Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison
Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/2/623 |
| _version_ | 1797494320535699456 |
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| author | Sumukh Surya Akash Samanta Vinicius Marcis Sheldon Williamson |
| author_facet | Sumukh Surya Akash Samanta Vinicius Marcis Sheldon Williamson |
| author_sort | Sumukh Surya |
| collection | DOAJ |
| description | Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes. |
| first_indexed | 2024-03-10T01:32:35Z |
| format | Article |
| id | doaj.art-62fad47cfdc8467b9331318c7b14bec9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:32:35Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-62fad47cfdc8467b9331318c7b14bec92023-11-23T13:39:24ZengMDPI AGEnergies1996-10732022-01-0115262310.3390/en15020623Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model ComparisonSumukh Surya0Akash Samanta1Vinicius Marcis2Sheldon Williamson3Robert Bosch Engineering and Business Solutions, Bangalore 560100, IndiaDepartment of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaEstimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.https://www.mdpi.com/1996-1073/15/2/623electric vehiclesstationary battery energy storage systembattery automated systemonline state estimationthermal modelingfirst-order model |
| spellingShingle | Sumukh Surya Akash Samanta Vinicius Marcis Sheldon Williamson Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison Energies electric vehicles stationary battery energy storage system battery automated system online state estimation thermal modeling first-order model |
| title | Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison |
| title_full | Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison |
| title_fullStr | Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison |
| title_full_unstemmed | Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison |
| title_short | Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison |
| title_sort | smart core and surface temperature estimation techniques for health conscious lithium ion battery management systems a model to model comparison |
| topic | electric vehicles stationary battery energy storage system battery automated system online state estimation thermal modeling first-order model |
| url | https://www.mdpi.com/1996-1073/15/2/623 |
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