scholarly journals Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Renato G. Nascimento ◽  
Matteo Corbetta ◽  
Chetan S. Kulkarni ◽  
Felipe A. C. Viana
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Lithium Ion ◽  
Unmanned Aircraft ◽  
Model Parameters ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Model Form ◽  
Battery Capacity ◽  
Battery Discharge

Lithium-ion batteries are commonly used to power electric unmanned aircraft vehicles (UAVs).Therefore, the ability to model both the state of charge as well as battery health is very important for reliable and affordable operation of UAV fleets.Even though models based on first principles are accurate and trustworthy, the complex electro-chemistry that governs battery discharge and aging makes it hard to build and use such models for in-time monitoring of battery conditions.Moreover, the careful tuning or estimation of high-fidelity model parameters hampers the straightforward deployment in the field.Alternatively, reduced order models have the advantage of capturing the overall behavior of battery discharge. Reduced-order principle-based models are built by carefully simplifying the physics/chemistry such that computational cost is dramatically reduced while the overall behavior of the system is still captured.These simplifications also lead to a number of parameters to be estimated based on data as well as residual discrepancy (model-form uncertainty).This approach can lead to a number of parameters to be estimated based on data as well as residual model-form uncertainty; a property shared with machine learning models. The latter are solely built on the basis of data, and can still capture unexpected nonlinearities.The drawback is that traditional machine learning tends to require large number of data points hard to retrieve in many scientific and engineering fields like, for example, the field of battery discharge and degradation prediction. In this paper, we will present a hybrid modeling approach for tracking and forecasting battery aging based on ``as-used'' conditions.Our approach directly implements a reduced-order model based on Nerst and Butler-Volmer equations within a deep neural network framework.While most of the input-output relationship is captured by reduced-order models, the data-driven kernels reduce the gap between predictions and observations.The hybrid model estimates the overall battery discharge, and a multilayer perceptron models the battery internal voltage.Battery aging is characterized by time-dependent internal resistance and the amount of available Li-ions.We address the difficult issue of building and updating the aging model by reducing the need for reference discharge cycles.This is beneficial to operators, since it reduces the need of taking the batteries out of commission.We compensate for lack of reference discharge cycles by using a probabilistic model that leverages previously available fleet-wide information. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence website.Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.Moreover, the model can help optimizing battery operation by offering long-term forecast of battery capacity.

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%.

scholarly journals Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhe Bai ◽  
Liqian Peng
Keyword(s):  
Machine Learning ◽  
Nonlinear Dynamical Systems ◽  
Reduced Order Models ◽  
Simulation Code ◽  
Nonlinear Dynamical ◽  
Reduced Order ◽  
Nonlinear Model Reduction ◽  
Regression Techniques ◽  
Low Dimensional ◽  
Modern Machine

AbstractAlthough projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

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.

Detection of Cracks in Mistuned Bladed Disks Using Reduced-Order Models and Vibration Data

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Chulwoo Jung ◽  
Akira Saito ◽  
Bogdan I. Epureanu
Keyword(s):  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Equations Of Motion ◽  
Computational Cost ◽  
Three Dimensional ◽  
Cost Savings ◽  
Forced Response ◽  
Response Analysis ◽  
Reduced Order Models ◽  
Reduced Order

A novel methodology to detect the presence of a crack and to predict the nonlinear forced response of mistuned turbine engine rotors with a cracked blade and mistuning is developed. The combined effects of the crack and mistuning are modeled. First, a hybrid-interface method based on component mode synthesis is employed to develop reduced-order models (ROMs) of the tuned system with a cracked blade. Constraint modes are added to model the displacements due to the intermittent contact between the crack surfaces. The degrees of freedom (DOFs) on the crack surfaces are retained as active DOFs so that the physical forces due to the contact/interaction (in the three-dimensional space) can be accurately modeled. Next, the presence of mistuning in the tuned system with a cracked blade is modeled. Component mode mistuning is used to account for mistuning present in the uncracked blades while the cracked blade is considered as a reference (with no mistuning). Next, the resulting (reduced-order) nonlinear equations of motion are solved by applying an alternating frequency/time-domain method. Using these efficient ROMs in a forced response analysis, it is found that the new modeling approach provides significant computational cost savings, while ensuring good accuracy relative to full-order finite element analyses. Furthermore, the effects of the cracked blade on the mistuned system are investigated and used to detect statistically the presence of a crack and to identify which blade of a full bladed disk is cracked. In particular, it is shown that cracks can be distinguished from mistuning.

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 Reduced order models and machine learning in analysis and optimum design of structures

2019 ◽  
Author(s):  
Νικόλαος Καλλιώρας
Keyword(s):  
Machine Learning ◽  
Optimum Design ◽  
Isotropic Material ◽  
Harmony Search ◽  
Reduced Order Models ◽  
Generative Design ◽  
Reduced Order ◽  
Boltzmann Machines ◽  
Deep Boltzmann Machines

Ο βέλτιστος σχεδιασμός κατασκευών απασχολεί τον άνθρωπο από την εποχή της πρώτης κατασκευής. Το ενδιαφέρον αυτό αυξήθηκε με την αύξηση του μεγέθους και της πολυπλοκότητας των κατασκευών. Η ανάλυση των κατασκευών και ο υπολογιστικός της φόρτος εξαρτάται από το μέγεθος και την πολυπλοκότητα των κατασκευών. Η χρήση προσεγγιστικών μεθόδων αυξήθηκε λόγω της αύξησης του υπολογιστικού φόρτου που απαιτούν οι ακριβείς μέθοδοι. Στην παρούσα διδακτορική διατριβή παρουσιάζεται συνεισφορά στους μεταευρετικούς αλγόριθμους, τα μοντέλα μειωμένης τάξης, τους αλγόριθμους μηχανικής μάθησης, την βελτιστοποίησης τοπολογίας και το generative design. Συγκεκριμένα, παρουσιάζεται ένας νέος μεταευρετικός αλγόριθμος που δημιουργήθηκε στα πλαίσια της διδακτορικής διατριβής αλλά και μια βελτιωμένη έκδοση του αλγόριθμου Harmony Search που αρχικά έχει προταθεί από τον Καθηγητή κ. Zong Woo Geem. Επίσης παρουσιάζονται τέσσερις διαφορετικές μεθοδολογίες συνδυασμού αλγορίθμων βαθιών νευρωνικών δικτύων και του αλγόριθμου βελτιστοποίησης τοπολογίας Solid Isotropic Material with Penalization (SIMP). Η πρώτη μεθοδολογία, DL-TOP, χρησιμοποιεί Deep Boltzmann Machines για να προβλέψει την τελική πυκνότητα των πεπερασμένων στοιχείων στη διαδικασία της βελτιστοποίησης τοπολογίας μελετώντας πλήθος αρχικών τιμών τους. Η μεθοδολογία DL-SCALE χρησιμοποιεί Deep Boltzmann Machines σε μια λογική Model Upgrading για να επιταχύνει την βελτιστοποίηση τοπολογίας μέσω μοντέλων μειωμένης τάξης και πύκνωσης του πλέγματος των πεπερασμένων στοιχείων. Η Τρίτη μεθοδολογία, DLRM-TOP, χρησιμοποιεί βαθιά νευρωνικά δίκτυα για να προβλέψει την τελική πυκνότητα κάθε πεπερασμένου στοιχείου βάση πληροφορίας από την τελική κατάσταση των μοντέλων μειωμένης τάξης. Η τέταρτη μεθοδολογία, CN-TOP, χρησιμοποιεί βαθιά συνελικτικά νευρωνικά δίκτυα που βελτιώνουν την ποιότητα εικόνας για την επιτάχυνση της βελτιστοποίησης τοπολογίας. Τέλος παρουσιάζεται μια λογική συνδυασμού βαθιών νευρωνικών δικτύων και SIMP για την αυτόματη παραγωγή πληθώρας σχεδιασμών χωρίς την παρέμβαση του χρήστη σε μια λογική generative design. Το μόνο που απαιτείται από τον χρήστη είναι ο ορισμός του προβλήματος. Τα αποτελέσματα των παραπάνω μεθοδολογιών (επιτάχυνση διαδικασιών και παραγωγή σχεδιασμών) που παρουσιάζονται στην διδακτορική διατριβή κάνουν φανερό πως η μηχανική μάθηση και οι σύγχρονες τεχνικές της μπορούν να αποτελέσουν σημαντικά εργαλεία στην επιστήμη του πολιτικού μηχανικού.

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