Xie et al., 2025 - Google Patents
State of charge estimation of Li-ion batteries based on strong tracking adaptive square root unscented Kalman filter with generalized maximum correntropy criterionXie et al., 2025
- Document ID
- 17179442449857703998
- Author
- Xie H
- Lin J
- Huang Z
- Kuang R
- Hao Y
- Publication year
- Publication venue
- Journal of Energy Storage
External Links
Snippet
The state of charge (SOC) of Li-ion batteries is a critical parameter in battery management system, affecting both the efficient use and lifespan of the battery. Thus, accurate estimation of SOC is essential. The unscented Kalman filter (UKF) is widely employed in SOC …
- 229910001416 lithium ion 0 title abstract description 10
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
- G01R31/3644—Various constructional arrangements
- G01R31/3648—Various constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
- G01R31/3651—Software aspects, e.g. battery modeling, using look-up tables, neural networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
- G01R31/3606—Monitoring, i.e. measuring or determining some variables continuously or repeatedly over time, e.g. current, voltage, temperature, state-of-charge [SoC] or state-of-health [SoH]
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lai et al. | A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter | |
| He et al. | State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter | |
| Ren et al. | A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries | |
| Sassi et al. | Comparative study of ANN/KF for on-board SOC estimation for vehicular applications | |
| Zhang et al. | State of charge estimation for lithium-ion battery based on adaptive extended Kalman filter with improved residual covariance matrix estimator | |
| Kim | The novel state of charge estimation method for lithium battery using sliding mode observer | |
| Meng et al. | Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine | |
| Hu et al. | Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries | |
| Li et al. | A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles | |
| Wang et al. | An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles | |
| Xu et al. | Lithium-ion battery state of charge and parameters joint estimation using cubature Kalman filter and particle filter | |
| Shrivastava et al. | Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications | |
| Liu et al. | A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter | |
| Zhao et al. | State of charge estimation of lithium-ion battery based on multi-input extreme learning machine using online model parameter identification | |
| Shi et al. | The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods | |
| Xie et al. | State of charge estimation of Li-ion batteries based on strong tracking adaptive square root unscented Kalman filter with generalized maximum correntropy criterion | |
| Gao et al. | Data pieces-based parameter identification for lithium-ion battery | |
| CN117074962B (en) | Lithium ion battery state joint estimation method and system | |
| Liu et al. | A Combined State of Charge Estimation Method for Lithium‐Ion Batteries Using Cubature Kalman Filter and Least Square with Gradient Correction | |
| Liu et al. | A state of charge estimation method for lithium‐ion battery using PID compensator‐based adaptive extended Kalman filter | |
| Fereydooni et al. | Robust adaptive sliding mode observer for core temperature and state of charge monitoring of Li-ion battery: A simulation study | |
| Zhang et al. | A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods | |
| Liu et al. | An improved adaptive cubature H‑infinity filter for state of charge estimation of lithium‑ion battery | |
| Omiloli et al. | State of charge estimation based on a modified extended Kalman filter. | |
| Xiao et al. | Online parameter identification and state of charge estimation of lithium-ion batteries based on improved artificial fish swarms forgetting factor least squares and differential evolution extended Kalman filter |