A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Abstract

There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

“…A related example focusing on the end-of-life screening of batteries was proposed by [30] based on measurements of battery parameters by hybrid pulse power characterisation (HPPC), which was shown to be effective at estimating the state of health with low error with the use of a small dataset. In our previous work [31], a similar concept was explored using a small dataset based on EIS measurements with ECM-based parameter extraction; we demonstrated that this method presented large improvements in measurement times over conventional SoH measurement methods while retaining low estimation error. However, the previous work does not address the limitation of the lack of temperature dependence and relies on a well-chosen ECM for best results.…”

“…A number of folds, typically K − 1, may then be reserved for training the network, while the remainder of the folds may be held-out as validation and testing data to evaluate the model. For this study, a value of K = 6 was chosen as a balance between validation accuracy and training performance as with our previous work [31]. For network evaluation and comparison, the metrics of RMS and mean absolute error, defined as…”

“…For this work, a library of impedance spectroscopy measurements paired with their corresponding capacities measured by discharge testing is considered, as used in our previous study in [31]. The dataset, openly accessible in [52], contains measurements collected from a series of 13 battery modules from an end-of-life Nissan Leaf 2011 battery pack to obtain a realistic representation of the end-of-life battery condition.…”

Development of a Test Method to Evaluate Lithium-Ion Batteries for Second Life in Renewable Energy Applications

“…SoH can be estimated by simple statistic methods, such as direct capacity test, but this type of methods will cost much time to obtain SoH as experimental result. As previously mentioned, ANNs considered an effective solution for handling with the behaviour of non-linear systems, cross-interaction between system variables and existing patterns in the data used to train the network [28] [29].…”

Towards robotizing the processes of testing lithium-ion batteries

“…Among these methods, capacity-based methods, [9][10][11] impedance spectroscopy 7,[12][13][14][15] and Kalman filtering methods are commonly used to estimate the battery model parameters that can be considered as battery health indicators. [16][17][18][19][20] Capacity-based SoH methods can result in extremely accurate and reasonably straight forward measurements, 21 but this test is a very time-consuming process that requires the cell to be fully charged and discharged to carry out the current integration. 22,23 On the other hand, the internal resistance can be determined through both pulse power tests and the use of electrochemical impedance spectroscopy (EIS).…”