There are well-known reasons why information about batteries’ degradation should be present in the cloud. Knowing the state of batteries’ degradation enables businesses to predict batteries’ remaining lifetimes and to procure new batteries more accurately. For large battery fleets, this is clearly the task for a cloud system to keep track of each battery’s state-of-health so that operators can schedule replacements when these batteries reach their predetermined end-of-life level of degradation. The battery’s state-of-health also informs the price of insurance against a battery failure.

It’s perhaps less widely appreciated how knowing the battery’s degradation parameters in the onboard BMS enables better failure prediction and more efficient control of the battery.

These are some of the possible signs of the onset of a cell failure that a reliable onboard BMS should detect (and, perhaps, stop the battery operation and alert the owner of the battery or the EV):

I have argued before that a robust algorithm should estimate most cell parameters at once because all parameters conflate within the sole output signal: the voltage. This means that to detect the onset of some battery failures, the onboard BMS is effectively required to estimate the battery's degradation parameters.

Knowing the degradation parameters in the onboard BMS could also help to avoid accelerated degradation (in a positive feedback loop), thus prolonging the battery’s life. BMS should use the remaining capacity estimate to narrow down the battery's charge and discharge voltage limits.



[1] Degradation diagnostics for lithium ion cells