WE CLAIM:
1. An edge computing system implemented in a vehicle for early detection and alert
generation for thermal runaway in a battery of the vehicle, the edge computing system
comprises:
a multicore processor comprising:
a first set of cores configured to run Real Time Operating System
(RTOS) for obtaining real time battery data; and
a second set of cores configured to run a hypervisor, wherein the
hypervisor enables a plurality of Operating Systems (OS) to operate
synchronously and communicate with each other, wherein the plurality of OS
are configured to:
receive the real-time battery data from the RTOS;
predict potential thermal runaway in the battery in early manner using a
trained learning model based on the real-time battery data; and
generate an alert upon early detection of the potential thermal runaway.
2. The edge computing system as claimed in claim 1, wherein the trained learning model
is an Artificial Intelligence (AI) model, wherein the AI model is trained on a dataset
comprising historical battery operation data which comprises information of thermal
runaway, for early prediction of the potential thermal runaway in the battery.
3. The edge computing system as claimed in claim 1, wherein at least one of the plurality
of OS is configured to generate and transmit the alert to at least one of: a sub-system
within the vehicle capable of displaying and alerting, and a user device of an operator
of the vehicle.
4. The edge computing system as claimed in claim 1, wherein the alert is generated based
on severity levels determined by analyzing temperature trends, voltage fluctuations, and
critical battery parameters extracted from the real-time battery data.
5. A method for early detection and alert generation for thermal runaway in a battery of
the vehicle, the method comprising:
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obtaining real time battery data by a Real Time Operating System (RTOS)
running in the first set of cores;
receiving the real-time battery data from the RTOS by a plurality of Operation
Systems (OS) enabled by a hypervisor running on second set of cores, wherein the
hypervisor enables a plurality of OS to operate synchronously and communicate with
each other;
predicting potential thermal runaway in the battery in early manner using a
trained learning model based on the real-time battery data by the plurality of OS; and
generating an alert upon early detection of the potential thermal runaway by the
plurality of OS.