SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

Zeng, Miaomiao; Zhang, Peng; Yang, Yang; Xie, Changjun; Shi, Ying

doi:10.3390/en12163122

Cited by 47 publications

(12 citation statements)

References 30 publications

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“…The accuracy of the estimation depends on the decay law of the model's key parameters representing the internal aging degree [42][43][44]. This method is relatively mature, mainly including several forms, such as the electrochemical impedance spectroscopy (EIS) model [45][46][47], the thermoelectric coupling model [48][49][50], the Thevenin model [51][52][53], and the shunt of the multi-stage resistor-circuit (RC) model [54][55][56]. According to the difference between the theory of model construction and the principle of algorithm in state prediction, it can be divided into two categories: electrochemical models and equivalent circuit models.…”

Section: Model-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

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Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

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Section: Model-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

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“…The accuracy of these methods, however, is strongly dependent on model assumptions, precise knowledge of battery parameters, and tuning of filter parameters such as measurement and process noise covariances. Model-based filtering approaches for simultaneous estimation of SoC and SoH in Li-ion batteries, such as the Dual Extended Kalman Filter [13] or Fuzzy Unscented Kalman Filter [14], have shown improved estimation performance when compared to methods that only estimate SoC assuming known and constant battery parameters. These methods are especially relevant for batteries approaching their end-of-life, when degraded cells experience accelerated capacity and power fade.…”

Section: Ensemble Learning Prediction and Li-ion Cellmentioning

confidence: 99%

Ensemble Learning, Prediction and Li-Ion Cell Charging Cycle Divergence

Obert

Trevizan

Torres-Castro

et al. 2021

IEEE Open J. Power Energy

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In recent years, the pervasive use of lithium ion (Liion) batteries in applications such as cell phones, laptop computers, electric vehicles, and grid energy storage systems has prompted the development of specialized battery management systems (BMS). The primary goal of a BMS is to maintain a reliable and safe battery power source while maximizing the calendar life and performance of the cells. To maintain safe operation, a BMS should be programmed to minimize degradation and prevent damage to a Li-ion cell, which can lead to thermal runaway. Cell damage can occur over time if a BMS is not properly configured to avoid overcharging and discharging. To prevent cell damage, efficient and accurate cell charging cycle characteristics algorithms must be employed. In this paper, computationally efficient and accurate ensemble learning algorithms capable of detecting Li-ion cell charging irregularities are described. Additionally, it is shown using machine and deep learning that it is possible to accurately and efficiently detect when a cell has experienced thermal and electrical stress due to cell overcharging by measuring charging cycle divergence.

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“…The capacity of the battery pack can be fully utilized. A fuzzy unscented Kalman filtering algorithm proposed by our research group was used to estimate the SOCs of retired batteries [32].…”

Section: Equalization Algorithmmentioning

confidence: 99%

A Layered Bidirectional Active Equalization Method for Retired Power Lithium-Ion Batteries for Energy Storage Applications

et al. 2020

Self Cite

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The power from lithium-ion batteries can be retired from electric vehicles (EVs) and can be used for energy storage applications when the residual capacity is up to 70% of their initial capacity. The retired batteries have characteristics of serious inconsistency. In order to solve this problem, a layered bidirectional active equalization topology is proposed in this paper. Specifically, a bridge-type equalization topology based on an inductor is adopted in the bottom layer, and the distributed equalization topological structure based on the bidirectional BUCK-BOOST circuit is adopted in the top layer. State of charge (SOC) is used as the equalization target variable, and the bottom layer equalization algorithm based on a “partition” idea and route optimization is proposed. The static equalization experiments and charge equalization experiments are performed by the 12 retired batteries selected from an electric sanitation vehicle. The results show that the proposed equalization method can reduce the SOC difference between retired batteries and can effectively improve the inconsistency of the retired battery pack with a faster equalization speed.

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SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

Cited by 47 publications

References 30 publications

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Ensemble Learning, Prediction and Li-Ion Cell Charging Cycle Divergence

A Layered Bidirectional Active Equalization Method for Retired Power Lithium-Ion Batteries for Energy Storage Applications

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