The industry has tried to normalize these trend discrepancies with oversimplified, terrible metrics, such as dollar per watt ($/W), which makes little to no sense unless it makes highly similar power supply comparisons.
As with just any aspect of evaluating power solutions and assessing their technical impacts and financial contributions, it is crucial to look beyond first-order analysis. Power consumption and energy efficiency can often be a game of “whack-a-mole,” in which optimization in one subsystem may lead to a lesser performance in another area; thus, the effective system-level impact is the same or even worse when taking this approach. Some classic examples are when the enhanced power density of a wide-bandgap power switch, such as gallium nitride or silicon carbide, enables a physically smaller (even with increased power-handling capability) power train by taking advantage of increased switching frequencies that enable the reduction of some power components.
However, it may also consequently require a larger (and perhaps costlier) thermal mitigation solution to handle the denser power dissipation in smaller geometries or even push a system into requiring liquid cooling. It can often be the “little” features that are “nice-to-haves” but can have a disproportional impact on solution size and/or cost. For instance, connectors (especially blind-mate type) and fans can be very significant contributors to all the FOMs in a SWaP-C analysis since they can be large, and electromechanical components are also the bottlenecks for maximizing system quality and reliability.
Power solutions do not scale at the same rates we see for items on the load side, such as those driven by Moore’s Law and microelectromechanical system devices. This means that system roadmaps cannot plan for an exponential reduction of power solution size (or exponential increase in power density, conversely) due to nearly year-over-year process node improvements. That being said, a power solution can help keep the pace of enhanced load size/performance by meeting the increasing demands of loads in its own way [2].
It is also good to differentiate power density from volumetric density, where the power density can be characterized specifically in the context of the power solution, which is a subset of the overall system volume. In general, power density ALWAYS goes up, where volumetric density can decrease as major system loads shrink in size (and perhaps in power requirements) and/or increase their functionality to perform more work in the same volume from generation to generation, lending themselves to a different trend than is seen directly for power solutions. The industry has tried to normalize these trend discrepancies with oversimplified, terrible metrics, such as dollar per watt ($/W), which makes little to no sense unless it makes highly similar power supply comparisons.
As with just any aspect of evaluating power solutions and assessing their technical impacts and financial contributions, it is crucial to look beyond first-order analysis. Power consumption and energy efficiency can often be a game of “whack-a-mole,” in which optimization in one subsystem may lead to a lesser performance in another area; thus, the effective system-level impact is the same or even worse when taking this approach. Some classic examples are when the enhanced power density of a wide-bandgap power switch, such as gallium nitride or silicon carbide, enables a physically smaller (even with increased power-handling capability) power train by taking advantage of increased switching frequencies that enable the reduction of some power components.
However, it may also consequently require a larger (and perhaps costlier) thermal mitigation solution to handle the denser power dissipation in smaller geometries or even push a system into requiring liquid cooling. It can often be the “little” features that are “nice-to-haves” but can have a disproportional impact on solution size and/or cost. For instance, connectors (especially blind-mate type) and fans can be very significant contributors to all the FOMs in a SWaP-C analysis since they can be large, and electromechanical components are also the bottlenecks for maximizing system quality and reliability.
Power solutions do not scale at the same rates we see for items on the load side, such as those driven by Moore’s Law and microelectromechanical system devices. This means that system roadmaps cannot plan for an exponential reduction of power solution size (or exponential increase in power density, conversely) due to nearly year-over-year process node improvements. That being said, a power solution can help keep the pace of enhanced load size/performance by meeting the increasing demands of loads in its own way [2].
