Battery analytics is the use of AI models that estimate a battery's health from its own data, predict fires before they start and decide how fast each cell may charge. The data comes from the battery management system in every pack, and the models run in the cloud.
A battery loses capacity as it ages. The pack cannot measure this directly. But the models can estimate it from the voltage, current and temperature readings recorded over many charges. The estimate is called the state of health, and it tells a fleet owner which packs to replace. The same readings can also reveal danger. A cell whose internal resistance rises faster than its neighbours' may be developing a fault inside. Spotting such a change is anomaly detection.
Consider a courier company in Gurugram with 500 delivery scooters. Each pack sends its voltage, temperature and current every 10 seconds, so the cloud stores a time series for every cell group. In one pack, the resistance of one cell group rises by 15 per cent in two weeks, while the other groups barely change. The model flags this pack and lowers its charging current from 30 amperes to 15 amperes. It also alerts the fleet manager to inspect the pack.
The cloud has something that the board inside one pack does not have: the history of thousands of packs. It can compare an ageing pack with other packs of the same type and the same use, and learn which pattern in the first months leads to a failure a year later. The board inside the pack has only simple rules and little memory. So the analytics adds judgement over the longer period, while the board still protects the pack second by second.
The buyers are fleet owners, vehicle makers and battery-swapping companies. They pay for packs that last longer and for fewer fires. This is a form of predictive maintenance for batteries. The decision about how fast to charge is also useful at the fast-charging batteries and at the cloud of EV charging management software.