Deep learning to estimate lithium-ion battery state of health without additional degradation experiments

Battery state of health estimation under fast charging via deep transfer learning

Accurate state of health (SOH) estimation is essential for effective lithium-ion battery management, particularly under fast-charging conditions with a constrained voltage window. This study proposes a hybrid deep neural network (DNN) learning model to improve SOH prediction. With approximately 22,000 parameters, the model effectively estimates battery capacity by combining local feature extraction (convolutional neural networks [CNNs]) and global dependency analysis (self-attention). The model was validated on 222 lithium iron phosphate (LFP) batteries, encompassing 146,074 cycles, with limited data availability in a state of charge (SOC) range of 80%-97%. Trained on fast-charging protocols (3.6C-8C charge, 4C discharge), it demonstrates high predictive accuracy, achieving a mean absolute percentage error (MAPE) of 3.89 mAh, a root-mean-square error (RMSE) of 4.79 mAh, and a coefficient of determination (R2) of 0.97. By integrating local and global analysis, this approach significantly enhances battery aging detection under fast-charging conditions, demonstrating strong potential for battery health management systems.

Lithiophilic-Gradient, Li+ Supplementary Interphase Design for Lean Lithium Metal Batteries

The practicability of anode-less/free lithiummetal batteries (LMBs) is impeded by unregulated dendrite formation on thedeposition substrate. Herein, this study presents a lithiophilic-gradient, layer-stacked interfacial design for the lean lithium metal battery (LLMB) model. Engineered via a facile wet-chemistry approach, the high entropy metalphosphide (HEMP) particles with tunable lithiophilic species are dispersedwithin reduced graphene oxide (RGO). Moreover, a poly (vinylidene fluoride co-hexafluoropropylenepolymer) (PVDF-HFP), blended with molten Li at the tailorable amounts, forms aLi supplementary top layer through a layer-transfer printing technique. Theintegrated layer (HEMP@RGO-MTL@PH) not only regulates the dendrite-free lithiumdeposition towards the Cu substrate up to 10 mAh cm-2, but also maintains robust cyclability of the symmetric cell at 5 mA cm-2 even under 83% depth of discharge. As pairing the modified Cu foil with the LiNi0.8Mn0.1Co0.1O2 cathode (NCM811, 16.9 mg cm-2, double sided, N/P ratio of 0.21) in the 200 mAh pouch cell, achieves gravimetric energy densities of 414.7 Wh kg-1, power output of 977.1 W kg-1, as well as highly reversible phasic evolutionmonitored in operando. This gradient interfacial strategy can promotethe commercialization of energy/power-dense energy storage solutions.

Data-driven energy management for electric vehicles using offline reinforcement learning

Energy management technologies have significant potential to optimize electric vehicle performance and support global energy sustainability. However, despite extensive research, their real-world application remains limited due to reliance on simulations, which often fail to bridge the gap between theory and practice. This study introduces a real-world data-driven energy management framework based on offline reinforcement learning. By leveraging electric vehicle operation data, the proposed approach eliminates the need for manually designed rules or reliance on high-fidelity simulations. It integrates seamlessly into existing frameworks, enhancing performance after deployment. The method is tested on fuel cell electric vehicles, optimizing energy consumption and reducing system degradation. Real-world data from an electric vehicle monitoring system in China validate its effectiveness. The results demonstrate that the proposed method consistently achieves superior performance under diverse conditions. Notably, with increasing data availability, performance improves significantly, from 88% to 98.6% of the theoretical optimum after two updates. Training on over 60 million kilometers of data enables the learning agent to generalize across previously unseen and corner-case scenarios. These findings highlight the potential of data-driven methods to enhance energy efficiency and vehicle longevity through large-scale vehicle data utilization.

Data-driven available capacity estimation of lithium-ion batteries based on fragmented charge capacity

Efficient and accurate available capacity estimation of lithium-ion batteries is crucial for ensuring the safe and effective operation of electric vehicles. However, incomplete charging cycles in practical applications challenge conventional methods. Here we manipulate fragmented charge capacity data to estimate available capacity without complete charging information. Considering correlation, charging time, and initial state of charge, 36 feature combinations are available for estimation. The basic machine learning model is established on 11,500 cyclic samples, and a transfer learning model is fine-tuned and validated on multiple datasets. The validation results indicate that the best root-mean-square error for the basic model is 0.012. Furthermore, the RMSE demonstrates consistent stability across different datasets in the transfer learning model, with fluctuations within 0.5% when considering feature combinations across cycles with spacings of 5, 10, and 20. This work highlights the promise of available capacity estimation using actual, readily accessible fragmented charge capacity data.

Multi-modal framework for battery state of health evaluation using open-source electric vehicle data

Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles.

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges.

Interpretable Learning of Accelerated Aging in Lithium Metal Batteries

Lithium metal batteries (LMBs) with high energy density are perceived as the most promising candidates to enable long-endurance electrified transportation. However, rapid capacity decay and safety hazards have impeded the practical application of LMBs, where the entangled complex degradation pattern remains a major challenge for efficient battery design and engineering. Here, we present an interpretable framework to learn the accelerated aging of LMBs with a comprehensive data space containing 79 cells varying considerably in battery chemistries and cell parameters. Leveraging only data from the first 10 cycles, this framework accurately predicts the knee points where aging starts to accelerate. Leaning on the framework's interpretability, we further elucidate the critical role of the last 10%-depth discharging on LMB aging rate and propose a universal descriptor based solely on early cycle electrochemical data for rapid evaluation of electrolytes. The machine learning insights also motivate the design of a dual-cutoff discharge protocol, which effectively extends the cycle life of LMBs by a factor of up to 2.8.

Prediction and analysis of relative error in electric vehicle charging stations based on an improved ConvFormer model

In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. The model combines Convolutional Neural Networks (CNN) with Transformer models in parallel, significantly improving the prediction accuracy. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE (Mean Absolute Error) and MSE (Mean Squared Error), are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 h is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 47.30 % reduction in MAE compared to the Transformer model and a 38.06 % reduction compared to LSTM, and a 66.94 % reduction in MSE compared to the Transformer model and approximately 62.32 % reduction compared to LSTM.

Data Science Applications in Circular Economy: Trends, Status, and Future

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

A green strategy for upcycling utilization of core parts from end-of-life vehicles (ELVs): Pollution source analysis, technology flowchart, technology upgrade

The disposal of end-of-life vehicles (ELVs) is an issue of great concern to the society, because of its huge amount, resource value and environmental pollution. A wide variety of pollutants generate and release during the recycling process. However, previous studies are piecemeal and segmentary, the correlation between treatment flowchart and pollution is unknown, and pollution source analysis in ELV recycling and core parts (engine, gear box, etc.) remanufacturing bases is still a challenge. In this study, the aim is to propose a green strategy for upcycling utilization of ELV part based on pollution source analysis, technology flowchart, and technology upgrade. We synthetically analyzed current typical ELV dismantling and core part remanufacturing processes of ELVs. A total of 36 volatile organic compound (VOC) species and 7 heavy metals were found in dismantling process, and 61 VOC species were detected in remanufacturing process. Based on statistical analysis and treatment process characteristics, 18 pollution fingerprints were constructed. At last, an intelligent dismantling and upcycle utilization line for ELVs has been developed to improve production efficiency and reduce pollution release.