Part of Digital twin for battery systems: Cloud BMS with online SoC and SoH estimation, all quotes are from this paper.

With the increase of battery cell number and algorithm complexity, onboard BMS is faced with problems in computation power and data storage for precise estimation and prediction of the battery’s states with model-based algorithms.

I think that sensing is the biggest obstacle to higher-quality monitoring of batteries with many small cylindrical cells: it's impossible (from cost, power draw, and system engineering points of view) to sense the current and temperature on each cell.

For estimating battery (or cell) parameters and subsequently SoH and SoC, it feels to me that it's enough to store relatively infrequent telemetry points (e. g., one point per minute, perhaps switching to one point per second during the periods of load transition, that will happen more often for a battery in a city-driving EV) and for a relatively short time (several full charge-discharge cycles or a week of calendar time, whatever is shorter) and then apply Stochastic estimator of cell parameters based on an electrical cell model.

The total volume of this data (assuming hundreds of cell telemetry timeseries) is just a few dozens of megabytes, so this volume can be stored easily on modern embedded computers.

Stochastic estimation algorithm might take some minutes on a low-power embedded computer (especially since it needs to share CPU time with the real-time logic of the BMS), but it should be fine because neither SoH nor even SoC estimation needs lower latency, really. However, "some minutes" is just my off-the-cuff guess, and the algorithm (which involves neural network training steps: see [Variational Auto-Regularized Alignment for Sim-to-Real Control](https://mellow-kileskus-a65.notion.site/Variational-Auto-Regularized-Alignment-for-Sim-to-Real-Control-2a5e545ed6674086970776cbe972997e)) might take such longer than minutes or be impossible on the embedded hardware at all.

Aging prognostics and strategy optimization which work based on historical operation data of the battery cells, are difficult to be implemented onboard.

I think it's possible to estimate the remaining battery life and the optimal 0/100 SoC levels onboard using long short-term memory models while keeping very little amount of historical data, such as the proportion of time a battery was used (and the amount of energy cycled) per day and the minimum and maximum levels of SoC achieved during a day. Similar to Online capacity estimation of lithium-ion batteries with deep long short-term memory networks.

All battery relevant data can be measured and transmitted seamlessly to the cloud platform, which is used to build up the digital twin for battery systems.

For estimating SoC, this will work only for batteries that are constantly or near-constantly connected the internet, such as energy storage at home or EV taxis which never exit the city.