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Article
Energy & Fuels
Jin Zhao et al.
Summary: In this study, the Hunger Game Search (HGS) and Chaos-HGS (CHGS) optimization algorithms were used to solve the feature selection and hyperparameter optimization problems in lithium-ion battery status prediction and health estimation. By combining CBHGS with GPR, it was found that the selected features can provide state-of-health prediction with higher accuracy and lower cost.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Lei Chen et al.
Summary: A combined estimation method for state of energy (SOE) and predicted maximum available energy, based on a fractional-order composite equivalent circuit model, is proposed to ensure SOE accuracy throughout the battery life cycle. The algorithm utilizes a fixed fractional-order forgetting factor recursive least squares method for parameter identification, an adaptive dual fractional-order extended Kalman filter algorithm for the co-estimation of SOC and SOE, and a fourth-order extended Kalman filter algorithm for the joint estimation of predicted maximum available energy and SOE.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Chen Zhang et al.
Summary: This paper proposes a model for predicting the cycle life of lithium-ion batteries using charging data and discharge curve features. The model combines the features from both charging and discharging processes and uses random forest regression for prediction. Experimental results show that the proposed method outperforms existing algorithms in predicting battery cycle life.
Article
Thermodynamics
Zhelin Huang et al.
Summary: This paper proposes a prediction method based on feature extraction and data-driven models for predicting the capacity of future cycles of lithium-ion batteries to assess their health condition. Experimental results demonstrate precise prediction results.
Article
Energy & Fuels
Yahui Yi et al.
Summary: This study proposes a real-time temperature prediction and degradation pattern analysis method based on digital twin technology and long short-term memory (LSTM) neural network for lithium-ion batteries (LIB). The experimental results demonstrate that the proposed framework delivers acceptable accuracy for CC discharging and dynamic discharging conditions, meeting the requirements of practical applications.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Sahar Khaleghi et al.
Summary: This paper develops a novel PHM methodology for lithium-ion batteries, which includes extracting health indicators and using a recurrent neural network for state estimation, as well as predicting remaining useful life based on a similarity-based model. The results of validation experiments demonstrate that this method can accurately diagnose and predict the health status of batteries regardless of their aging patterns.
Article
Computer Science, Artificial Intelligence
S. Akila et al.
Summary: Attribute selection is crucial in optimization and machine learning, and a multi-objective binary bat algorithm with greedy crossover has been proposed for attribute selection and classification, achieving better performance than existing algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Yu Huang et al.
Summary: This paper proposes a novel sequence-to-sequence predictive model based on a variational autoencoder for predicting future performance progression and remaining useful life of systems. The model, trained with generative adversarial networks, is capable of handling uncertainties and providing probabilistic predictions. Validation using real-world health monitoring data shows significant performance improvement in long-term degradation progress and RUL prediction tasks.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Thermodynamics
Shuzhi Zhang et al.
Summary: In this paper, a comparative study of different adaptive extended Kalman filters (AEKFs)/adaptive unscented Kalman filters (AUKFs) is conducted to achieve more precise and reliable lithium-ion battery state-of-charge (SOC) estimation. A novel multi-objective analysis decision method is proposed to allocate weights for various extracted indexes and further compare the comprehensive estimation performance of different AEKFs/AUKFs. The results provide suggestions and guidance for researchers when choosing AEKFs/AUKFs for online SOC estimation.
Article
Energy & Fuels
Jingjin Wu et al.
Summary: This study proposes a multi-scale fractional-order dual unscented Kalman filter to improve the accuracy of SOC estimation for lithium-ion batteries. The characteristics of lithium-ion batteries are represented using a fractional-order model based on the fractional calculus theory, and the parameters are identified using an adaptive genetic algorithm. The fractional-order dual unscented Kalman filter is employed to jointly estimate the battery parameters and SOC. Experimental results verify the effectiveness and accuracy of the proposed method.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Ujjval B. Vyas et al.
Summary: This article presents a new method for representing the open circuit voltage (OCV) of a battery using a three-dimensional polynomial equation through optimization-based regression analysis. The proposed method requires less memory and reduces computational complexity compared to traditional methods.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Yun Zhu et al.
Summary: This paper proposes a novel evaluation model based on the performance degradation Wiener process model, verifies the effectiveness and accuracy of the model through experiments, and achieves real-time reliability evaluation of lithium-ion batteries using Bayesian updating algorithm.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Industrial
Xing Shu et al.
Summary: In this paper, a fused capacity estimation method is proposed by cooperation of multiple machine learning algorithms, and its accuracy and robustness are demonstrated in experiments.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Hardware & Architecture
Prerna Sharma et al.
Summary: This paper proposes an Enhanced Binary Bat algorithm (EBBA) for feature selection and classification of cardiotocography dataset in the multi-classification problem, which can efficiently and accurately assess the hypoxic condition of fetuses, achieving a high classification accuracy.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Automation & Control Systems
Kailong Liu et al.
Summary: This paper provides a systematic review of recent advancements in electrochemical model development and parameterization for battery management systems. It summarizes and analyzes classic pseudo-two-dimensional models and related model order reduction methodologies, as well as enhanced models considering cell internal inhomogeneity. The paper also discusses parameter identification techniques and solutions for optimizing the parameterization procedure, and highlights current research gaps and challenges.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Energy & Fuels
Xiaodong Xu et al.
Summary: This paper presents a novel joint state estimation method for lithium-ion batteries based on a hybrid model. The method accurately estimates the state of charge, state of health, state of power, and state of energy, and it has been verified to have high accuracy and strong robustness through experiments.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Zhenjie Cui et al.
Summary: Accurate estimation of SOC is crucial for the safe operation of Li-ion batteries. This paper proposes a method combining Thevenin equivalent circuit model and extended Kalman filtering algorithm, which achieves better performance and higher accuracy in Gaussian noise environments.
Article
Energy & Fuels
Lili Xia et al.
Summary: Accurate prediction of remaining mileage for electric vehicles is still a challenge. This paper proposes a novel fusion equivalent-circuit model of lithium-ion batteries considering the influence of temperature to obtain a high precision mathematical description and state parameters. An adaptive noise correction-dual extended Kalman filtering algorithm is adopted for the estimation of state-of-energy and state-of-charge, solving the noise influence of Kalman filtering. Experimental results show that the proposed method achieves estimation errors within 1.83% and 1.92% at different working temperatures and conditions, proving the efficiency of the co-estimation method.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Energy & Fuels
M. Hossain et al.
Summary: This paper presents a comprehensive review of different techniques for state of charge (SoC) estimation of batteries and Li-ion battery model parameter estimation methods. The study classifies Kalman filters (KFs) and conducts a literature review on linear and non-linear Kalman filters (LKFs and NLKFs). The NLKFs, such as EKF, AEKF, UKF, and AUKF, are found to be the most extensively established techniques for accurate and reliable SoC estimation. However, accurate battery modeling and online model parameter estimation are crucial for precise estimation using Kalman filters.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Chemistry, Physical
Ruihe Li et al.
Summary: This paper systematically reviews the electrochemical-mechanical coupled behaviors in lithium-ion battery cells and proposes a logic model of "mechanical origins - structural changes - electrochemical changes - performance". By introducing the mechanical origins, structural changes, and electrochemical changes, the paper provides useful guidelines for the design and manufacture of lithium-based rechargeable batteries and promotes the development of the electric vehicle industry.
ENERGY STORAGE MATERIALS
(2022)
Article
Energy & Fuels
Mina Ma et al.
Summary: This study proposes a multi-fault diagnosis strategy focusing on detecting and isolating different types of faults in lithium-ion batteries. By using principal component analysis (PCA) and parallel kernel principal component analysis (KPCA), the method accurately detects faults and reconstructs fault waveforms to improve fault diagnosis reliability, as verified by tested data.
Article
Thermodynamics
Zhenhua Cui et al.
Summary: This paper proposes a hybrid method to achieve stable and real-time estimation of State of Charge (SOC) for lithium-ion batteries. The method can adapt to various working conditions and has good estimation accuracy and robustness.
Article
Thermodynamics
Shunli Wang et al.
Summary: In this paper, an improved feedforward-long short-term memory (FF-LSTM) modeling method is proposed for accurate whole-life-cycle state of charge (SOC) prediction of lithium-ion batteries. The method effectively considers the variations in current, voltage, and temperature. Experimental tests show that the battery capacity reduces significantly over time.
Article
Energy & Fuels
Michael Schmid et al.
Summary: Safe operation of large battery storage systems requires advanced fault diagnosis. This study addresses the early detection of incipient internal short circuits using a nonlinear data model based on Kernel Principal Component Analysis. Experimental validation shows accurate detection in a wide range of short-circuit resistances.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Energy & Fuels
Aryan Shah et al.
Summary: The rapid adoption of li-ion battery-fueled vehicles is a promising approach to mitigate the impact of climate change. However, ensuring the reliability and consistency of the battery management system in these vehicles is a complex task. This paper conducts a comparative study of various methods for estimating the state of charge of lithium-ion batteries, including adaptive, data-driven, and hybrid approaches. The study finds that hybrid techniques incorporating machine learning algorithms yield good results, improving battery prognostics accuracy.
RENEWABLE ENERGY FOCUS
(2022)
Article
Engineering, Electrical & Electronic
Michael Schmid et al.
Summary: Fault diagnosis in Battery Management Systems is crucial to prevent catastrophic consequences such as thermal runaway of battery cells. This study presents a novel data-driven approach based on comparing single cell voltages to detect faults and localize them using Principal Component Analysis. The method shows sensitivity and robustness in detecting abnormalities even under dynamic load profiles and sensor noise, demonstrating fault detection and isolation capabilities in a large battery system.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Physical
Kodjo S. R. Mawonou et al.
Summary: This paper investigates the critical health assessment of Lithium-ion batteries and proposes two new aging indicators to improve current solutions, as well as introducing a data-driven battery aging prediction method using the random forest algorithm. The study shows that these methods can generate reliable health status evaluation and high-precision aging prediction.
JOURNAL OF POWER SOURCES
(2021)
Article
Chemistry, Physical
Quan Xia et al.
Summary: A novel reliability optimization method based on multiphysics coupling simulation and response surface methodology is proposed to optimize the redundancy and layout design of battery packs accurately and efficiently. By applying this method, the system reliability can be significantly improved through precise and efficient design optimization of battery pack redundancy and layout.
JOURNAL OF POWER SOURCES
(2021)
Article
Chemistry, Physical
Shichun Yang et al.
Summary: This study establishes a parallel-module model for lithium-ion batteries and proposes a charging strategy based on minimum Li plating overpotential control (MLPOC) to reduce capacity loss. The research also investigates the sensitivity of resistance ratio between branch and interconnect resistances on module performance. Results show that a medium resistance ratio of 20-30 is recommended for optimal module performance.
JOURNAL OF POWER SOURCES
(2021)
Review
Engineering, Environmental
Bo Sun et al.
Summary: This paper introduces a new approach that combines the PoF method and copula Bayesian network to improve the reliability analysis of complex electronic systems. The method entails qualitative analysis, forward inference, and backward inference to analyze key subsystems and devices, and calculate failure life distributions.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Energy & Fuels
Yixin Yang
Summary: This paper focuses on accurate prediction of battery cycle life and remaining useful life for lithium-ion batteries, utilizing various data curves and a hybrid convolutional neural network for modeling prediction, while also introducing feature attention algorithm and multiscale cycle attention algorithm to enhance prediction performance.
Review
Green & Sustainable Science & Technology
Xin Lai et al.
Summary: This article reviews the current status and challenges of echelon utilization for retired lithium batteries, comprehensively summarizes the standards, policies, technologies, and environmental values of echelon utilization, and provides valuable solutions to address technical issues.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Energy & Fuels
Talal Mouais et al.
Summary: This paper explores three-lifetime models for commercial Lithium-Ion Batteries and finds that the lognormal distribution is the best among the three suggested models after conducting a comparative study on censored data from an accelerated lifetime test.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Guoqi Zhao et al.
Summary: This paper presents an estimation method of SOC and SOH of lithium-ion battery based on ultrasonic guided wave technology, which comprehensively evaluates the battery's state with multiple parameters and guided-wave parameters. The experiment shows that guided wave parameters have consistent correspondence with SOC/SOH, and the analysis of signal amplitude can effectively reflect the phase transitions in the charge-discharge cycle and aging process. The sensitivity of ultrasonic guided wave technology to estimation of SOC/SOH gradually decreases with battery aging.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Computer Science, Artificial Intelligence
Xiaopeng Tang et al.
Summary: By combining industrial data with accelerated aging tests, high-quality battery aging datasets can be recovered to improve the assessment of battery aging with up to 90% experimental time saved.
Article
Computer Science, Interdisciplinary Applications
Marcus Johnen et al.
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(2020)
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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
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