20.500.12556/DKUM-91502 A genetic algorithm based ESC model to handle the unknown initial conditions of state of charge for lithium ion battery cell Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. enhanced self-correcting model state of charge estimation lithium-ion cell parameter identification baterije življenjska doba naprave true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/batteries © 2024 by the authors Članek v reviji 2025-01-08 15:07:08 2025-01-08 15:07:09 2026-01-19 15:06:55 0000-00-00 00:00:00 2025 0 0 26 str. iss. 1 Vol. 11 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-10-07 2024-12-18 2024-12-24 681.5 221279747 10.3390/batteries11010001 2313-0105 batteries-11-00001.pdf batteries-11-00001.pdf 1 D26421DA114C58D742B43B54DDD16373 e1487e6d162dcfefd41199b6af2a7075596b0504ed14328af0d4c45eccd0188d 13c24853-cdca-11ef-9362-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187700 https://www.mdpi.com/2313-0105/11/1/1 0 d3afa4b0-f53f-11f0-94a4-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202185 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0