Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile
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. T...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671125000099 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592771699965952 |
|---|---|
| author | Tasadeek Hassan Dar Satyavir Singh |
| author_facet | Tasadeek Hassan Dar Satyavir Singh |
| author_sort | Tasadeek Hassan Dar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1b20c23f9a724abaa0fea73d1e9d9191 |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-1b20c23f9a724abaa0fea73d1e9d91912025-01-21T04:13:24ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100902Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profileTasadeek Hassan Dar0Satyavir Singh1Corresponding authors.; Department of Electrical and Electronics Engineering, SRM University AP, Andhra Pradesh, 522240, IndiaCorresponding authors.; Department of Electrical and Electronics Engineering, SRM University AP, Andhra Pradesh, 522240, IndiaLithium-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.http://www.sciencedirect.com/science/article/pii/S2772671125000099Battery management systemHPPC testLithium-ion batteryParameter estimationImproved cuckoo search algorithm |
| spellingShingle | Tasadeek Hassan Dar Satyavir Singh Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile e-Prime: Advances in Electrical Engineering, Electronics and Energy Battery management system HPPC test Lithium-ion battery Parameter estimation Improved cuckoo search algorithm |
| title | Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| title_full | Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| title_fullStr | Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| title_full_unstemmed | Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| title_short | Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| title_sort | optimized parameter estimation of lithium ion batteries using an improved cuckoo search algorithm under variable temperature profile |
| topic | Battery management system HPPC test Lithium-ion battery Parameter estimation Improved cuckoo search algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2772671125000099 |
| work_keys_str_mv | AT tasadeekhassandar optimizedparameterestimationoflithiumionbatteriesusinganimprovedcuckoosearchalgorithmundervariabletemperatureprofile AT satyavirsingh optimizedparameterestimationoflithiumionbatteriesusinganimprovedcuckoosearchalgorithmundervariabletemperatureprofile |