24 research outputs found
Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries
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 data-driven
diagnostic technique, Gaussian Process regression for In-situ Capacity
Estimation (GP-ICE), which estimates battery capacity using voltage
measurements over short periods of galvanostatic operation. Unlike previous
works, GP-ICE does not rely on interpreting the voltage-time data as
Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes
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. GP-ICE is applied to two datasets,
consisting of 8 and 20 cells respectively. In each case, within certain voltage
ranges, as little as 10 seconds of galvanostatic operation enables capacity
estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial
Informatic
Combining offline and online machine learning to estimate state of health of lithium-ion batteries
This article reports a new state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with cell inconsistency and online implementability are addressed using a proposed individualized estimation scheme that blends a model migration method with ensemble learning. A set of candidate models, based on slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are first trained offline by choosing a single-point feature on the incremental capacity curve as the model input. For online operation, the prediction errors due to cell inconsistency in the target new cell are next mitigated by a proposed modified random forest regression (mRFR) for high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably high SOH estimation accuracy with only a small amount of early data and online measurements are needed for practical operation
An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses
Situation awareness is essential for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoyanalogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world is typically limited to a small range of sea states, the ML method might suffer from catastrophic failure when the encountered sea state is not in the training dataset. This paper proposes a hybrid approach that combined the two methods above. The ML method is compensated by the WBA method based on the uncertainty of estimation results and, thus, the catastrophic failure can be avoided. Real-world historical data from the Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the hybrid approach improves estimation accuracy.acceptedVersio
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
Abstract: Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)—a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis—with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures—the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems
Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles
In this study, a machine learning method is proposed for online diagnosis of battery state of health. A prediction model for future voltage profiles is established based on the extreme learning machine algorithm with the short-term charging data. A fixed size least squares-based support vector machine with a mixed kernel function is employed to learn the dependency of state of health on feature variables generated from the charging voltage profile without preprocessing data. The simulated annealing method is employed to search and optimize the key parameters of the fixed size least squares support vector machine and the mixed kernel function. By this manner, the proposed algorithm requires only partial random and discontinuous charging data, enabling practical online diagnosis of state of health. The model training and experimental validation are conducted with different kernel functions, and the influence of voltage range and noise are also investigated. The results indicate that the proposed method can not only maintain the state of health estimation error within 2%, but also improve robustness and reliability
Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data
To meet the fairly high safety and reliability requirements in practice, the
state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a
close relationship with the degradation performance, has been extensively
studied with the widespread applications of various electronics. The
conventional SOH estimation approaches with digital twin are end-of-cycle
estimation that require the completion of a full charge/discharge cycle to
observe the maximum available capacity. However, under dynamic operating
conditions with partially discharged data, it is impossible to sense accurate
real-time SOH estimation for LIBs. To bridge this research gap, we put forward
a digital twin framework to gain the capability of sensing the battery's SOH on
the fly, updating the physical battery model. The proposed digital twin
solution consists of three core components to enable real-time SOH estimation
without requiring a complete discharge. First, to handle the variable training
cycling data, the energy discrepancy-aware cycling synchronization is proposed
to align cycling data with guaranteeing the same data structure. Second, to
explore the temporal importance of different training sampling times, a
time-attention SOH estimation model is developed with data encoding to capture
the degradation behavior over cycles, excluding adverse influences of
unimportant samples. Finally, for online implementation, a similarity
analysis-based data reconstruction has been put forward to provide real-time
SOH estimation without requiring a full discharge cycle. Through a series of
results conducted on a widely used benchmark, the proposed method yields the
real-time SOH estimation with errors less than 1% for most sampling times in
ongoing cycles.Comment: This paper has been accepted for IEEE Transactions on Industrial
Informatic
Digital Twins for Lithium-Ion Battery Health Monitoring with Linked Clustering Model using VGG 16 for Enhanced Security Levels
Digital Twin (DT) has only been widely used since the early 2000s. The concept of DT refers to the act of creating a computerized replica of a physical item or physical process. There is the physical world, the cyber world, a bridge between them, and a portal from the cyber world to the physical world. The goal of DT is to create an accurate digital replica of a previously existent physical object by combining AI, IoT, deep learning, and data analytics. Using the virtual copy in real time, DTs attempt to describe the actions of the physical object. Battery based DT's viability as a solution to the industry's growing problems of degradation evaluation, usage optimization, manufacturing irregularities, and possible second-life applications, among others, are of fundamental importance. Through the integration of real-time checking and DT elaboration, data can be collected that could be used to determine which sensors/data used in a batteries to analyze their performance. This research proposes a Linked Clustering Model using VGG 16 for Lithium-ion batteries health condition monitoring (LCM-VGG-Li-ion-BHM). This work explored the use of deep learning to extract battery information by selecting the most important features gathered from the sensors. Data from a digital twin analyzed using deep learning allowed us to anticipate both typical and abnormal conditions, as well as those that required closer attention. The proposed model when contrasted with the existing models performs better in health condition monitoring
Linearizing Battery Degradation for Health-aware Vehicle Energy Management
The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.</p
A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions.
The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the energy management methods, the state of charge is estimated by introducing the differential Kalman filtering method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient. The model simulates the transient response with high precision which is suitable for its high current and complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the model is differential and the improved iterate calculation model is obtained. As can be known from the experimental verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which can estimate its state of charge value with considerably high precision, providing an effective energy management strategy for the ternary lithium batteries