Optimized Parameter Estimation of Lithium-Ion Batteries Using an Improved Cuckoo Search Algorithm Under Variable Temperature Profile

•A novel ICSA integrated with an equivalent circuit model is implemented.•identification of battery parameters under different sets of temperatures.•Optimized parameter identification by Improved cuckoo search algorithm.•The optimized approach of parameter estimation for BMS in Electric vehicles. Lithium-ion batteries are an intuitive choice for electric vehicles and many other gadgets. Parameters play a critical role in addressing its performance characterization. Accurate parameter estimation and real-time monitoring of lithium-ion batteries are important in modeling equivalent circuits. The characteristics of lithium-ion batteries are dynamic due to energy storage. Dynamical behavior is characterized by RC equivalent models. This work presents the estimation of parameters associated with the n-RC equivalent circuit model in integration with the Improved Cuckoo Search Algorithm (ICSA). To get it, battery tests such as HPPC test, static capacity test, and open circuit voltage test in consideration of temperatures are carried out. The experiments are carried out under different temperature ranges to record the valid data sets. ICSA is advantageous over existing algorithms in estimating the battery parameters under temperature ranges. The performance of the proposed approach captures and estimates the parameters in the dynamic range of temperatures of the lithium-ion battery. The error profile is addressed with the root mean square error and it is found to be 0.23% at 30 °C. It is observed that experimental data with ICSA accurately matches the simulated model data at different temperature ranges.