Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

Zhang, Xin; Hou, Jiawei; Wang, Zekun; Jiang, Yueqiu

doi:10.3390/en16010132

Cited by 26 publications

(15 citation statements)

References 19 publications

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“…The Gaussian process regression (GPR) is one of the data-driven algorithms, [28] which has been widely proposed for battery SOH estimation with the advantages of adaptive acquisition of hyperparameters and simplicity of use without performance loss compared to neural networks and SVM. [29,30] In this article, GPR is used to model HFs and capacity mapping.…”

Section: Soh Estimation Methodsmentioning

confidence: 99%

Joint State‐of‐Charge and State‐of‐Health Estimation for Lithium‐Ion Batteries Based on Improved Lebesgue Sampling and Division of Aging Stage

et al. 2023

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Accurate estimation of state of charge (SOC) and state of health (SOH) is an important guarantee for the safe and stable operation of Lithium‐ion batteries (LIBs). To reduce the computational cost of the battery management system (BMS), a joint SOC and SOH estimation method is proposed that achieves a balanced allocation of computational resources and prediction accuracy. It is based on a combined circuit model and data‐driven method, which uses improved Lebesgue sampling for the capacity update of SOC estimation. It updates the capacity reduction calculation when the capacity decays slowly, and can reduce the estimation error without increasing the number of sampling calculations. To improve the SOH estimation accuracy, two types of relevant health factors (HFs) are selected according to the aging mechanism of the battery. The battery aging stage is divided according to the HFs, and the SOH estimation method is proposed according to the battery aging stage. Experimental validation of the method was carried out by means of the aging dataset of LIBs from Oxford University batteries. The results show that the proposed joint estimation of SOH and SOC method has high accuracy and less computational cost in the whole lifecycle of LIBs.This article is protected by copyright. All rights reserved.

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Section: Soh Estimation Methodsmentioning

confidence: 99%

Joint State‐of‐Charge and State‐of‐Health Estimation for Lithium‐Ion Batteries Based on Improved Lebesgue Sampling and Division of Aging Stage

et al. 2023

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“…Prevent direct exposure to sunlight or placing the battery in hot areas such as crowded parking lots.Controlling temperature changes during charging and discharging processes is essential [6] . Avoid charging or discharging the battery at extreme temperatures to prevent damage to its performance and lifespan.In conclusion, the operating temperature has a significant impact on the health status of a power battery [7] . Proper temperature management and environmental control can extend battery lifespan, improve performance, and ensure safety [8] .…”

Section: Operating Temperaturementioning

confidence: 98%

Analysis of the relationship between operating parameters and health state of power batteries

Pi,

Liu

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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The health state of power batteries is influenced by various operating parameters, including charge-discharge rate, state of charge, and operating temperature. In this study, experiments were conducted to test the variations in the health state of power batteries under different charge-discharge rates, state of charge, and operating temperatures. The impact of these operating parameters on the health state of power batteries was analyzed. This research not only contributes to a more accurate assessment of the health state of power batteries but also provides labeled values for different operating parameters, which can be utilized in machine learning algorithms for determining the health state of power batteries. This, in turn, promotes the development of algorithms for assessing the health state of power batteries.

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“…In addition, estimation models have been developed using data-fitting methods. For instance, the neural network is used to analyze the relationship between the SOH and the battery voltage, current, temperature and other parameters directly measured from batteries and then estimate the capacity of the battery [17][18][19]. Sui et al extracted features from battery voltage using the fuzzy entropy method [20].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

Fan,

Lin,

Huang

2024

Energies

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Battery state-of-health (SOH) estimation is an effective approach to evaluate battery reliability and reduce maintenance costs for battery-based backup power supply systems. This paper proposes a novel SOH estimation method for batteries, which only uses the response characteristics of load surges and is, therefore, non-destructive to the estimated battery and its system. The discrete wavelet transform (DWT) method based on multi-resolution analysis (MRA) is used for wavelet energy features extraction, and the fuzzy cerebellar model neural network (FCMNN) is introduced to design the battery SOH estimator. The response voltage signals to load surges are used in the training and detection process of the FCMNN. Compared to conventional methods, the proposed method only exploits characteristics of online response signals to the inrush currents rather than injecting interference signals into the battery. The effectiveness of the proposed method is validated by detailed simulation analysis and experiments.

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Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

Cited by 26 publications

References 19 publications

Joint State‐of‐Charge and State‐of‐Health Estimation for Lithium‐Ion Batteries Based on Improved Lebesgue Sampling and Division of Aging Stage

Joint State‐of‐Charge and State‐of‐Health Estimation for Lithium‐Ion Batteries Based on Improved Lebesgue Sampling and Division of Aging Stage

Analysis of the relationship between operating parameters and health state of power batteries

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

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