2 research outputs found
Predicting the Batteries' State of Health in Wireless Sensor Networks Applications
[EN] The lifetime of wireless sensor networks deployments
depends strongly on the nodes battery state of
health (SoH). It is important to detect promptly those motes
whose batteries are affected and degraded by ageing, environmental
conditions, failures, etc. There are several parameters
that can provide significant information of the battery
SoH, such as the number of charge/discharge cycles, the
internal resistance, voltage, drained current, temperature,
etc. The combination of these parameters can be used to
generate analytical models capable of predicting the battery
SoH. The generation of these models needs a previous
process to collect dense data traces with sampled values of
the battery parameters during a large number of discharge
cycles under different operating conditions. The collected
data allow the development of mathematical models that
can predict the battery SoH. These models are required to
be simple because they must be executed in motes with
low computational capabilities. The paper shows the complete
process of acquiring the training data, the models generation
and its experimental validation using rechargeable
batteries connected to Telosb motes. The obtained results
provide significant insight of the battery SoH at different
temperatures and charge/discharge cycles.This work was supported in part by the Spanish MINECO under Grant BIA2016-76957-C3-1-R and in part by the I+D+i Program of the Generalitat Valenciana, Spain, under Grant AICO/2016/046.Lajara Vizcaino, JR.; Perez Solano, JJ.; Pelegrà Sebastiá, J. (2018). Predicting the Batteries' State of Health in Wireless Sensor Networks Applications. IEEE Transactions on Industrial Electronics. 65(11):8936-8945. https://doi.org/10.1109/TIE.2018.2808925S89368945651