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Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (10 August 2020) | Viewed by 16917

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Special Issue Editor

Prof. Dr. Woojin Choi

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Guest Editor

Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea
Interests: high power battery charger for EV; battery management system; estimation for state-of-charge (SOC) and state-of-health (SOH) of the battery; study on future fuel cell and electric vehicle; fuel cell system’s balance-of-plant; design and control of the power converter (DC/DC converter and DC/AC inverter); grid-connected and distributed power using renewable energy; modeling and application of electrochemical energy source (fuel cell, supercapacitor, battery, etc.); diagnosis of electric apparatuses and electrochemical energy devices
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Special Issue Information

Dear Colleagues,

Battery aging and life prediction have become a challenge and research hotspot in many application areas, such as electric vehicles, energy storage systems and portable electronics. Hence, their degradation identification, state estimation, and prediction of remaining useful life have become a focus of attention to avoid its premature failure and improve system reliability. An advanced battery management system which can accurately monitor the battery degradation process and predict life is essential for the automated and optimized scheduling of the maintenance which, in turn, ensure the safe operation and extended life of batteries.

This Special Issue highlights research at the forefront of this field, inviting contributions (either research, perspective or review articles) addressing battery modeling and aging mechanism, anti-aging operation methodologies, life span and remaining useful life prediction, diagnosis and prognosis, accelerated life testing and data analysis, optimal battery management strategies, and application of artificial intelligence. Further, authors are encouraged to submit papers addressing the state-of-the-art and recent advancements in the areas, providing useful guidelines for future research directions.

Potential topics include but are not limited to:

Modeling of the batteries for aging and life prediction;
Anti-aging operation strategy;
Calendar life and remaining useful life prediction;
Diagnosis and prognosis of the failure;
Accelerated life testing and data analysis;
Optimal battery management strategies;
Online estimation for state of charge, state of health, and state of function;
Application of artificial intelligence.

Prof. Woojin Choi
Guest Editor

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Keywords

battery modeling
aging mechanism
remaining useful life prediction
diagnosis and prognosis
accelerated life testing
battery management system
online estimation
state-of-charge
state-of-health
state-of-function
artificial intelligence

Published Papers (5 papers)

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Research

15 pages, 1655 KiB

Open AccessArticle

An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

by Thanh-Tung Nguyen, Abdul Basit Khan, Younghwi Ko and Woojin Choi

Energies 2020, 13(17), 4536; https://doi.org/10.3390/en13174536 - 01 Sep 2020

Cited by 9 | Viewed by 2279

Abstract

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods. Full article

(This article belongs to the Special Issue Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics)

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20 pages, 5001 KiB

Open AccessArticle

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

by Jinhyeong Park, Munsu Lee, Gunwoo Kim, Seongyun Park and Jonghoon Kim

Energies 2020, 13(9), 2138; https://doi.org/10.3390/en13092138 - 29 Apr 2020

Cited by 37 | Viewed by 3806

Abstract

To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively. Full article

(This article belongs to the Special Issue Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics)

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17 pages, 4396 KiB

Open AccessArticle

Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

by Kaizhi Liang, Zhaosheng Zhang, Peng Liu, Zhenpo Wang and Shangfeng Jiang

Energies 2019, 12(24), 4772; https://doi.org/10.3390/en12244772 - 13 Dec 2019

Cited by 22 | Viewed by 3161

Abstract

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs. Full article

(This article belongs to the Special Issue Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics)

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12 pages, 4382 KiB

Open AccessArticle

A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

by Yuan Chen, Yigang He, Zhong Li and Liping Chen

Energies 2019, 12(22), 4376; https://doi.org/10.3390/en12224376 - 17 Nov 2019

Cited by 6 | Viewed by 2564

Abstract

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%. Full article

(This article belongs to the Special Issue Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics)

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14 pages, 3262 KiB

Open AccessArticle

Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features

by Muhammad Umair Ali, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Gwan-Soo Park and Hee-Je Kim

Energies 2019, 12(22), 4366; https://doi.org/10.3390/en12224366 - 15 Nov 2019

Cited by 34 | Viewed by 4651

Abstract

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles. Full article

(This article belongs to the Special Issue Battery Aging and Life Prediction for Electric Vehicles, Energy Storage Systems and Portable Electronics)

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