A Method for Abnormal Battery Charging Capacity Diagnosis Based on Electric Vehicles Operation Data
Source abstract
Abstract
Overcharging due to an abnormal charging capacity is one of the most common causes of thermal runaway (TR). This study proposes a method for diagnosing abnormal battery charging capacity based on electric vehicle (EV) data. The proposed method can obtain the fault frequency and output the corresponding state of charge (SOC) when a fault occurs. First, a machine-learning-based data cleaning framework is developed to overcome the limitations of the interpolation method. Then, offline training is implemented, based on big vehicle operation data and an improved Gaussian process regression (GPR). Thereafter, online monitoring of the discrete capacity increment (DCI) is used to identify the abnormal charging capacity. The abnormal charging capacity fault is identified by the absolute error between the GPR outputs and the true DCI, and the thresholds are determined using a Box–Cox transformation with a value of 3σ. The diagnostic results indicate that the abnormal charging capacity of the TR vehicle is identified two months in advance, and the fault frequency of the abnormal and normal vehicles is 0.5221 and 0.0311, respectively. EV operation data and various methods are used to validate the robustness and applicability of the proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Advancements in Battery Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
References (38)
Show source-provided references
1. A review of the life cycle assessment of electric vehicles: Considering the influence of batteries10.1016/j.scitotenv.2021.152870 ↗
2. Safety challenges and safety measures of Li-ion batteries10.1002/ese3.895 ↗
3. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations10.1016/j.rser.2017.05.001 ↗
4. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles10.1016/j.apenergy.2020.115855 ↗
5. Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles10.1016/j.apenergy.2022.119229 ↗
6. Miao, Y., Hynan, P., Von Jouanne, A., and Yokochi, A. (2019). Current Li-ion battery technologies in electric vehicles and opportunities for advancements. Energies, 12.10.3390/en12061074 ↗
7. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends10.1016/j.rser.2020.110480 ↗
8. State of health estimation of lithium-ion batteries based on the constant voltage charging curve10.1016/j.energy.2018.11.008 ↗
9. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression10.1016/j.energy.2019.116467 ↗
10. A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression10.1016/j.microrel.2021.114405 ↗
11. She, C., Zhang, L., Wang, Z., Sun, F., Liu, P., and Song, C. (2021). Battery state of health estimation based on incremental capacity analysis method: Synthesizing from cell-level test to real-world application. IEEE J. Emerg. Sel. Top. Power Electron.10.1109/JESTPE.2021.3112754 ↗
12. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis10.1016/j.est.2020.101836 ↗
13. Jiang, L., Diao, X., Zhang, Y., Zhang, J., and Li, T. (2021). Review of the Charging Safety and Charging Safety Protection of Electric Vehicles. World Electr. Veh. J., 12.10.3390/wevj12040184 ↗
14. Kurzweil, P., Frenzel, B., and Scheuerpflug, W. (2022). A Novel Evaluation Criterion for the Rapid Estimation of the Overcharge and Deep Discharge of Lithium-Ion Batteries Using Differential Capacity. Batteries, 8.10.3390/batteries8080086 ↗
15. Study on Fault Diagnosis Method and Application of Automobile Power Supply Based on Fault Tree-Bayesian Network
16. Wang, L.-y., Wang, L.-f., Liu, W., and Zhang, Y.-w. (2017, January 5–8). Research on Fault Diagnosis System of Electric Vehicle Power Battery Based on OBD Technology. Proceedings of the 2017 International Conference on Circuits, Devices and Systems (ICCDS), Chengdu, China.10.1109/ICCDS.2017.8120458 ↗
17. Sensor fault diagnosis for lithium-ion battery packs based on thermal and electrical models10.1016/j.ijepes.2020.106087 ↗
18. Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm10.1016/j.est.2018.04.020 ↗
19. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods10.1016/j.apenergy.2017.05.139 ↗
20. Wang, C., Yu, C., Guo, W., Wang, Z., and Tan, J. (2022). Identification of Typical Sub-Health State of Traction Battery Based on a Data-Driven Approach. Batteries, 8.10.3390/batteries8070065 ↗
21. Thermal runaway prognosis of battery systems using the modified multiscale entropy in real-world electric vehicles10.1109/TTE.2021.3079114 ↗
22. A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings10.1016/j.jpowsour.2019.227275 ↗
23. Modified Relative Entropy-Based Lithium-Ion Battery Pack Online Short-Circuit Detection for Electric Vehicle10.1109/TTE.2021.3128048 ↗
24. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles10.1016/j.apenergy.2016.12.143 ↗
25. Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model10.1109/TPEL.2020.3008194 ↗
26. Klink, J., Hebenbrock, A., Grabow, J., Orazov, N., Nylén, U., Benger, R., and Beck, H.-P. (2022). Comparison of Model-Based and Sensor-Based Detection of Thermal Runaway in Li-Ion Battery Modules for Automotive Application. Batteries, 8.10.3390/batteries8040034 ↗
27. Fault identification and quantitative diagnosis method for series-connected lithium-ion battery packs based on capacity estimation10.1109/TIE.2021.3068553 ↗
28. A data-driven method for battery charging capacity abnormality diagnosis in electric vehicle applications10.1109/TTE.2021.3117841 ↗
29. Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China10.1016/j.energy.2018.12.061 ↗
30. A Novel Method for Lithium-Ion Battery Fault Diagnosis of Electric Vehicle Based on Real-Time Voltage
31. Nan, J., Deng, B., Cao, W., Hu, J., Chang, Y., Cai, Y., and Zhong, Z. (2022). Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation. Energies, 15.10.3390/en15155333 ↗
32. Zhang, Z., Liu, P., Zhang, X., and Lin, N. (2022, January 24–26). Research on Safety of New Energy Vehicles based on Big Data. Proceedings of the 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.10.1109/ICAICA54878.2022.9844426 ↗
33. Chen, T., and Guestrin, C. (2016, January 13–17). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939785 ↗
34. Data-driven fault diagnosis of lithium-ion battery overdischarge in electric vehicles10.1109/TPEL.2021.3121701 ↗
35. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions10.1016/j.jmp.2018.03.001 ↗
36. Compositional kernel learning using tree-based genetic programming for Gaussian process regression10.1007/s00158-020-02559-7 ↗
37. The impact of income inequality and economic complexity on ecological footprint: An analysis covering a long-time span10.1080/21606544.2021.1930188 ↗
38. Quinonero-Candela, J. (2004). Learning with Uncertainty: Gaussian Processes and Relevance Vector Machines, Technical University of Denmark.