Abstract
Safe and reliable management of rechargeable lithium-ion batteries relies on accurate state-of-charge (SOC) estimation. SOC estimation algorithms developed based on the conventional state-space model can be inaccurate when the slope of the battery SOC to open-circuit voltage (VOC) profile is close to zero, where the observability matrix of the conventional battery state-space model is ill-conditioned. This ill-conditioned observability issue is usually overlooked and results in unreliable SOC estimations. This article proposes a robust state-space model using a set of carefully selected states, making the proposed model well-conditioned over the full SOC range and insesitive to the slope of the SOC-VOC profile. The proposed model, therefore, solves the observability issue and improves the SOC estimation accuracy. To examine the performance of the proposed state-space model over the full SOC range, the battery is discharged using a standard electric vehicle driving profile. Experiments are further carried out in various environmental temperature conditions to validate the robustness of the proposed model. The results show that the proposed state-space model is robust against the change of the slope of the SOC-VOC profile and significantly improves the SOC estimation accuracy, rendering more than 30% improvement in various temperature conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 305-313 |
| Number of pages | 9 |
| Journal | IEEE Journal of Emerging and Selected Topics in Industrial Electronics |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Jul 2021 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Battery modeling
- condition number
- electric circuit model (ECM)
- open circuit voltage
- sensitivity analysis
- state-of-charge (SOC) estimation
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