A New Time Constant Approach to Online Capacity Monitoring and Lifetime Prediction of Lithium Ion Batteries for Electric Vehicles (EV)

Attidekou, Pierrot S.; Wang, Chen; Armstrong, Matthew; Lambert, Simon; Christensen, Paul

doi:10.1149/2.0101709jes

Cited by 19 publications

(16 citation statements)

References 45 publications

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“…Lithium-ion (Li-ion) batteries have been widely used in electric vehicles, hybrid electric vehicles, and other power storage fields owing to their advantages such as high energy density, high-voltage platform, no memory effect, low self-discharge, and long life. [1][2][3] The ternary Li-ion battery stands out amidst other types of Li-ion batteries owing to its higher energy density and longer cycle life; thus, it has been widely used in electric vehicles. 4 However, in recent years, accidents such as vehicle fires and explosions have frequently occurred because of the thermal runaway (TR) of Li-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Effect of high Ni on battery thermal safety

Wang

Zheng

et al. 2020

Int J Energy Res

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Frequent accidents involving Li-ion batteries have prompted higher safety requirements for these batteries. In this study, the high-temperature, thermal runaway (TR) characteristic parameters at 100% state of charge (SOC) for cylindrical NCM811 batteries with a high-energy density were compared to the widely commercialized NCM523 batteries. The average TR trigger temperature of NCM811 battery was 157.54 C, which was 20.62 C lower than that of NCM523. Moreover, the average TR maximum temperature of NCM811 battery is 858.22 C, which was 212.81 C higher than that of NCM523. The maximum TR temperature of the NCM811 battery was 1289.53 C. The high Ni batteries exhibited poor thermal stability and severe TR. An increase in the Ni

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Section: Introductionmentioning

confidence: 99%

Effect of high Ni on battery thermal safety

Wang

Zheng

et al. 2020

Int J Energy Res

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“…35 The advantages are that this method is non-evasive, the system needs to be in thermodynamic equilibrium, the perturbation is approximately linear with a small perturbation and the measurement can cover a large frequency range with different time constant. 36 Moreover, the analytical models are readily available. The disadvantages are that the equipment is expensive and the low-frequency range is difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

Towards robotizing the processes of testing lithium-ion batteries

Rastegarpanah

Ahmeid

Marturi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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To boost the circular economy of the electric vehicle battery industry, an accurate assessment of the state of health of retired batteries is essential to assign them an appropriate value in the post automotive market and material degradation before recycling. In practice, the advanced battery testing techniques are usually limited to laboratory benches at the battery cell level and hardly used in the industrial environment at the battery module or pack level. This necessitates developing battery recycling facilities that can handle the assessment and testing undertakings for many batteries with different form factors. Towards this goal, for the first time, this article proposes proof of concept to automate the process of collecting the impedance data from a retired 24kWh Nissan LEAF battery module. The procedure entails the development of robot end-of-arm tooling that was connected to a Potentiostat. In this study, the robot was guided towards a fixed battery module using visual servoing technique, and then impedance control system was applied to create compliance between the end-of-arm tooling and the battery terminals. Moreover, an alarm system was designed and mounted on the robot’s wrist to check the connectivity between a Potentiostat and the battery terminals. Subsequently, the electrochemical impedance spectroscopy test was run over a wide range of frequencies at a 5% state of charge. The electrochemical impedance spectroscopy data obtained from the automated test is validated by means of the three criteria (linearity, causality and stability) and compared with manually collected measurements under the same conditions. Results suggested the proposed automated configuration can accurately accomplish the electrochemical impedance spectroscopy test at the battery module level with no human intervention, which ensures safety and allows this advanced testing technique to be adopted in grading retired battery modules.

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“…Qian et al 16 used a second-order ECM to describe the voltage relaxation, and found that the extracted parameters provided an evaluation of the battery SoH and aging mechanisms. Attidekou et al 17 modelled the battery capacity decay during rest periods at 100% SoC using a dynamic time constant derived from the RC network model. Fang et al 18 proposed a battery SoH estimation method based on the linear relationship between feature parameters and open circuit time during the battery relaxation even under different SoCs.…”

Section: Introductionmentioning

confidence: 99%

Data-driven lithium-ion battery capacity estimation from voltage relaxation

Zhu

Huang

Knapp³

et al. 2021

Preprint

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Accurate capacity estimation is critical for reliable and safe operation of lithium-ion batteries. A proposed approach exploiting features from the relaxation voltage curve enables battery capacity estimation without requiring previous cycling information. Machine learning methods are used in the approach. A dataset including 27,330 data units are collected from batteries with LiNi0.86Co0.11Al0.03O2 cathode (NCA battery) cycled at different temperatures and currents until reaching about 71% of their nominal capacity. One data unit comprises three statistical features (variance, skewness, and maxima) derived from the relaxation voltage curve after fully charging and the following discharge capacity for verification. Models adopting machine learning methods, i.e., ElasticNet, XGBoost, Support Vector Regression (SVR), and Deep Neural Network (DNN), are compared to estimate the battery capacity. Both XGBoost and SVR methods show good predictive ability with 1.1 % root-mean-square error (RMSE). The DNN method presents a 1.5% RMSE higher than that obtained using ElasticNet and SVR. 30,312 data units are extracted from batteries with LiNi0.83Co0.11Mn0.07O2 cathode (NCM battery). The model trained by the NCA battery dataset is verified on the NCM battery dataset without changing model weights. The test RMSE is 3.1% for the XGBoost method and 1.8% RMSE for the DNN method, indicating the generalizability of the capacity estimation approach utilizing battery voltage relaxation.

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A New Time Constant Approach to Online Capacity Monitoring and Lifetime Prediction of Lithium Ion Batteries for Electric Vehicles (EV)

Cited by 19 publications

References 45 publications

Effect of high Ni on battery thermal safety

Effect of high Ni on battery thermal safety

Towards robotizing the processes of testing lithium-ion batteries

Data-driven lithium-ion battery capacity estimation from voltage relaxation

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