scholarly journals Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 75143-75152 ◽  
Author(s):  
Yohwan Choi ◽  
Seunghyoung Ryu ◽  
Kyungnam Park ◽  
Hongseok Kim
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Capacity Estimation ◽  
Battery Capacity

scholarly journals Fast battery capacity estimation using convolutional neural networks

2020 ◽  
pp. 014233122096642 ◽  
Author(s):  
Yihuan Li ◽  
Kang Li ◽  
Xuan Liu ◽  
Li Zhang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Smart Grids ◽  
Estimation Method ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Capacity Estimation ◽  
Starting Point ◽  
Battery Capacity ◽  
Mean Square Errors

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

scholarly journals Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yujie Cheng ◽  
Laifa Tao ◽  
Chao Yang
Keyword(s):  
Lithium Ion Battery ◽  
Feature Vector ◽  
Geodesic Distance ◽  
Lithium Ion ◽  
Visual Cognition ◽  
Cognitive Computing ◽  
Capacity Estimation ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Low Dimensional

This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using non-subsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing.

scholarly journals Data-driven lithium-ion battery capacity estimation from voltage relaxation

2021 ◽  
Author(s):  
Jiangong Zhu ◽  
Yuan Huang ◽  
Michael Knapp ◽  
Xinhua Liu ◽  
Yixiu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion ◽  
Predictive Ability ◽  
Support Vector ◽  
Capacity Estimation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Voltage Curve ◽  
Battery Capacity ◽  
Nominal Capacity

Abstract Accurate capacity estimation is critical for reliable and safe operation of lithium-ion batteries. A proposed approach exploiting features from the relaxation voltage curve enables battery capacity estimation without requiring previous cycling information. Machine learning methods are used in the approach. A dataset including 27,330 data units are collected from batteries with LiNi0.86Co0.11Al0.03O2 cathode (NCA battery) cycled at different temperatures and currents until reaching about 71% of their nominal capacity. One data unit comprises three statistical features (variance, skewness, and maxima) derived from the relaxation voltage curve after fully charging and the following discharge capacity for verification. Models adopting machine learning methods, i.e., ElasticNet, XGBoost, Support Vector Regression (SVR), and Deep Neural Network (DNN), are compared to estimate the battery capacity. Both XGBoost and SVR methods show good predictive ability with 1.1 % root-mean-square error (RMSE). The DNN method presents a 1.5% RMSE higher than that obtained using ElasticNet and SVR. 30,312 data units are extracted from batteries with LiNi0.83Co0.11Mn0.07O2 cathode (NCM battery). The model trained by the NCA battery dataset is verified on the NCM battery dataset without changing model weights. The test RMSE is 3.1% for the XGBoost method and 1.8% RMSE for the DNN method, indicating the generalizability of the capacity estimation approach utilizing battery voltage relaxation.

scholarly journals An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

2020 ◽  
Vol 266 ◽  
pp. 114817 ◽  
Author(s):  
Yujie Cheng ◽  
Dengwei Song ◽  
Zhenya Wang ◽  
Chen Lu ◽  
Noureddine Zerhouni
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Time Varying ◽  
Capacity Estimation ◽  
Battery Capacity ◽  
Weight Allocation

Modeling and numerical simulation of the battery capacity estimation based on neural network

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840062 ◽  
Author(s):  
Mingfang Liu ◽  
Bei Li ◽  
Fafa Qian ◽  
Guanzhou Qian
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Numerical Simulation ◽  
Lithium Ion Battery ◽  
Working Conditions ◽  
Lithium Ion ◽  
Capacity Estimation ◽  
Discharge Characteristics ◽  
Battery Capacity ◽  
Input Parameters

The lithium ion battery is considered as the experimental object, and its discharge characteristics are studied. A model of the battery in different charge-states is established by a tool of neural network while battery’s rebound voltage, temperature and load are set as input parameters. The validity of the model is tested based on the experimental data. The accuracy, adaptability and stability of the SOC in this model is validated in a variety of the working conditions, and the accuracy of the model is demonstrated to be higher than 5%.

Sign in / Sign up

Export Citation Format

Share Document