State of health estimation of lithium‐ion batteries: A multiscale <scp>G</scp>aussian process regression modeling approach

He, Yijun; Shen, Jinhua; Shen, Ji-Fu; Ma, Zi-Feng

doi:10.1002/aic.14760

Cited by 102 publications

(51 citation statements)

References 27 publications

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“…Hybrid models: Combining models Kalman filter [6,9] and data to achieve High cost for model development Particle filter (PF) [7,8] a better performance Data-driven approach Neural networks [11,12,18] Simple Large amount of data are required Gaussian process regression (GPR) [13] Relevance vector machine (RVM) [14] Practical Only for short-term prediction SVR and PF [15] High accuracy Hybrid approach GPR and PF [16] Combines advantages Complex RVM and PF [17] of different approaches…”

Section: Algorithms/methods Advantages Disadvantagesmentioning

confidence: 99%

“…These model-based approaches can achieve high estimation accuracy, but they also require heavy work in the model development. For the data-driven approach [11][12][13][14], it is not necessary to understand the degradation principle of the battery; it only uses degradation data to build the degradation model. For example, Rezvani et al [12] used an adaptive neural network (ANN) and linear prediction error method for the SOH quantification of a lithium-ion battery.…”

Section: Introductionmentioning

confidence: 99%

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Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system's proper operation. A direct way to estimate the SOH is through the measurement of the battery's capacity; however, this measurement during the battery's operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery's operation. These indicators are extracted from the battery's voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery's capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

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Section: Algorithms/methods Advantages Disadvantagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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“…5, 6 and 7. The prediction errors of published methods are obtained from reference [15,25,28]. It is clear that the proposed RTPF has much better prediction performance than the eight published methods.…”

Section: Prediction and Comparisonmentioning

confidence: 99%

“…GP model is a flexible nonparametric model, which has been widely applied to multi-step-ahead predictions in time series analysis [25,34]. A GP model is completely specified by its mean function and covariance function.…”

Section: Gaussian Process Modelmentioning

confidence: 99%

“…Liu et al [15] has predicted SOH regeneration and degradation by using combination Gaussian progress function regression. He et al [25] has decoupled local regeneration and global degradation with wavelet and predicted them respectively. However, the capacity regeneration phenomenon is strongly related to the rest time which is determined by practical application demands of the battery [24].…”

Section: Introductionmentioning

confidence: 99%

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A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

et al. 2016

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Abstract:State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

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Ion‐“distillation” for isolating lithium from lake brine

Jiang

Chen

et al. 2022

AIChE Journal

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Lithium demands increase dramatically and make it highly attractive to develop advanced ion separation technology/material. However, high Mg2+/Li+ ratio impedes the extraction due to the difficulties in separation of the two ions. Here, we propose an ion‐“distillation” technology based on electro‐membrane stacking for the extraction of Li+ from lake brine (Mg2+/Li+ ratio: 31.58). This technology employs commercially available monovalent ion‐selective membranes, and ions are driven by electric. Using the four‐stage ion‐“distillation” technology, selectivity values of 26,177 and 27,000 are achieved between Li+ and Mg2+ and between Cl− and SO42−, respectively. The electro‐stripping mechanism when monovalent ion migrating across the membranes probably magnitude the Li+ selectivity, which is higher than the other reported values in the literature for membrane processes, and the purity of the final LiCl product is greater than battery grade (99.95%). The proposed process can potentially be applied in efficient ion fractionation and special separations.

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State of health estimation of lithium‐ion batteries: A multiscale Gaussian process regression modeling approach

Cited by 102 publications

References 27 publications

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

Ion‐“distillation” for isolating lithium from lake brine

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