Identification and Error Analysis of Lithium-Ion Battery Oriented to Cloud Data Application Scenario
Source abstract
Abstract
The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Electric and Hybrid Vehicle Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
Funding
Natural Science Foundation of Shanghai · National Natural Science Foundation of China (NSFC)
References (20)
Show source-provided references
1. Cycle life prediction of lithium-ion batteries based on data-driven methods10.1016/j.etran.2021.100137 ↗
2. Inline state of health estimation of lithium-ion batteries using state of charge calculation10.1016/j.jpowsour.2015.08.091 ↗
3. A remaining charging electric quantity based pack available capacity optimization method considering aging inconsistency10.1016/j.etran.2021.100149 ↗
4. The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles10.1109/TVT.2017.2751613 ↗
5. Guo, Y., Zhao, Z., and Huang, L. (2017, January 8–11). SoC Estimation of Lithium Battery Based on AEKF Algorithm. Proceedings of the 8th International Conference on Applied Energy (ICAE), Beijing Institute Technology, Beijing, China.
6. Wang, J., and Zhang, Z. (2020, January 20–22). Lithium-ion Battery SOC Estimation Based on Weighted Adaptive Recursive Extended Kalman Filter Joint Algorithm. Proceedings of the 8th IEEE International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China.10.1109/ICCSNT50940.2020.9304993 ↗
7. Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm10.1016/j.etran.2022.100156 ↗
8. A Method for Online Capacity Estimation of Lithium Ion Battery Cells Using the State of Charge and the Transferred Charge10.1109/TIA.2011.2180689 ↗
9. Zhou, X., and Pan, Z. (November, January 29). An online capacity estimation method for lithium ion battery cells using the significant points. Proceedings of the 43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON), Beijing, China.
10. Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles10.1016/j.jpowsour.2015.01.129 ↗
11. A study of cell-to-cell variation of capacity in parallel-connected lithium-ion battery cells10.1016/j.etran.2020.100091 ↗
12. A Novel Capacity Estimation Approach for Lithium-Ion Batteries Combining Three-Parameter Capacity Fade Model with Constant Current Charging Curves10.1109/TEC.2021.3058531 ↗
13. A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning10.1016/j.energy.2021.122185 ↗
14. A sequential capacity estimation for the lithium-ion batteries combining incremental capacity curve and discrete Arrhenius fading model10.1016/j.jpowsour.2020.229248 ↗
15. Implications of road transport electrification: A long-term scenario-dependent analysis in China10.1016/j.etran.2020.100072 ↗
16. Recursive approximate weighted total least squares estimation of battery cell total capacity10.1016/j.jpowsour.2010.09.048 ↗
17. Liu, G., Lu, L., Li, J., and Ouyang, M. (2013, January 15–18). Thermal Modeling of a LiFePO4/Graphite Battery and Research on the Influence of Battery Temperature Rise on EV Driving Range Estimation. Proceedings of the 9th IEEE Vehicle Power and Propulsion Conference (VPPC), Beijing, China.
18. Battery aging conscious intelligent energy management strategy and sensitivity analysis of the critical factors for plug-in hybrid electric buses10.1016/j.etran.2020.100061 ↗
19. Nonlinear health evaluation for lithium-ion battery within full-lifespan10.1016/j.jechem.2022.04.013 ↗
20. Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors10.1016/j.etran.2020.100078 ↗