Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201

Comparison of a Particle Filter and Other State Estimation Methods for Prognostics of Lithium-Ion Batteries

A particle filter (PF) is shown to be more accurate than non-linear least squares (NLLS) and an unscented Kalman filter (UKF) for predicting the remaining useful life (RUL) and time until end of discharge voltage (EODV) of a Lithium-ion battery. The three algorithms track four states with correct initial guesses and 5% variation on the initial guesses. The more accurate prediction performance of PF over NLLS and UKF is reported for three Lithium-ion battery models: a data-driven empirical model, an equivalent circuit model, and a physics-based single particle (SP) model

Integrated Bayesian Framework for Remaining Useful Life Prediction.

International audienceIn this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL. The proposed method extracts monotonic trends from offline sensor signals, which are used to build reference models. From online signals the method represents the uncertainty about the current status, using discrete Bayesian filter. Finally, the method predicts RUL of the monitored component using integrated method based on K-nearest neighbor (k-NN) and Gaussian process regression (GPR). The performance of the algorithm is demonstrated using two real data sets from NASA Ames prognostics data repository. The results show that the algorithm obtain good results for both application

A Distributed Approach to System-Level Prognostics

Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion

Reliability Study of Battery Lives: A Functional Degradation Analysis Approach

Renewable energy is critical for combating climate change, whose first step is the storage of electricity generated from renewable energy sources. Li-ion batteries are a popular kind of storage units. Their continuous usage through charge-discharge cycles eventually leads to degradation. This can be visualized in plotting voltage discharge curves (VDCs) over discharge cycles. Studies of battery degradation have mostly concentrated on modeling degradation through one scalar measurement summarizing each VDC. Such simplification of curves can lead to inaccurate predictive models. Here we analyze the degradation of rechargeable Li-ion batteries from a NASA data set through modeling and predicting their full VDCs. With techniques from longitudinal and functional data analysis, we propose a new two-step predictive modeling procedure for functional responses residing on heterogeneous domains. We first predict the shapes and domain end points of VDCs using functional regression models. Then we integrate these predictions to perform a degradation analysis. Our approach is fully functional, allows the incorporation of usage information, produces predictions in a curve form, and thus provides flexibility in the assessment of battery degradation. Through extensive simulation studies and cross-validated data analysis, our approach demonstrates better prediction than the existing approach of modeling degradation directly with aggregated data.Comment: 28 pages,16 figure

Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis

Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.Battery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs

Prediction of lithium-ion battery capacity by functional monitoring data using functional principal component analysis

Lithium-ion batteries have been a promising energy storage technology for applications such as electronics, automobiles, and smart grids over the years. Extensive research was conducted to improve the prediction of the remaining capacity of the lithium-ion battery. A robust prediction model would improve the battery performance and reliability for forthcoming usage. To develop a data-driven capacity prediction model of lithium-ion batteries most of past studies employed capacity degradation data, yet very few tried using other performance monitoring variables such as temperature, voltage, and current data to estimate and predict the battery capacity. In this thesis, we aim to develop a data-driven model for predicting the capacity of lithium-ion battery adopting functional principal component analysis applied to functional monitoring data of temperature, voltage, and current observations collected from NASA Ames Prognostics Center of Excellence repository. The result of capacity prediction has been substantiated with past studies and obtained root mean square error (RMSE) of 0.009. The proposed data-driven approach performs well to predict the capacity employing functional performance measures over the life span of a lithium-ion battery

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately

Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks are demonstrated on three data sets, particularly at the early stages of the battery lifetime