15,453 research outputs found
Novel data-efficient mechanism-agnostic capacity fade model for Li-ion batteries
No full textAccurate capacity fade prediction of Li-ion batteries is essential to reduce the time spent by manufacturers in performing quality assurance tests and to ensure the safety and durability of these batteries for end users. Various complicated aging mechanisms and the resulting capacity fade phenomena of Li-ion batteries make such predictions challenging; thus, mechanism-agnostic approaches using empirical and data-driven models are considered to be promising. This article proposes a mechanism-agnostic capacity fade empirical model called aging density function model (ADFM) for Li-ion batteries. Developed by innovating existing empirical models, the proposed ADFM predicts capacity fades for arbitrary battery input current trajectories, requires no additional experiments at the prediction phase, and reflects real batteries phenomena such as the varying amount of capacity fade for each cycle. As the proposed ADFM could generate a large amount of synthetic data, it was augmented with Bayesian neural networks (BNNs) to enhance its data efficiency. As a result, it can completely utilize the experimental data and achieve reasonable prediction accuracy regardless of the amount of experimental data. This BNN-augmented ADFM can also provide the reliability of the capacity fade prediction to ensure safety. Through charge/discharge cycle tests with an NCM/graphite Li-ion battery, the proposed BNN-augmented ADFM was shown to provide good performance in terms of the capacity fade prediction accuracy, with a mean absolute error of approximately 0.5% and maximum absolute error of approximately 2.5%.11Nsciescopu
OnNetwork+
No full textNetwork errors such as packet losses consume large amounts of energy. We analyzed the reason for this through measurements using the latest smartphones and full-system simulation. We found that on packet losses the smartphones maintain high frequencies for CPU without doing useful work. To address this problem, we propose a method for reducing the energy consumption by lowering the performance level by exploiting a dynamic voltage and frequency scaling mechanism when long network delays are expected. According to our experiments, our method reduces the total energy consumption of web browsing on two different smartphones by up to 10.0% and 11.5%, respectively.</jats:p
Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix
No full textMonitoring the state of health (SOH) for Li-ion batteries is crucial in the battery management system (BMS), for their efficient and safe use. Due to time-varying battery parameters and insufficient computation capability of the BMSs, computationally efficient online parameter identification is practically required. So, a simple equivalent circuit model (ECM) based recursive least squares (RLS) parameter identification algorithm has been widely used. However, it has long been acknowledged that this algorithm suffers from wind-up problem when the input current doesnt provide sufficient excitation. It causes numerical instability and then induces large sensitivity of identified parameter values to the noise or truncation error of sensor data, leading to large parameter identification errors. In this work, a new reliable version of ECM based RLS, called a condition number based recursive least squares (CNRLS) algorithm, is proposed to avoid large errors due to insufficient excitation by monitoring the condition number of the error covariance matrix If the condition number is greater than a certain prescribed value, currently identified parameters are considered unreliable and hence the proposed algorithm uses stored internal variables previously computed with sufficiently exciting input current, leading to small condition number of the error covariance matrix. Accordingly, the forgetting factor is also adjusted to give a larger weight to such stored internal variables in order to overcome the insufficient excitation of the input current. It is shown with a1-RC equivalent circuit model that the proposed CNRLS algorithm is more noise-tolerant and accurate than two benchmarks including the standard RLS and adaptive forgetting factor RLS (AFFRLS) in terms of mean absolute errors, with almost the same computing cost.11Ysciescopu
Author Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
No full textThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
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