Capacity and state‐of‐charge (SOC) estimation for lithium‐ion cells based on charging time differences curves. (10th August 2022)
- Record Type:
- Journal Article
- Title:
- Capacity and state‐of‐charge (SOC) estimation for lithium‐ion cells based on charging time differences curves. (10th August 2022)
- Main Title:
- Capacity and state‐of‐charge (SOC) estimation for lithium‐ion cells based on charging time differences curves
- Authors:
- Zheng, Yuejiu
Pang, Kang
Xu, Chaojie
Su, Teng
Rohit, Bhagat
Guo, Yue
Han, Xuebing - Abstract:
- Summary: The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state‐of‐charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first‐order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data. Abstract : In this study, based on the principle of the similarity of charging curves, this paper proposes an online estimation method for the cell capacity and initial SOC in the battery pack. Through the transformation of charging curves, the corresponding relationship between the slope (and/or intercept) of the CTD curve and the capacity (and/or initial SOC) of the cells in the battery pack are established. The capacity and initial SOC of other series cells in the battery packSummary: The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state‐of‐charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first‐order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data. Abstract : In this study, based on the principle of the similarity of charging curves, this paper proposes an online estimation method for the cell capacity and initial SOC in the battery pack. Through the transformation of charging curves, the corresponding relationship between the slope (and/or intercept) of the CTD curve and the capacity (and/or initial SOC) of the cells in the battery pack are established. The capacity and initial SOC of other series cells in the battery pack are calculated. … (more)
- Is Part Of:
- International journal of energy research. Volume 46:Number 13(2022)
- Journal:
- International journal of energy research
- Issue:
- Volume 46:Number 13(2022)
- Issue Display:
- Volume 46, Issue 13 (2022)
- Year:
- 2022
- Volume:
- 46
- Issue:
- 13
- Issue Sort Value:
- 2022-0046-0013-0000
- Page Start:
- 18757
- Page End:
- 18767
- Publication Date:
- 2022-08-10
- Subjects:
- capacity estimation -- charging curves -- charging time difference curve -- Lithium‐ion batteries -- SOC estimation
Power resources -- Periodicals
Power (Mechanics) -- Periodicals
Power resources -- Research -- Periodicals
621.042 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/er.8495 ↗
- Languages:
- English
- ISSNs:
- 0363-907X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.236000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 24283.xml