Microsoft, Tesla say software-defined batteries could redirect power on the fly
pcworld.com
pcworld.com
What I mean is: surely a "software-defined" battery would be some kind of redox/flow battery where you can literally adjust the physical properties of the battery to suit the current SOC and current/anticipated system demands.
Don't get me wrong, we need more work like this, particularly if Tesla powerwall home batteries and the like are going to become a thing that we don't want filling landfills with avoidable charge/discharge cycles.
But wow, SDx. I mean, SDR is obvious; software-defined radios clearly replace fixed hardware with programmable IFs and tonnes of DSP. Similarly, networks can be software-defined if they replace discrete stand-alone equipment with fewer tiers of more capable hardware delineated more by connectivity/performance than function.
When it comes to this paper though, nothing is being replaced. And again, it's useful work, particularly in the context of consumer applications (cf. aerospace which has already had to produce systems that do funky, adaptive, predictive, cooperative load/charge management across multiple battery chemistries).
I don't know what I'm saying. It's an interesting paper, but as someone working on low-power systems and exploring different battery chemistries for different things, the whole SDx angle somehow cheapens it... but perhaps I'm just weird.
I had expected this story to tell me that different battery chemistries react differently to different drain patterns, ie that low-drain usage might be better served by battery chemistry X and spiky high-drain usage by battery chemistry Y, and the OS could switch the power source between the batteries depending on the predicted near-future discharge pattern, based on what app was foregrounded or whatever.
But the idea that your battery would benefit by knowing your flight schedule seems to be reaching pretty far for a justification.
BMS in a laptop mostly just has to keep the battery safe and avoid unnecessary charge/discharge cycles. In a more complex system trying to salvage every joule of energy and maximize lifespan of each cell, the fact that batteries are less efficient to charge (or discharge) the closer they are to full (or empty) might be taken into account when deciding which to charge/discharge (for example).
A car might dump sudden regenerative braking power into high-C batteries if the main batteries can't take that charge at a given instant.
Satellites often use exotic batteries able to fully charge and discharge 10 times a day for decades at a time, but these have enormous self-discharge rates measured in days. Every chemistry has a unique set of compromises. There is plenty of room for smarter BMS in consumer systems that manage complex battery storage arrays responding to thermal, aging, cycle count, charge and load pressures more intelligently.
Laptops? Probably just a gimmick. It won't increase the stored energy.
[1] http://www1.eere.energy.gov/vehiclesandfuels/pdfs/merit_revi... [2] http://www.technologyreview.com/news/417053/a-battery-ultrac...
your device would learn how to charge, just as it learns your schedule, your voice, and other things about you
...no thanks.
With that said I don't think the concept itself is bad, its just that the execution will inevitably fail except under very regular circumstances such as carrying your laptop back and forth to the office each day. Volvo trucks has a similar thing to save fuel for their gearboxes that automatically chooses gear based on the performance you got the last time you drove that very route.
But it's not just anticipating demands by learning past behaviour. There's also the problem in any multi-battery system (which is what they cover), let alone with multiple types, optimizing what should take charge or load at a given time. Most people don't realize that many battery chemistries actually don't have a flat efficiency rating with charge: often, the closer to 100% charged you get, the less efficient (more loss) you have compared to what you'll be able to extract later.
For example, charging from 0 to 100% might be 85% efficient for a given battery type. But charging it from 80% to 100% full might only be 40% efficient.
And this variable efficiency also applies to discharge as well. And the variables all change with what recent battery demands have been, let alone the current loads - but also cell voltages, temperature, age and cycle count.
Even planning to cope with self-discharge over days, weeks or months might benefit from smarter BMS. Some battery management systems even take into consideration thermal management (taking into account the cost of ramping up active cooling or throttling charge rates to keep batteries at a temperature efficient for taking charge).
I'm wondering if other BMS (Battery Management Systems) used in other domains are not already doing what they propose in the paper.
The Nest thermostat I have at home promised to save me energy by learning my schedule, and heating the house only when needed - I had to turn this off because it never really worked. People do not inherently have a predictable schedule anyway.
The leaving for the airport in 15 minutes example in this article is similarly contrived.
Perhaps using multiple types of batteries will be useful in the future, but I'm not sold on the software learning part ATM.