Can onboard BMS estimate all cell parameters itself?

Energy infrastructure (including batteries) must be autonomous

Wiring vs. wireless communication between BMS components

Furthermore, the system reliability increases by replacing wiring communication with wireless IoT communication.

The authors don't develop this idea in the paper, so it's not quite clear what they are referring to: the communication between the onboard BMS and the IoT gateway, or the communication between the IoT gateway and the centralised hub, or the cloud itself, via an Ethernet cable to a switch. If they refer to the communication between the onboard BMS and the IoT gateway, I think there are some nuances to the "wired vs. wireless" question, so it's not possible to say that wireless communication is strictly better.

Compared with onboard BMSs, digital twin for battery systems has potential benefits [...] in early detection of system faults in different levels with big data analysis.

The conclusion from The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety is that it's hard to train models to predict cell failures from the operational data because there are too few cell failures and that data to train on. The most promising approach, as it seems to me, is to induce faults during the regular end-of-line Li-ion cell testing and verification process in the factory and to train a failure detecting model on those faults. Then, the model can be deployed on the onboard BMS.

Onboard BMS should detect anomalies and report failures itself

Visualisation in onboard BMS

Compared with the onboard BMS which has little data visualization opportunities, the browser-based UI in our cloud BMS can provide not only the real-time visualization of the measurement data and internal states of the battery cells but also historical operation data with numerous display types and options, which helps the operators in scheduling maintenance and repair.

The data can be stored locally on an IoT gateway (Raspberry Pi) in an embedded database like SQLite. The IoT gateway also runs an HTTP server which provides API for querying recent battery's telemetry data and the state. Operators can open the local diagnostics interface on their phone or tablets by connecting to the HTTP server in the local network. This approach has been proven to work at Northvolt: our Connected Battery IoT gateway does exactly what I've just described.

Estimating State of Charge with an adaptive extended H-infinity filter

Estimator of cell parameters based on particle swarm optimisation


I think that "digital batteries" systems in the cloud should focus on monitoring, fleet management, capacity and replacement planning, and, probably, end-of-life prognostics. Cloud should not control the batteries directly during the operation.

The onboard BMS should be completely autonomous. The onboard BMS should itself estimate state-of-charge estimation, estimate cell parameters, and detect failures and send alerts.