A Collaborative Estimation Scheme for Lithium-Ion Battery State of Charge and State of Health Based on Electrochemical Model
Source abstract
Abstract
Nowadays, many studies focus on single state of health (SOH) or state of charge (SOC) estimation. How to effectively combine the two for joint estimation is particularly important. This paper relies on our previous research foundation of SOH, and further increases the electrochemical model, and realizes the SOC estimation of the battery under different SOH stages by combining the two. An SPM model based on electrochemical mechanism is proposed and sensitivity analysis is performed for the parameters in the model. Three types of dynamic conditions, FUDS, BJDST and DST, are used to verify the feasibility of the model. Further, the synergistic estimation of SOH and SOC is realized by the change of the difference between the two lithium ion concentrations with the battery decay when SOC = 100% and SOC = 0% in the SPM model. In the offline state, a linear regression relationship is constructed between the results of the GPR model and the parameters of the SPM model. During online prediction, the parameters of SPM are obtained by calibrating the offline model, and the goal of estimating SOC based on SOH is realized. The experimental results show that the maximum error value of SOC estimation does not exceed 0.08, and the model has high accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
Show source-provided references
1. A data-driven fuzzy information granulation approach for battery state of health forecasting10.1016/j.jpowsour.2020.228716 ↗
2. A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves10.1016/j.est.2021.103072 ↗
3. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries10.1016/j.measurement.2021.109057 ↗
4. Data-driven prediction of battery cycle life before capacity degradation, Nature10.1038/s41560-019-0356-8 ↗
5. Closed-loop optimization of fast-charging protocols for batteries with machine learning10.1038/s41586-020-1994-5 ↗
6. Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications10.1016/j.jclepro.2021.128015 ↗
7. Model-based voltage construction method for robust state-of-health estimation of lithium-ion batteries10.1109/TIE.2020.3044779 ↗
8. State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble10.1016/j.ress.2018.02.022 ↗
9. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve10.1016/j.jpowsour.2018.03.015 ↗
10. State-of-Health Estimation of Lithium-ion Batteries by Fusing an Open-Circuit-Voltage Model and Incremental Capacity Analysis10.1109/TPEL.2021.3104723 ↗
11. Battery health prediction using fusion-based feature selection and machine learning10.1109/TTE.2020.3017090 ↗
12. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics10.1109/TSMC.2015.2389757 ↗
13. Intelligent prognostics for battery health monitoring based on sample entropy10.1016/j.eswa.2011.03.063 ↗
14. Using probability density function to evaluate the state of health of lithium-ion batteries10.1016/j.jpowsour.2013.01.018 ↗
15. Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis10.1016/j.jpowsour.2020.228806 ↗
16. Influence of cycling profile, depth of discharge and temperature on commercial LFP/C cell ageing: cell level analysis with ICA, DVA and OCV measurements10.1149/1945-7111/ab9cd1 ↗
17. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries10.1016/j.energy.2017.12.061 ↗
18. Noise-immune model identification and state-of-charge estimation for lithium-ion battery using bilinear parameterization10.1109/TIE.2019.2962429 ↗
19. Load current and state-of-charge coestimation for current sensor-free lithium-ion battery10.1109/TPEL.2021.3068725 ↗
20. Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network10.1109/JESTIE.2022.3148031 ↗
21. Transfer function establishment for Li-ion battery using improved P2D modeling methodology10.119/IPEMC.2016.7512491 ↗
22. Lithium-ion battery equivalent model over full-range state of charge based on electrochemical process simplification10.1016/j.electacta.2021.138698 ↗
23. Determination of the hydrogen diffusion coefficient in metal hydrides by impedance spectroscopy10.1016/S0378-7753(98)00092-5 ↗
24. Experiment-driven electro- chemical modeling and systematic parameterization for a lithium-ion battery cell10.1016/j.jpowsour.2010.02.029 ↗
25. State-of-charge estimation of lithium-ion battery based on a novel reduced order electrochemical model10.20964/2018.01.133 ↗
26. Lithium-ion battery state of charge estimation with a Kalman Filter based on an electrochemical model10.1109/CCA.2008.4629639 ↗
27. State of charge estimation for lithium-ion battery using an electrochemical model based on electrical double layer effect10.1016/j.electacta.2019.134966 ↗
28. Minimum lithium plating overpotential control based charging strategy for parallel battery module prevents side reactions10.1016/j.jpowsour.2021.229772 ↗
29. Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles10.1109/TTE.2015.2512237 ↗
30. Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus10.1109/TVT.2018.2865664 ↗
31. An information appraisal procedure: endows reliable online parameter identification to lithium-ion battery model10.1109/TIE.2021.3091920 ↗
32. Estimation of state of charge of lithium-ion battery based on single particle model [J]10.19344/j.cnki.issn1671-5276.2018.04.050 ↗
33. A review of SOC estimation methods for lithium-ion batteries based on electrochemical model, transactions of china electrotechnical10.19595/j.cnki.1000-6753.tces.211030 ↗
34. Review of simplified Pseudo-two-Dimensional models of lithium-ion batteries10.1016/j.jpowsour.2016.07.036 ↗
35. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures10.1016/j.apenergy.2013.07.008 ↗
36. Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries10.1016/j.apenergy.2016.09.010 ↗
37. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation10.1016/j.ijepes.2014.04.059 ↗
38. Design and parametrization analysis of a reduced-order electrochemical model of graphite/LiFePO4 cells for SOC/SOH estimation10.1016/j.jpowsour.2012.12.120 ↗
39. Single-particle model for a lithium-ion cell: thermal behavior10.1149/1.3521314 ↗
40. Empirical study of particle swarm optimization10.1109/CEC.1999.785511 ↗
41. A cubature Kalman filter for online state-of-charge estimation of lithium-ion battery using a gas-liquid dynamic model10.1016/j.est.2022.105141 ↗
42. Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model10.1016/j.energy.2021.122008 ↗
43. An effective method for estimating state of charge of lithium-ion batteries based on an electrochemical model and nernst equation10.1109/ACCESS.2020.3039783 ↗
44. An integration and selection scheme for capacity estimation of Li-ion battery based on different state-of-charge intervals10.1016/j.est.2022.105073 ↗