Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.

Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

https://www.cell.com/joule/pdf/S2542-4351(19)30585-9.pdf

Battery Lifetime Prognostics

Gaussian process regression for forecasting battery state of health

https://www.prognostics.umd.edu/calcepapers/13_Prognostics_for_state.pdf

Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

Maximum releasable capacity and internal resistance are often used as the health indicators (HIs) of a lithium-ion battery for degradation modeling and estimation of remaining useful life (RUL). However, the maximum releasable capacity is usually difficult to estimate in online applications due to complex operating conditions in the field. Moreover, measuring the internal resistance is too expensive to be implemented on-line. In this paper, an HI extraction and optimization framework requiring only the operating parameters of lithium-ion batteries is proposed for battery degradation modeling and RUL estimation. The framework carries out raw HI extraction, transformation, correlation analysis, and verification and evaluation to achieve HI enhancement. In particular, the Box–Cox transformation is adopted to improve the correlation between the extracted HI and the battery’s actual degradation state. To estimate the battery’s RUL using the enhanced HI, an optimized relevance vector-machine algorithm is utilized, which can be performed in a flexible and agile way. Experimental studies using two different industrial testing data sets illustrate the high efficiency and adaptability of the proposed framework in lithium-ion battery degradation modeling and RUL estimation.

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Lithium-ion battery is a promising power source for electric vehicles owing to its high specific energy and power. Through monitoring battery health in effective way such as determining the operating conditions, planning replacement interval could increase the reliability and stability of the whole system. However, due to the reliance on integration, errors in terminal measurement caused by noise, resolution, the uncertainty when we make prognostics for battery health are cumulative, the prediction result is combined with unsatisfied errors. As a result, the prognostic algorithms supporting uncertainty representation and management are emphasized. So in this paper, we present the Gaussian process model to realize the prognostics for battery health. Because of the advantages of flexible, probabilistic, nonparametric model with uncertainty predictions, the Gaussian process model can provide variance around its mean predictions to describe associated uncertainty in the evaluation and prediction. To evaluate the proposed prediction approach, we have executed experiments with lithium-ion battery. Experimental results prove its effectiveness and confirm the algorithm can be effectively applied to the battery monitoring and prognostics. Furthermore, the comparison of prediction with different amounts of training data has been achieved, and the dynamic model is introduced to improve the prediction for the battery health.

Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression

Performance degradation and remaining useful life (RUL) estimation for lithium-ion battery has broad and practical applications in almost all industrial fields. The model-based prognostics is so complicated, moreover, they are not suitable for on-line application since that more parameters and modeling information should be obtained in advance. An on-line data-driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR) is proposed. With Online SVR algorithm, the lithium-ion battery monitoring data series can be forecasted precisely, on the other hand, an ensemble approach is adopted to realize combined prediction with multi-models containing off-line and on-line algorithms to achieve better prediction capacity. Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery.

Jianbao Zhou

Papers

Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression

Dynamic battery remaining useful life estimation: An on-line data-driven approach