Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910479700459520 |
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| author | Kulkarni, Abhijit Teodorescu, Remus |
| author_facet | Kulkarni, Abhijit Teodorescu, Remus |
| contents | A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation Kulkarni, Abhijit Teodorescu, Remus Systems and Control A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data. |
| title | Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.06151 |