Etransportation最新文献

The low economic feasibility caused by inefficient testing and inaccurate performance estimation is one of the main bottlenecks in the echelon utilization of large-scale retired batteries. This study proposes a fast and accurate capacity estimation method for retired batteries based on electrochemical impedance spectroscopy (EIS). Firstly, the EIS of the batteries that experience multi-condition aging in the laboratory are collected. EIS characteristic parameter sequences highly related to battery performance, including real part and magnitude, are directly extracted to establish a base bi-directional long short-term memory model. Secondly, a transfer learning method based on feature matching is designed, which applies a linear transformation layer to map the features between the source and target domains. The proposed transfer learning method has been effectively validated on laboratory battery data measured at different temperatures and retired battery datasets of different material types. The improvements are especially notable for retired batteries. The detection time has been reduced, with each cell requiring only 1.67 min. And using only a small amount of data as input for transfer learning can achieve an accuracy improvement of over 90 %, indicating an effective transfer channel from the base model established on laboratory small-capacity battery aging data to large-capacity retired battery data is successfully established for the first time. For retired nickel-cobalt-manganese batteries, the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) are 2.33 % and 2.75 %, respectively, while for retired lithium-iron-phosphate batteries, the MAPE and RMSPE reached 4.12 % and 5.04 %, respectively. The results demonstrate the proposed method reduces the cost of repeated testing, modeling, and training for specific retired batteries while maintaining the accuracy of capacity estimation. This advancement helps to improve the efficiency of large-scale retired battery grading, and injects new momentum into facilitating more effective decision-making processes.

Lithium-ion batteries (LIBs) play a pivotal role in promoting transportation electrification and clean energy storage. The safe and efficient operation is the biggest challenge for LIBs. Smart batteries and intelligent management systems are one of the effective solutions to address this issue. Multiparameter monitoring is regarded as a promising tool to achieve the goal. This paper provides an overview of the state of the art in multiparameter monitoring approaches for LIBs. Further, the sensing principle, experimental configuration, and sensor performance are elaborated and discussed. The results show that internal parameter monitoring of cells is more attractive and challenging than external parameter monitoring. Temperature, deformation, and gas are the most concerned parameters inside batteries. Finally, the outlooks and challenges for the implementation and application of LIB multiparameter monitoring are investigated from two aspects: internal parameters monitoring and application of the monitored multivariate data. Compact, precise, and stable sensors compatible with the internal environment of batteries as well as efficient and intelligent algorithms for battery management are still awaiting breakthroughs.

Accurate battery health estimation is pivotal for ensuring the safety and performance of electric vehicles (EVs). While predominant research has centered on laboratory-level single cells, the accurate estimation of battery system capacity using real-world data remains a challenge, due to the vast diversity in battery types, operating conditions, data recordings, etc. To this end, we release three large-scale field datasets of 464 EVs from three manufacturers, comprising over 1.2 million charging snippets. The EVs’ capacity and State of Health (SOH) are effectively labeled using K-means to cluster and concatenate charging snippets. A robust data-driven framework integrating a Gated Convolutional Neural Network (GCNN) for estimating battery capacity is proposed, and the results outperform other machine learning models. In addition, a fine-tuning technique is employed to further enhance model efficacy on new datasets and with limited training data. This research not only advances battery health estimations but also paves the way for broader applications in battery management systems (BMSs), offering a scalable solution to real-world challenges in battery technology.

Understanding the failure behaviors and failure mechanisms of lithium-ion batteries under mechanical abuse is essential for numerical reconstruction of abuse scenarios for different types of cells. This study investigates the mechanical-electrical-thermal characteristics, components tensile properties and failure mechanisms of LiFePO₄ (LFP), Li(Ni_0.5Mn_0.3Co_0.2)O₂ (NMC), and Li₂TiO₃ (LTO) cells through indentation experiments, including ball intrusion, cylindrical intrusion, and out-of-plane compression modes at quasi-static loading rates. Additional ball intrusion experiments were conducted at varying loading rates. This study compares the effects of different material systems on battery performance under standardized mechanical abuse conditions. Post-test examinations analyze surface damage and internal component fracture morphology. Two distinct fracture modes were observed: ductile fracture and brittle fracture. The findings suggest that, under the same loading mode, LTO cells exhibit distinct failure behavior compared to NMC and LFP cells, attributed to differing material properties and resulting fracture modes during intrusion. Based on the analysis of the tensile results of the battery components, the cell fracture mode may be related to the tensile strength of the separator. The loading rate significantly impacts the mechanical-electrical-thermal performance of pouch cells, resulting in increased cell stiffness and shorter internal short circuit duration at higher loading speeds. However, the effect of loading rate is consistent across cells with different material systems.