Degradation mechanism models RUL battery
208630361
A Data driven RUL Prediction Method Enhanced by Identified Degradation Model for Lithium ion Battery of EVs
A reliable and high fidelity of the battery remaining useful life (RUL) prediction is dependent on a large amount of high qualified labeled data, which is a difficult and tedious work in practical implementation. To ease the training process and improve the prediction accuracy under limited labeled training data, this paper proposes a data driven RUL prediction method enhanced by the identified mechanism degradation model. With the Frisch Scheme based Bias Compensating Recursive Least Squares (FBCRLS) algorithm, the battery mechanism aging model parameters are identified under limited labeled data. The model is then used to generate labeled data in a full life cycle, which is used to enhance the training of the Support Vector Regression (SVR) model. Test results reveal that the proposed method can effectively reduce the requirement of measured data and ensure high prediction accuracy even under very limited training data.
2019 IEEE Energy Conversion Congress and Exposition (ECCE)
Jun Peng Min-Hsi Wu Dianzhu Gao Xiaoyong Zhang Yijun Cheng Zhiyong Zheng B Chen Fu Jiang Zhiwu Huang
208281149
A Novel Health Index for Battery RUL Degradation Modeling and Prognostics
Lithium ion batteriesare widely used in our daily life. However, with the frequent use of lithium battery, the performance of lithium battery decreases due to the change of internal physical properties. The most intuitive result is that the capacity gradually decreases with the use of the battery. Therefore, timely and effective prediction of lithium ion battery remaining useful life (RUL) is particularly important. In this paper, two new health indexes (HI) namely, discharging time difference of equal voltage interval (DtD_EVI) and discharging temperature difference of equal time interval (DTD_EtI) are proposed to represent the degradation process of lithium battery. Pearson correlation coefficient is used to analyze the relationship between these two health indexes and capacity, and then support vector regression (SVR) is used to establish the RUL regression model. Finally, the validity of the proposed method is verified by analyzing the lithium battery dataset of NASA.
2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)
Qiuhui Ma Weidong Yang Bo Tao Ying Zheng
195257849
A Lithium ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon
Prediction of Remaining Useful Life (RUL) of lithium ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium ion battery is proposed. Firstly, the multi scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.
Energies
Xiaoqiong Pang Rui Huang Jie Wen Yuanhao Shi Jianfang Jia Jianchao Zeng
188236125
Centralised battery flexibility assessment for imbalance management in Spain considering lithium ion degradation mechanism
The rise of Distributed Energy Resources (DER) cause more uncertainty for the Balance Responsible Party (BRP) with regards to precise forecasting of supply and demand. In case of an imbalance, ancil lary services have to be activated resulting in monetary deviation penalties for the respective BRP. The implementation of reliable communication technology enables the use of aggregated flexibility, utilising smart industrial and residential applications, heating systems, electric vehicles, distributed generation and energy storage. This work analyses the use case of centralised Lithium Ion battery providing flexibility for BRPs. The analysis was conducted using Spanish electricity market data and real imbalance data from the year 2017. The model developed in this work integrates battery cycle aging using a piecewise linear cost function. This approach provides a close approximation of the battery degradation mechanism of electrochemical batteries and can be incorporated easily into existing market dispatch programs with short time window. Having battery degradation implemented in the decision making reveals a more considerate operation of the battery, which prolongs the battery's lifetime and improves the project's profitability. Moreover, the analysis revealed that the Lithium Ion Titanate (LTO) chemistry performs the best from an operational and project based perspective. Eventually, the business case of usin ng centralised battery flexibility for deviation management using the example of Spain was assessed and concluded to be insufficient to reach break even for the investment
Leon Haupt
135445087
Data driven prediction of battery cycle life before capacity degradation
Accurately predicting the lifetime of complex, nonlinear systems such as lithium ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data driven modelling to predict the behaviour of complex dynamical systems.Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine learning method to predict battery life before the onset of capacity degradation with high accuracy.
Nature Energy
Kristen A Severson Peter M Attia Norman Jin Nicholas Perkins Benben Jiang Zi Yang Michael H Chen Muratahan Aykol Patrick K Herring Dimitrios Fraggedakis Martin Z Bazant Stephen Harris William C Chueh Richard D Braatz
139947266
Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data
Reliability and remaining useful life (RUL) estimation for a satellite rechargeable lithium battery (RLB) are significant for prognostic and health management (PHM) A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data, including bivariate degradation data and lifetime data. Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system. First, linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage. Next, the Bayesian method, in combination with Markov Chain Monte Carlo (MCMC) simulations, is provided to integrate limited bivariate degradation data with other congeneric RLBs' lifetime data. Then reliability evaluation and RUL prediction are carried out for PHM. A simulation study demonstrates that due to the data fusion, parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes. Finally, a practical case study of a satellite RLB verifies the usability of the model.Zhai Yao Wei Xing Xu Dian Chi Sheng Yu Sheng Yu Shou Ming Yu Ce Shi Wei Xing Xi Tong Gu Zhang Zhen Duan Yu Jian Kang Guan Li De Zhong Yao Yi Huan Ben Wen Chuang Xin Xing De Ti Chu Liao Yi Ge Rong He Er Yuan Xing Neng Tui Hua Shu Ju He Shou Ming Shu Ju De Ke Kao Xing Fen Xi Bei Xie Si Mo Xing Dui Wei Xing Li Dian Chi De Sheng Yu Shou Ming Jin Xing Yu Ce Er Yuan Xing Neng Tui Hua Shi Zhi Xi Tong Cun Zai Liang Ge Xiang Guan De Xing Neng Tui Hua Xian Xiang Er Zhe De Gong Tong Zuo Yong Xia Ke Neng Dao Zhi Xi Tong Gu Zhang Mo Xing Zhong Shou Xian Li Yong Copula Han Shu He Xian Xing Wei Na Guo Cheng Dui Li Dian Chi De Er Yuan Xing Neng Tui Hua Shu Ju Jin Xing Jian Mo Er Hou Li Yong Bei Xie Si Mo Xing Rong He Xiao Zi Yang Shou Ming Shu Ju Cai Yong Ma Er Ke Fu Meng Te Qia Luo Fang Zhen Gu Ji Mo Xing Can Shu Cong Er Dui Sheng Yu Shou Ming Jin Xing Yu Ce Bing Tong Guo Fang Zhen He Shi Li Fen Xi Dui Suo Ti Mo Xing De Xing Neng Jin Xing Fen Xi Jie Guo Biao Ming Rong He Liao Er Yuan Xing Neng Tui Hua Shu Ju He Shou Ming Shu Ju Hou Sheng Yu Shou Ming De Yu Ce Jing Du Neng Gou You Xiao Ti Gao
Yang Zhang Xiang Jia Bo Guo
164935862
The effect of cell to cell variations and thermal gradients on the performance and degradation of lithium ion battery packs
The performance of lithium ion battery packs are often extrapolated from single cell performance however uneven currents in parallel strings due to cell to cell variations, thermal gradients and/or cell interconnects can reduce the overall performance of a large scale lithium ion battery pack. In this work, we investigate the performance implications caused by these factors by simulating six parallel connected batteries based on a thermally coupled single particle model with the solid electrolyte interphase growth degradation mechanism modelled. Experimentally validated simulations show that cells closest to the load points of a pack experience higher currents than cells further away due to uneven overpotentials caused by the interconnects. When a cell with a four times greater internal impedance was placed in the location with the higher currents this actually helped to equalise the cell to cell current distribution, however if this was placed at a location furthest from the load point this would cause a ~6% reduction in accessible energy at 1.5 C. The influence of thermal gradients can further affect this current heterogeneity leading to accelerated aging. Simulations show that in all cases, cells degrade at different rates in a pack due to the uneven currents, with this being amplified by thermal gradients. In the presented work a 5.2% increase in degradation rate, from 7.71 mWh/cycle (isothermal) to 8.11 mWh/cycle (non isothermal) can be observed. Therefore, the insights from this paper highlight the highly coupled nature of battery pack performance and can inform designs for higher performance and longer lasting battery packs.
Applied Energy
Xinhua Liu Weilong Ai Max Naylor Marlow Yatish Patel Billy Wu
148574526
An Empirical Capacity Degradation Modeling and Prognostics of Remaining Useful Life of Li ion Battery using Unscented Kalman Filter
In battery power, electric vehicle lifetime and the cost of the battery are the primary concern and challenge in the fast evolving and promising uptake of an electric vehicle. Battery aging leads to gradually deteriorates in battery performance hence identification of battery states and health is essential. Battery health identification helps the customer to maintain and replace batteries in advance to prevent the loss caused by the unexpected failure of these batteries and help in reducing maintenance cost. This paper presents and compares two empirical models to describe battery degradation behaviors over its lifespan. Validation of the accuracy of the empirical model is performed by using experimental life cycle test data. Subsequently, the most accurate model has been used in an Unscented Kalman Filter to predict battery remaining useful life (RUL) This work provides the initial step towards the development of battery health management system.
2018 8th IEEE India International Conference on Power Electronics (IICPE)
Venu Sangwan Rajesh Kumar Akshay Kumar Rathore
115599841
Degradation model and cycle life prediction for lithium ion battery used in hybrid energy storage system
Abstract Lithium ion battery/ultracapacitor hybrid energy storage system is capable of extending the cycle life and power capability of battery, which has attracted growing attention. To fulfill the goal of long cycle life, accurate assessment for degradation of lithium ion battery is necessary in hybrid energy management. This paper proposes an improved degradation model of lithium ion battery based on the electrochemical mechanism of capacity fade, in which the influence of cycling current is taken into consideration. Moreover, genetic algorithm is applied to identify the initial values of model parameters. A particle filter based data driven framework is also designed to track the variation of model parameters and states during the cycling process. Short term and long term degradation prediction methods are then developed, to forecast the essential indicators of battery health. Datasets of battery cycling tests under both constant cycling current and dynamic cycling current are used for verification. The root mean square error results for state of health prediction (less than 18 cycles) and remaining useful life prediction are below 8% and 40 cycles, respectively, showing the accuracy and applicability of the proposed methods. Additionally, the baseline degradation prediction for case with cycling current information unknown is also illustrated.
Chang Liu Yujie Wang Zonghai Chen
11423211
Prognostics of Li(NiMnCo)O2 based lithium ion batteries using a novel battery degradation model
Abstract Some lithium ion battery materials show two phase degradation behavior with evident inflection points, such as lithium nickel manganese cobalt oxide (Li(NiMnCo)O 2 or NMC) cells. A model based Bayesian approach is proposed in this paper to predict remaining useful life (RUL) for these types of batteries. First, a two term logarithmic model is developed to capture the degradation trends of NMC batteries. By fitting the battery degradation data, it is experimentally demonstrated that the developed model is superior to existing empirical battery degradation models. A particle filtering based prognostic method is then incorporated into the model to estimate the batteries' possible degradation trajectories. Correspondingly, the RUL values of NMC batteries are expressed in terms of probability density function. The effectiveness of the developed method is verified with our collected experimental data. The results indicate that the proposed prognostic method can achieve higher predictive accuracy than the existing two term exponential model.
Microelectron. Reliab.
Fangfang Yang Dong Wang Yinjiao Xing Kwok-Leung Tsui