Data from a battery management system can be stored in the cloud. Once there it is possible to run calculations on that pack data and data from other packs to learn more around how it is operating and ageing. This then allows the battery management system to be updated in terms of calibration, limits and even imposing restrictions around it’s safe use.
In the Cloud: Including initial setup expenses and operational costs over time, significantly impacting the choice between local and cloud BMS. Initial setup costs refer to the initial expenses incurred when establishing a system, encompassing elements like hardware procurement, software licenses, installation, and configuration.
- Local BMS: Lower initial setup cost but may incur higher operational costs over time due to manual maintenance and upgrades.
- Cloud BMS: Higher initial setup cost but potentially lower operational costs due to automated maintenance and the ability to leverage shared resources in the cloud.
Unlocking Unlabeled Battery Field Data
Despite significant advancements achieved by data-driven methods, diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.
In this paper experimental verification is performed on the battery degradation dataset from 20 commercial EVs, collected over more than two years. The proposed framework enables accurate and robust aging diagnosis, requiring only the labeled data from two EVs.
What an AI – Powered BMS Actually Delivers
Engineering Advances and Technical Insights
“AI-powered BMS” is a term we hear more and more frequently. In the broader AI world, we see the landscape evolving through distinct phases – most recently captured by Andrej Karpathy’s “Software 3.0” talk as Software 1.0 (hand-coded logic), Software 2.0 (deep learning), and the emerging Software 3.0, where we interact with foundation models through prompts rather than fixed network weights.