Design of an efficient battery model using evolutionary algorithms.

Abstract

Batteries play a vital role in current scenario of energy storage, even though many techniques of energy storage are available, since the time taken to start delivering the stored energy is very less. The battery life time depends upon its charging and discharging characteristics, which are in turn, depend on the internal parameters of battery. These parameters include resistance, capacitance and open circuit voltage. The amount of energy stored in the battery can be calculated by estimating these parameters. In this paper, an optimized model for Lithium ion batteries is presented using evolutionary algorithms to estimate the internal parameters of the battery over different charging and discharging rates. A sample EIG make, 2.5 V, 8 Ahr Lithium ion battery is modeled using two evolutionary algorithms such as genetic algorithm and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for different charging and discharging rates. The results of two algorithms are compared with the catalog values given by the manufacturer in order to identify the appropriate algorithm for battery modeling and validation. This paper concludes that battery characteristics obtained by CMA-ES algorithm match with the measured manufacturer characteristics.

A Review on Battery Modelling Techniques

“…The parameters of the non-linear Vc j c /Vd j c relationship are also expressed in terms of polynomial equations, where i and j signify the ith and jth determined charging and discharging voltage values, respectively. As a result, Equations ( 10) and (11) yield the battery terminal voltage for charging and discharging scenarios with regard to time when the current is constant (6) [9,97,98].…”

“…The user-defined C r limit (C lmt ) and initial battery SoC (SOC ini ) have also been taken into consideration. The C crt r and D crt r of the battery can be expressed as given below [9,97,98].…”

“…A similar type of control algorithm is used for discharging scenarios. The SOC cr and DOD cr are calculated from Equations ( 14) and ( 15) [9,97,98].…”

“…The fitness function for optimisation is calculated as the difference between measured (V M ci or V M di ) and the calculated terminal voltage (V C ci or V C di ) for the charging or discharging case and is determined as given in Equation ( 16) [9,97,98].…”