A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model

Zheng, Yuejiu; Qin, Chao; Lai, Xin; Han, Xiao; Xie, Yi

doi:10.1016/j.apenergy.2019.113327

Cited by 82 publications

(19 citation statements)

References 46 publications

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“…A linear aging model was constructed using the moving-window-based method considering the HI. Zheng et al [19] utilized the HI to determine the optimal charge region of the fixed voltage window and proposed an online capacity estimation method that incorporates both the discrete Arrhenius aging model and extended Kalman filter. Liu et al [20] proposed a relevance vector machine using the optimized HI to enhance the accuracy and stability of RUL prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

et al. 2020

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To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

et al. 2020

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“…19,20 Generally, the cathode and anode of LIBs should operate within their safe and reliable temperature and state-of-charge (SOC) windows. 1,[21][22][23] Thus, any fast charging strategy developed should also maintain the battery within reliable electrochemical and temperature boundaries.…”

Section: Introductionmentioning

confidence: 99%

Lithium‐plating‐free fast charging of large‐format lithium‐ion batteries with reference electrodes

Liu

Chu

Li³

et al. 2021

Int J Energy Res

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Fast charging without lithium deposition has been one of the most important goals for commercial lithium-ion batteries. This study developed a fast charging strategy for a commercial large-format NCM/graphite lithium-ion battery with a nominal capacity of 120 Ah. Reliable reference electrodes, whose performances were thoroughly investigated with high fidelity, were implanted into the cells to provide anode potential signals during the charging process. The charging current was strictly modulated to maintain both the anode potential and temperature within the safety regions. The cell's full charging capability was exploited and twice the charging speed was realized using the proposed strategy compared with the manufacturer's fast charging strategy. Following the cycle life tests, an incremental capacity analysis and scanning electron microscopy tests were conducted to identify the degradation mechanism under fast charging. The results indicated that after the fast charging, the battery's decay rate was similar to that of a battery subjected to slow charging after 100 cycles, while no plated lithium was detected on the negative electrode. An electric vehicle (EV) could be driven 300 km after charging for 16 minutes without lithium deposition. It can be concluded that the proposed lithium-plating-free fast charging strategy has high battery applicability and manufacturer friendliness, and provides a new perspective on EV fast charging. Highlights • A method for developing a fast charging strategy for large-capacity lithium batteries is proposed. • The cell's full charging capability was exploited and twice the charging speed was realized using the proposed strategy compared with the manufacturer's fast charging strategy. • The battery's decay rate was similar to that of a battery subjected to slow charging after 100 cycles, while no plated lithium was detected on the negative electrode.

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“…As an improved version of ICA/DVA, probability density function (PDF) is also adopted for SOH estimation, in a sense a discretization of ICA/DVA is realized by PDF method, and differential operation is avoided, instead, this is realized by probability counting of the sampled voltage, so PDF method is more convenient for on‐board use, but the second drawback of ICA/DVA method is still a problem for PDF method 18,19 . Besides, analysis of battery voltage profiles during integral or partial charging process is a very popular method to get SOH , its accuracy is good according to literatures, but the application of this method is usually restricted by certain charging mode 20‐22 …”

Section: Introductionmentioning

confidence: 99%

Battery state of health estimation method based on sparse auto‐encoder and backward propagation fading diversity among battery cells

Sun

Zhang

et al. 2021

Int J Energy Res

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Summary This paper studies LiFePO4 (LFP) battery capacity fading diversity among different cells with same type and specification under same working states during their whole life cycle; and with consideration of this phenomenon, a novel battery state of health (SOH) estimation method with adaptability to capacity fading diversity is proposed. In order to cope with this capacity fading diversity, a machine learning structure involving a sparse auto‐encoder (SAE) and a backward propagation neural network (BPNN) is designed for battery SOH estimation. In this strategy, battery terminal voltage during the later stage of charging process is used as input of SAE; through the reconstruction of input signal, compressive feature of battery voltage is abstracted by SAE; then this compressive feature is used as the input signal of BPNN, and through nonlinear mapping of the neural network, battery SOH can be finally obtained. In this way, a relationship between the battery voltage information at its later charging stage and its SOH can be established. Verification tests show that this SAE‐BPNN based SOH estimation strategy possesses a good accuracy with adaptability to the capacity fading diversity and voltage differences among different battery cells, the SOH estimation error can be restrained within the range of ±5%, and it is also very convenient to adopt this method in real online battery management system (BMS).

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A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model

Cited by 82 publications

References 46 publications

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Lithium‐plating‐free fast charging of large‐format lithium‐ion batteries with reference electrodes

Battery state of health estimation method based on sparse auto‐encoder and backward propagation fading diversity among battery cells

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