Search

High maintenance costs and safety risks due to lithium-ion battery degeneration have significantly and seriously restricted the application potential of batteries. Thus, this paper proposes an efficient calculation approach for state of health (SOH) estimation in lithium-ion batteries that can be implemented in battery management system (BMS) hardware. First, from the variables of the charge profile, only the complete voltage data is taken as the input to represent the complete aging characteristics of the batteries while limiting the computational complexity. Then, this paper combines the light gradient boosting machine (LightGBM) and weighted quantile regression (WQR) methods to learn a nonlinear mapping between the measurable characteristics and the SOH. A confidence interval is applied to quantify the uncertainty of the SOH estimate, and the model is called LightGBM-WQR. Finally, two public datasets are employed to verify the proposed approach. The proposed LightGBM-WQR model achieves high accuracy in its SOH estimation, and the average absolute error (MAE) of all cells is limited to 1.57%. In addition, the average computation time of the model is less than 0.8 ms for ten runs. This work shows that the model is effective and rapid in its SOH estimation. The SOH estimation model has also been tested on the edge computing module as a possible innovation to replace the BMS bearer computing function, which provides tentative solutions for online practical applications such as energy storage systems and electric vehicles. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method's high efficiency and speed. [ABSTRACT FROM AUTHOR]

Non-maximum suppression (NMS) is widely adopted as a post-processing step in the state-of-the-art object detection pipelines to merge the nearby detections around one object. However, its performance is affected by objects that are highly overlapped with each other, and its localization accuracy depends solely on the highest scored detection. To tackle this, an accurate NMS method is proposed in this letter, which gradually merges the highly overlapped detections in an iterative way. In each iteration, detections overlapped with the highest scored one are grouped with a harder threshold to regress for a new proposal, and then the scores within the group are softly suppressed. This process is recursively applied on the remaining detections. The proposed method can not only detect more overlapped objects, but also achieve better object localization accuracy. Experimental results demonstrate that this simple and unsupervised method can gain obvious performance improvement on the majority of classes, compared with the state-of-the-art NMS methods. [ABSTRACT FROM PUBLISHER]