A method for identifying an anomaly in at least one of a re-chargeable battery of an equipment and at least one component connected to the re-chargeable battery, the method comprising: receiving, by a processor, training parametric measurement data from at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, wherein the parametric measurement data comprises at least one of current measurement data, voltage measurement data, and temperature measurement data; creating a training model based on the received training parametric measurement data; determining, by the processor, a first plurality of characteristic data from the training model; receiving, by a processor, test parametric measurement data from the at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery; determining, by the processor, a second plurality of characteristic data based on the test parametric measurement data; comparing, by the processor, the second plurality of characteristic data with the first plurality of characteristic data determined from the training model; analyzing, by the processor, a value of a probability of an anomaly in the at least one of the re-chargeable battery of the equipment and at least one component connected to the re-chargeable battery based on the comparison; and if the probability is above a threshold, outputting, by the processor, a severity level of the anomaly in the at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, wherein creating the training model of learned data further comprises: analyzing, by the processor, a normal operation range of the at least one of a re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, based on the first plurality of the characteristic data; calculating, by the processor, a Probability Density Function, PDF, for the first plurality of characteristic data; segregating, by the processor, the first plurality of characteristic data, based on a statistical classification method; and creating, by the processor, a training model associated with the learned data corresponding to the segregated first plurality of characteristic data. receiving, by a processor, training parametric measurement data from at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, wherein the parametric measurement data comprises at least one of current measurement data, voltage measurement data, and temperature measurement data; creating a training model based on the received training parametric measurement data; determining, by the processor, a first plurality of characteristic data from the training model; receiving, by a processor, test parametric measurement data from the at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery; determining, by the processor, a second plurality of characteristic data based on the test parametric measurement data; comparing, by the processor, the second plurality of characteristic data with the first plurality of characteristic data determined from the training model; analyzing, by the processor, a value of a probability of an anomaly in the at least one of the re-chargeable battery of the equipment and at least one component connected to the re-chargeable battery based on the comparison; and if the probability is above a threshold, outputting, by the processor, a severity level of the anomaly in the at least one of the re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, wherein creating the training model of learned data further comprises: analyzing, by the processor, a normal operation range of the at least one of a re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, based on the first plurality of the characteristic data; calculating, by the processor, a Probability Density Function, PDF, for the first plurality of characteristic data; segregating, by the processor, the first plurality of characteristic data, based on a statistical classification method; and creating, by the processor, a training model associated with the learned data corresponding to the segregated first plurality of characteristic data. analyzing, by the processor, a normal operation range of the at least one of a re-chargeable battery of the equipment and the at least one component connected to the re-chargeable battery, based on the first plurality of the characteristic data; calculating, by the processor, a Probability Density Function, PDF, for the first plurality of characteristic data; segregating, by the processor, the first plurality of characteristic data, based on a statistical classification method; and creating, by the processor, a training model associated with the learned data corresponding to the segregated first plurality of characteristic data.