Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter

2020 ◽  
pp. 014233122096157
Author(s):  
Shuai Wang ◽  
Wei Han ◽  
Lifei Chen ◽  
Xiaochen Zhang ◽  
Michael Pecht
Keyword(s):  
Particle Filter ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Li Ion Batteries ◽  
Model Particle ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion

A new data-driven prognostic method based on an interacting multiple model particle filter (IMMPF) is proposed for use in the determination of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the uncertainty associated with the RUL. An IMMPF is applied to different state equations. The battery capacity degradation model is very important in the prediction of the RUL of Li-ion batteries. The IMMPF method is applied to the estimation of the RUL of Li-ion batteries using the three improved models. Three case studies are provided to validate the proposed method. The experimental results show that the one-dimensional state equation particle filter (PF) is more suitable for estimating the trend of battery capacity in the long term. The proposed method involving interacting multiple models demonstrated a stable and high prediction accuracy, as well as the capability to narrow the uncertainty in the PDF of the RUL prediction for Li-ion batteries.

Capacity Loss Estimation For Li-Ion Batteries Based On A Semi-Empirical Model

2021 ◽  
Author(s):  
Mohammed Rabah ◽  
Eero Immonen ◽  
Sajad Shahsavari ◽  
Mohammad-Hashem Haghbayan ◽  
Kirill Murashko ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Empirical Model ◽  
Lithium Ion ◽  
Average Error ◽  
Li Ion Batteries ◽  
Capacity Loss ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion ◽  
Semi Empirical

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

Interacting multiple model particle filter for prognostics of lithium-ion batteries

2017 ◽  
Vol 70 ◽  
pp. 59-69 ◽  
Author(s):  
Xiaohong Su ◽  
Shuai Wang ◽  
Michael Pecht ◽  
Lingling Zhao ◽  
Zhe Ye
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Particle

scholarly journals Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ye Tian ◽  
Chen Lu ◽  
Zili Wang ◽  
Laifa Tao
Keyword(s):  
Particle Filter ◽  
Lithium Ion ◽  
Integrated Approach ◽  
Remaining Useful Life ◽  
Battery Life ◽  
Li Ion Batteries ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Li Ion ◽  
Swarm Algorithm

An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries based on artificial fish swarm algorithm (AFSA) and particle filter (PF), which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

scholarly journals A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3678 ◽  
Author(s):  
Tianfei Sun ◽  
Bizhong Xia ◽  
Yifan Liu ◽  
Yongzhi Lai ◽  
Weiwei Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Model Based ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life ◽  
Learning Machine

The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine (ELM). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM, the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM, the PFMLP, and the BATPFNN under the same conditions.

scholarly journals Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 13333
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Network ◽  
Single Channel ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Channel Input ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

scholarly journals Charge and Discharge Behaviour of Li-Ion Batteries at Various Temperatures Containing LiCoO2 Nanostructured Cathode Produced by CCSO

2015 ◽  
Vol 15 (4) ◽  
pp. 301 ◽  
Author(s):  
Y.Y. Mamyrbayeva ◽  
R.E. Beissenov ◽  
M.A. Hobosyan ◽  
S.E. Kumekov ◽  
K.S. Martirosyan
Keyword(s):  
Lithium Ion Battery ◽  
Active Material ◽  
Lithium Ion ◽  
Synthetic Approach ◽  
Capacity Fade ◽  
Li Ion Batteries ◽  
Material Loss ◽  
Method Performance ◽  
Battery Capacity ◽  
Li Ion

<p>There are technical barriers for penetration market requesting rechargeable lithium-ion battery packs for portable devices that operate in extreme hot and cold environments. Many portable electronics are used in very cold (-40 °C) environments, and many medical devices need batteries that operate at high temperatures. Conventional Li-ion batteries start to suffer as the temperature drops below 0 °C and the internal impedance of the battery  increases. Battery capacity also reduced during the higher/lower temperatures. The present work describes the laboratory made lithium ion battery behaviour features at different operation temperatures. The pouch-type battery was prepared by exploiting LiCoO<sub>2</sub> cathode material synthesized by novel synthetic approach referred as Carbon Combustion Synthesis of Oxides (CCSO). The main goal of this paper focuses on evaluation of the efficiency of positive electrode produced by CCSO method. Performance studies of battery showed that the capacity fade of pouch type battery increases with increase in temperature. The experimental results demonstrate the dramatic effects on cell self-heating upon electrochemical performance. The study involves an extensive analysis of discharge and charge characteristics of battery at each temperature following 30 cycles. After 10 cycles, the battery cycled at RT and 45 °C showed, the capacity fade of 20% and 25% respectively. The discharge capacity for the battery cycled at 25 °C was found to be higher when compared with the battery cycled at 0 °C and 45 °C. The capacity of the battery also decreases when cycling at low temperatures. It was important time to charge the battery was only 2.5 hours to obtain identical nominal capacity under the charging protocol. The decrease capability of battery cycled at high temperature can be explained with secondary active material loss dominating the other losses.</p>

scholarly journals Derating Guidelines for Lithium-Ion Batteries

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3295 ◽  
Author(s):  
Yongquan Sun ◽  
Saurabh Saxena ◽  
Michael Pecht
Keyword(s):  
Lithium Ion ◽  
Stress Factors ◽  
Li Ion Batteries ◽  
Degradation Data ◽  
Capacity Loss ◽  
Battery Capacity ◽  
Operational Life ◽  
Battery Degradation ◽  
Li Ion

Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been reviewed to demonstrate the role of stress factors in battery degradation and derating for two widely used Li-ion batteries: graphite/LiCoO2 (LCO) and graphite/LiFePO4 (LFP). Derating factors have been computed based on the battery capacity loss to quantitatively evaluate the derating effects of the stress factors and identify the significant factors for battery derating.

Diagnostics and Prognostics of Lithium-Ion Batteries

2015 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Cheol Lee
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Data Driven ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Battery State Of Charge ◽  
Li Ion ◽  
Useful Life

This paper investigates recent research on battery diagnostics and prognostics especially for Lithium-ion (Li-ion) batteries. Battery diagnostics focuses on battery models and diagnosis algorithms for battery state of charge (SOC) and state of health (SOH) estimation. Battery prognostics elaborates data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH. Readers will learn not only basics but also very recent research developments on battery diagnostics and prognostics.

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