Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries

By Yu Merla, Billy Wu, Vladimir Yufit, Nigel P. Brandon, Ricardo Martinez-Botas, Gregory J. Offer. 2016

143 citations recorded by the source corpus.

Abstract Understanding and tracking battery degradation mechanisms and adapting its operation have become a necessity in order to enhance battery durability. A novel use of differential thermal voltammetry (DTV) is presented as an in-situ state-of-health (SOH) estimator for lithium-ion batteries. Accelerated ageing experiments were carried on 5Ah commercial lithium-ion polymer cells operated and stored at different temperature and loading conditions. The cells were analysed regularly with various existing in-situ diagnosis methods and the novel DTV technique to determine their SOH. The diagnosis results were used collectively to elaborate the degradation mechanisms inside the cells. The DTV spectra were decoupled into individual peaks, which each represent particular phases in the negative and positive electrode combined. The peak parameters were used to quantitatively analyse the battery SOH. A different cell of the same chemistry with unknown degradation history was then analysed to explore how the cell degraded. The DTV technique was able to diagnose the cell degradation without relying on supporting results from other methods nor previous cycling data.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c73302bfc5354f5ebc34acabaf33614818452d7b

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In Automotive Engineering

1. Kailong Liu
2. Simona Onori
3. Xiaosong Hu
4. Xianke Lin
5. Zhenpo Wang
6. Maitane Berecibar
7. Widanalage Dhammika Widanage
8. Yunhong Che

In Electrical and Electronic Engineering

1. Xiaosong Hu
2. Simona Onori
3. Kailong Liu
4. Xianke Lin
5. Zhenpo Wang
6. Yunhong Che
7. Maitane Berecibar
8. Yi Li

Citations in the corpus

• Model-based real-time thermal fault diagnosis of Lithium-ion batteries (2016, methodology): “The correlation between thermal imbalance and cell degradation has been studied in Merla et al. (2016).”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “The DT value is obtained by the temperature difference to a small capacity interval, and the DT curve shows the DT values versus capacities [61].”
• Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression (2021, methodology): “[32], [33] proposed a low-cost battery health diagnostic method by analyzing the changes of entropic heats.”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[55] carried out DTV analysis and found that the peak position and height, both correlated with the cell impedance rise, change significantly with battery ageing.”
• A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems (2017, methodology): “To quantify such inaccuracy and validate the results obtained alternative in-situ diagnosis techniques such as DTV [10,11] and ex-situ post- mortem analysis such as SEM, EDS or XRD [12] should be used.”