State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves

Data-Driven Cycle Life Prediction of Lithium Metal-Based Rechargeable Battery Based on Discharge/Charge Capacity and Relaxation Features

Achieving precise estimates of battery cycle life is a formidable challenge due to the nonlinear nature of battery degradation. This study explores an approach using machine learning (ML) methods to predict the cycle life of lithium-metal-based rechargeable batteries with high mass loading LiNi0.8Mn0.1Co0.1O2 electrode, which exhibits more complicated and electrochemical profile during battery operating conditions than typically studied LiFePO₄/graphite based rechargeable batteries. Extracting diverse features from discharge, charge, and relaxation processes, the intricacies of cell behavior without relying on specific degradation mechanisms are navigated. The best-performing ML model, after feature selection, achieves an R2 of 0.89, showcasing the application of ML in accurately forecasting cycle life. Feature importance analysis unveils the logarithm of the minimum value of discharge capacity difference between 100 and 10 cycle (Log(|min(ΔDQ 100-10(V))|)) as the most important feature. Despite the inherent challenges, this model demonstrates a remarkable 6.6% test error on unseen data, underscoring its robustness and potential for transformative advancements in battery management systems. This study contributes to the successful application of ML in the realm of cycle life prediction for lithium-metal-based rechargeable batteries with practically high energy density design.

Enhanced electrochemical discharge of Li-ion batteries for safe recycling

The recycling of spent lithium-ion batteries (LIBs) is crucial to sustainably manage resources and protect the environment as the use of portable electronics and electric vehicles (EVs) increases. However, the safe recycling of spent LIBs is challenging, as they often contain residual energy. Left untreated, this can trigger a thermal runaway and result in disasters during the recycling process. For efficient recycling, it is important to withdraw any leftover energy from LIBs, regardless of the processing method that follows the discharge. The electrochemical discharge method is a quick and inexpensive method to eliminate this hazard. This method works by immersing batteries in an aqueous inorganic salt solution to discharge LIBs completely and efficiently. Previously, research focus has been on different inorganic salt solutions that release toxic or flammable gaseous products during discharge. In contrast, we present an entirely new approach for electrochemical discharge - the utilization of an Fe(ii)-Fe(iii) redox couple electrolyte. We show that this medium can be used for efficient LIB deep discharge to a voltage of 2.0 V after rebound, a level that is low enough for safe discharge. To accomplish this, periodic discharge methods were used. In addition, no corrosion on the battery casing was observed. The pH behavior at the poles was also investigated, and it was found that without convection, gas evolution during discharge cannot be avoided. Finally, it was discovered that the battery casing material plays a vital role in electrochemical discharge, and its industrial standardization would facilitate efficient recycling.

Battery deactivation with redox shuttles for safe and efficient recycling

To safely recycle spent lithium-ion batteries (LIBs), their deactivation as a pretreatment is essential. However, the conventional deactivation methods, mainly inducing an external short circuit, cannot be applied to LIBs with disconnected electrical circuits or Li deposited, despite their safety risk. Here, we propose a deactivation method using redox shuttles (RSs). The addition of an RS with redox potentials located between the two electrode potentials into a LIB electrochemically induces an internal short circuit with or without disconnected electrical circuits. A fully charged LIB discharges to approximately 0 V when a deactivation agent containing ferrocene or phenothiazine as an RS is added. Moreover, we demonstrate that RSs introduced into LIB can simultaneously dissolve Li deposited on the negative electrode surface and return it to the positive electrode as mobile ions. These characteristics of our method contribute to the improvement in safety and collection rate of Li in the recycling processes, promoting the sustainability of LIBs.

Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi_0.86Co_0.11Al_0.03O₂-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi_0.83Co_0.11Mn_0.07O₂-based positive electrodes and batteries with the blend of Li(NiCoMn)O₂ - Li(NiCoAl)O₂ positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.

Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.

Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

Grid-tied energy storage will play a key role in the reduction of carbon emissions. Systems based on Li-ion batteries could be good candidates for the task, especially those using lithium titanate negative electrodes. In this work, we will present the study of seven years of usage of a lithium titanate-based battery energy storage system on an isolated island grid. We will show that, even after seven years, the modules’ capacity loss is below 10% and that overall the battery is still performing within specifications. From our results, we established a forecast based on the internal degradation mechanisms of the hottest and coldest modules to show that the battery full lifetime on the grid should easily exceed 15 years. We also identified some inaccuracies in the online capacity estimation methodology which complicates the monitoring of the system.

Analysis of the Effect of the Variable Charging Current Control Method on Cycle Life of Li-ion Batteries

Applications of rechargeable batteries have recently expanded from small information technology (IT) devices to a wide range of other industrial sectors, including vehicles, rolling stocks, and energy storage system (ESS), as a part of efforts to reduce greenhouse gas emissions and enhance convenience. The capacity of rechargeable batteries adopted in individual products is meanwhile increasing and the price of the batteries in such products has become an important factor in determining the product price. In the case of electric vehicles, the price of batteries has increased to more than 40% of the total product cost. In response, various battery management technologies are being studied to increase the service life of products with large-capacity batteries and reduce maintenance costs. In this paper, a charging algorithm to increase the service life of batteries is proposed. The proposed charging algorithm controls charging current in anticipation of heating inside the battery while the battery is being charged. The validity of the proposed charging algorithm is verified through an experiment to compare charging cycles using high-capacity type lithium-ion cells and high-power type lithium-ion cells.