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 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 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 Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

scholarly journals A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Nan Zhou ◽  
Hong Liang ◽  
Jing Cui ◽  
Zeyu Chen ◽  
Zhiyuan Fang
Keyword(s):  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Feasible Region ◽  
Discharge Data ◽  
Online Estimation ◽  
Soc Estimation ◽  
Battery Capacity

The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Screening of retired lithium-ion batteries using incremental capacity charging curve based residual capacity estimation method for facilitating sustainable circular lithium-ion battery system

2021 ◽  
pp. 1-16
Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Characteristic Parameters ◽  
Capacity Estimation ◽  
Capacity Curve ◽  
Residual Capacity ◽  
Remaining Capacity ◽  
Incremental Capacity

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

2017 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Diagnostic Technique ◽  
Lithium Ion ◽  
Constant Current ◽  
Time Data ◽  
Capacity Estimation ◽  
Voltage Range ◽  
Battery Capacity

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

Sign in / Sign up

Export Citation Format

Share Document