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

Possibilities to identify defective electric automobile batteries

ArticleA pack of batteries is one of the most important and expensive assemblies for an electric vehicle. A pack of batteries is comprised of several batteries connected in series. The number of the batteries connected depends on the operating voltage of the vehicle’s on-board system as well as on the individual characteristics of the batteries used, e.g. the operating voltage of a single cell. One or several cells of a pack of batteries could be damaged if improperly exploiting an electric vehicle– excessively discharging the batteries or overloading the electric vehicle. If a self-converted vehicle does not use an intellectual BMS (battery management system) that can identify and register voltage drop for any individual cell in the high-load regime, e.g. when accelerating, it is difficult to identify and change the cells damaged. In case a cell does not demonstrate a complete failure, it is almost impossible to identify a defect in any regime other than the load regime. The research developed and compared three different methods for identifying defective battery cells. The methods were approbated on a converted Renault Clio. The experiment involved making voltage measurements in road tests, running the electric vehicle on a roll test bench and making voltage measurements of maximally discharged batteries in the no-load regime. A comparison of the measurement results revealed that the measurements made in the road tests were the most accurate and useful. After the experiment, the defective battery cells were replaced, thereby restoring the performance of the battery pack

ZeChipC: Time Series Interpolation Method Based on Lebesgue Sampling

In this paper, we present an interpolation method based on Lebesgue sampling that could help to develop systems based time series more efficiently. Our methods can transmit times series, frequently used in health monitoring, with the same level of accuracy but using much fewer data. Our method is based in Lebesgue sampling, which collects information depending on the values of the signal (e.g. the signal output is sampled when it crosses specific limits). Lebesgue sampling contains additional information about the shape of the signal in-between two sampled points. Using this information would allow generating an interpolated signal closer to the original one. In our contribution, we propose a novel time-series interpolation method designed explicitly for Lebesgue sampling called ZeChipC. ZeChipC is a combination of Zero-order hold and Piecewise Cubic Hermite Interpolating Polynomial(PCHIP) interpolation. ZeChipC includes new functionality to adapt the reconstructed signal to concave/convex regions. The proposed methods have been compared with state-of-the-art interpolation methods using Lebesgue sampling and have offered higher average performance

Systematic approach for the test data generation and validation of ISC/ ESC detection methods

Various methods published in recent years for reliable detection of battery faults (mainly internal short circuit (ISC)) raise the question of comparability and cross-method evaluation, which cannot yet be answered due to significant differences in training data and boundary conditions. This paper provides a Monte Carlo-like simulation approach to generate a reproducible, comprehensible and large dataset based on an extensive literature background on common assumptions and simulation parameters. In some cases, these assumptions are quite different from field data, as shown by comparison with experimentally determined values. Two relatively simple ISC detection methods are tested on the generated dataset and their performance is evaluated to illustrate the proposed approach. The evaluation of the detection performance by quantitative measures such as the Youden-index shows a high divergence with respect to internal and external parameters such as threshold level and cell-to-cell variations (CtCV), respectively. These results underline the importance of quantitative evaluations based on identical test data. The proposed approach is able to support this task by providing cost-effective test data generation with incorporation of known factors affecting detection quality

Systematic approach for the test data generation and validation of ISC/ESC detection methods

Various methods published in recent years for reliable detection of battery faults (mainly internal short circuit (ISC)) raise the question of comparability and cross-method evaluation, which cannot yet be answered due to significant differences in training data and boundary conditions. This paper provides a Monte Carlo-like simulation approach to generate a reproducible, comprehensible and large dataset based on an extensive literature search on common assumptions and simulation parameters. In some cases, these assumptions are quite different from field data, as shown by comparison with experimentally determined values. Two relatively simple ISC detection methods are tested on the generated dataset and their performance is evaluated to illustrate the proposed approach. The evaluation of the detection performance by quantitative measures such as the Youden-index shows a high divergence with respect to internal and external parameters such as threshold level and cell-to-cell variations (CtCV), respectively. These results underline the importance of quantitative evaluations based on identical test data. The proposed approach is able to support this task by providing cost-effective test data generation with incorporation of known factors affecting detection quality

A data-driven health assessment method for electromechanical actuation systems

The design of health assessment applications for the electromechanical actuation system of the aircraft is a challenging task. Physics-of-failure models involve non-linear complex equations which are further complicated at the system-level. Data-driven techniques require run-to-failure tests to predict the remaining useful life. However, components are not allowed to run until failure in the aerospace engineering arena. Besides, when adding new monitoring elements for an improved health assessment, the airliner sets constraints due to the increased cost and weight. In this context, the health assessment of the electromechanical actuation system is a challenging task. In this paper we propose a data-driven approach which estimates the health state of the system without run-to-failure data and limited health information. The approach combines basic reliability theory with Bayesian concepts and obtained results show the feasibility of the technique for asset health assessment

Interpretable Battery Lifetime Prediction Using Early Degradation Data

Battery lifetime prediction using early degradation data is crucial for optimizing the lifecycle management of batteries from cradle to grave, one example is the management of an increasing number of batteries at the end of their first lives at lower economic and technical risk.In this thesis, we first introduce quantile regression forests (QRF) model to provide both cycle life point prediction and range prediction with uncertainty quantified as the width of the prediction interval. Then two model-agnostic methods are employed to interpret the learned QRF model. Additionally, a machine learning pipeline is proposed to produce the best model among commonly-used machine learning models reported in the battery literature for battery cycle life early prediction. The experimental results illustrate that the QRF model provides the best range prediction performance using a relatively small lab dataset, thanks to its advantage of not assuming any specific distribution of cycle life. Moreover, the two most important input features are identified and their quantitative effect on predicted cycle life is investigated. Furthermore, a generalized capacity knee identification algorithm is developed to identify capacity knee and capacity knee-onset on the capacity fade curve. The proposed knee identification algorithm successfully identifies both the knee and knee-onset on synthetic degradation data as well as experimental degradation data of two chemistry types.In summary, the learned QRF model can facilitate decision-making under uncertainty by providing more information about cycle life prediction than single point prediction alone, for example, selecting a high-cycle-life fast-charging protocol. The two model-agnostic interpretation methods can be easily applied to other data-driven methods with the aim of identifying important features and revealing the battery degradation process. Lastly, the proposed capacity knee identification algorithm can contribute to a successful second-life battery market from multiple aspects