A novel autoregressive rainflow-integrated moving average modeling method for the accurate state of health prediction of lithium-ion batteries.

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

A Lebesgue Sampling based Diagnosis and Prognosis Methodology with Application to Lithium-ion Batteries

Fault diagnosis and prognosis (FDP) plays an important role in the modern complex industrial systems to maintain their reliability, safety, and availability. Diagnosis aims to monitor the fault state of the component or the system in real-time. Prognosis refers to the generation of long-term predictions that describe the evolution of a fault and the estimation of the remaining useful life (RUL) of a failing component or subsystem. Traditional Riemann sampling-based FDP (RS-FDP) takes samples and executes algorithms in periodic time intervals and, in most cases, requires significant computational resources. This makes it difficult or even impossible to implement RS-FDP algorithms on hardware with very limited computational capabilities, such as embedded systems that are widely used in industries. To overcome this bottleneck, this proposal develops a novel Lebesgue sampling-based FDP (LS-FDP), in which FDP algorithms are implemented “as-neede”. Different from RS-FDP, LS-FDP divides the state axis by a number of predefined states (also called Lebesgue states). The computation of LS-based diagnosis is triggered only when the value of measurements changes from one Lebesgue state to another, or “event-triggered”. This method significantly reduces the computation demands by eliminating unnecessary computation. This LS-FDP design is generic and able to accommodate different algorithms, such as Kalman filter and its variations, particle filter, relevant vector machine, etc. This proposal first develops a particle filtering based LS-FDP for li-ion battery applications. To improve the accuracy and precision of the diagnosis and prognosis results, the parameters in the models are treated as time-varying ones and adjusted online by a recursive least square (RLS) method to accommodate the changing of dynamics, operation condition, and environment in the real cases. Uncertainty management is studied in LS-FDP to handle the uncertainties from inaccurate model structure and parameter, measurement noise, process noise, and unknown future loading. The extended Kalman filter implemented in the framework of LS-FDP yields a more efficient LS-EKF algorithm. The proposed method takes full advantage of EKF and Lebesgue sampling to alleviate computation requirements and make it possible to be deployed on most of the distributed FDP systems. All the proposed methods are verified by a study with the estimation of the state of health and RUL prediction of Lithium-ion batteries. The comparisons between traditional RS-FDP methods and LS-FDP show that LS-FDP has a much lower requirement on the computational resource. The proposed parameter adaptation and uncertainty management methods can produce more accurate and precise diagnostic and prognostic results. This research opens a new chapter for FDP method and make it easier to deploy FDP algorithms on the complicate systems build by embedded subsystem and micro-controllers with limited computational resources and communication band width

Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems.

Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction.

Battery life prediction is of great significance to the safe operation, and the maintenance costs are reduced. This paper proposed a hybrid framework considering feature extraction to solve the problem of data backward, large sample data and uneven distribution of high-dimensional feature space, then to achieve a more accurate and stable prediction performance. By feature extraction, the measured data can be directly fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm. Better parameters of the estimation model are obtained by introducing elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The comparison is conducted by dataset from National Aeronautics and Space Administration, which shows that the proposed framework has a more accurate and stable prediction performance

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

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions

Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction.

Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-electrochemical thermal coupling modeling method is proposed considering low-temperature performance degradation for the complete characteristic expression of multi-dimensional information. This is to obtain the parameter influence mechanism with a multi-variable coupling relationship. An optimized decoupled deviation strategy is constructed for accurate state of charge prediction with real-time correction of time-varying current and temperature effects. The innovative decoupling method is combined with the functional relationships of state of charge and open-circuit voltage to capture energy management effectively. Then, an adaptive equivalent-prediction model is constructed using the state-space equation and iterative feedback correction, making the proposed model adaptive to fractional calculation. The maximum state of charge estimation errors of the proposed method are 4.57% and 0.223% under the Beijing bus dynamic stress test and dynamic stress test conditions, respectively. The improved multiple feature-electrochemical thermal coupling modeling realizes the effective correction of the current and temperature variations with noise influencing coefficient, and provides an efficient state of charge prediction method adaptive to complex conditions