Battery management strategies: An essential review for battery state of health monitoring techniques

Pradhan, Sunil Kumar; Chakraborty, Basab

doi:10.1016/j.est.2022.104427

Cited by 94 publications

(35 citation statements)

References 217 publications

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“…105 Additionally, differential analysis can be used to develop practical models that can be used in the battery management system (BMS) of large-format batteries for the purposes of more accurately estimating SOH, assessing individual cell aging, and identifying cell-to-cell variations in cell balancing that can induce aging disparities. 23,52,106,107 For example, Wang et al used differential voltage analysis to quantify the SOH of a seriesconnected battery pack by comparison to measured cycling behavior before pack assembly. 27…”

Section: Differential Analysis In Practice: Case Study and Recommenda...mentioning

confidence: 99%

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

2023

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Differentiation of a Li-ion battery cycling profile (galvanostatic voltage vs charge) yields a pair of complementary measures: differential capacity (dQ/dV vs voltage, also called incremental capacity) and differential voltage (dV/dQ vs charge). These metrics, especially when obtained under experimental conditions approximating cell equilibrium, are widely used to diagnose cell state and mechanisms related to cell aging. Central to the resulting diagnosis is the understanding of how the chemistry of individual electrode materials manifests in the full-cell differential plots. In this article, a fundamental theory of differential analysis is advanced from the starting point of idealized single electrode (half-cell) measurements, building up to the nontrivial translation to full cell data, with consideration of cell balancing and degradation. Various complementary approaches from the existing literature are harmonized with the aid of a novel graphical heuristic in which cell voltage and differential capacity data are superimposed on a 2D representation (“square plot”) of the cell thermodynamic state. Interpretative analysis based on the new approach is demonstrated using data from a commercial pouch cell with the well-characterized LiCoO2|graphite chemistry. A brief review of current literature on practical uses of differential analysis is also provided.

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Section: Differential Analysis In Practice: Case Study and Recommenda...mentioning

confidence: 99%

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

2023

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“…[5][6][7] To obtain the battery SOH estimation, the measurement of capacity and resistance is the most immediate way. [8] For instance, the capacity can be calculated by performing full charging-discharge cycles while Ohm's law or electrochemical impedance spectroscopy can obtain the resistance, [9] further, SOH can be calculated through the definitions. However, direct measuring techniques take a lot of time and require specialized equipment, which is not suitable for onboard vehicle scenarios.…”

Section: Introductionmentioning

confidence: 99%

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Qiang,

Tang,

et al. 2023

Energy Tech

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Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs.

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“…In addition, the need for a multi-time scale estimator with an appropriate decoupling mechanism to eliminate cross-talk between SOC and SOH is a great challenge for ohmic internal resistance testing. 15 Vyroubal et al 16 use EIS to obtain the parameters of the battery equivalent circuit. The proposed numerical simulations were compared with the actual measurements, and the results obtained by EIS proved to be more satisfactory than the conventional parameter identification methods.…”

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confidence: 99%

State of Health Prediction of Lithium-Ion Batteries Using Combined Machine Learning Model Based on Nonlinear Constraint Optimization

Liang,

Wang,

Fan

et al. 2024

J. Electrochem. Soc.

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Accurate State of Health (SOH) estimation of battery systems is critical to vehicle operation safety. However, it’s difficult to guarantee the performance of a single model due to the unstable quality of raw data obtained from lithium-ion battery aging and the complexity of operating conditions in actual vehicle operation. Therefore, this paper combines a long short-term memory (LSTM) network with strong temporality, and support vector regression (SVR) with nonlinear mapping and small sample learning. A novel LSTM-SVR combined model with strong input features, less computational burden and multiple advantage combinations is proposed for accurate and robust SOH estimation. The nonlinear constraint optimization is used to assign weights to individual models in terms of minimizing the sum of squared errors of the combined models, which can combine strengths while compensating for weaknesses. Furthermore, voltage, current and temperature change curves during the battery charging were analyzed, and indirect health features (IHFs) with a strong correlation with capacity decline were extracted as model inputs using correlation analysis and principal component analysis (PCA). The NASA dataset was used for validation, and the results show that the LSTM-SVR combined model has good SOH estimation performance, with MAE and RMSE all less than 0.75% and 0.97%.

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Battery management strategies: An essential review for battery state of health monitoring techniques

Cited by 94 publications

References 217 publications

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

State of Health Prediction of Lithium-Ion Batteries Using Combined Machine Learning Model Based on Nonlinear Constraint Optimization

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