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I would prefer more manual control than trusting some kind of AI calendar understanding that a scheduled flight will require a charged battery, that also requir

by Too 11y ago

I would prefer more manual control than trusting some kind of AI calendar understanding that a scheduled flight will require a charged battery, that also requires me to actually schedule the flight in said calendar. Case in point is lenovos charging algorithm, they tried to be smart, probably with same intentions as Microsoft is here, by only charging the battery to 83% on normal days because that supposedly reduces the wear of the battery if you frequently move the laptop. Problem is I was actually on my way to a flight and there was no override, I had plenty of time but the battery simply wouldn't go up any more. After the trip I found the option, in some obscure menu only available after installing a special battery control program, you choose between battery life and battery performance, guess which one does what.. 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.

csirac2 11y ago
Statistically though, these approaches can really reduce cycle count - even if they sometimes get it wrong. 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).

by Too 11y ago

csirac2 11y ago

Statistically though, these approaches can really reduce cycle count - even if they sometimes get it wrong. 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).