Article
Multidisciplinary Sciences
Jiangong Zhu, Yixiu Wang, Yuan Huang, R. Bhushan Gopaluni, Yankai Cao, Michael Heere, Martin J. Muhlbauer, Liuda Mereacre, Haifeng Dai, Xinhua Liu, Anatoliy Senyshyn, Xuezhe Wei, Michael Knapp, Helmut Ehrenberg
Summary: Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. Researchers have found that utilizing features from the relaxation voltage curve enables battery capacity estimation without requiring other previous cycling information.
NATURE COMMUNICATIONS
(2022)
Review
Thermodynamics
Yusheng Zheng, Yunhong Che, Xiaosong Hu, Xin Sui, Daniel-Ioan Stroe, Remus Teodorescu
Summary: This paper provides a comprehensive review of temperature estimation techniques in battery systems, discussing potential metrics, different estimation methods, and their strengths and limitations in battery management. The challenges and future opportunities in battery thermal state monitoring are also identified and discussed.
PROGRESS IN ENERGY AND COMBUSTION SCIENCE
(2024)
Article
Energy & Fuels
Fang Liu, Yan-peng Liu, Wei-xing Su, Chang-ping Jiao, Yang Liu
Summary: This study proposed an SOH estimation framework that can automatically correct errors caused by battery consistency, providing accurate estimation of battery health status during electric vehicle charging and discharging. By introducing an equivalent circuit based on the AR model, the complexity of the method was reduced while maintaining estimation accuracy. Comparing with traditional external feature relationship methods, this framework achieves better practicality and higher estimation accuracy in estimating lithium-ion battery SOH during discharge.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Energy & Fuels
Donghui Li, Xu Liu, Ze Cheng
Summary: In order to accurately estimate the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of lithium-ion batteries, this paper proposes a SOC-SOH-RUL co-estimation method using segment data of constant current charge. The method extracts fused health features (FHF) from the constant current charging segment data, and uses Gaussian process regression (GPR) to establish a capacity degradation model for SOH estimation. The equivalent circuit model (ECM) parameters and current SOH are used for SOC estimation, and the FHF prediction model is established for RUL estimation. Experimental results show high accuracy, stability, and applicability of the proposed method.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Shuxin Zhang, Zhitao Liu, Hongye Su
Summary: State of health (SOH) is a critical indicator for lithium-ion battery analysis, but limited data is available for accurate model establishment due to difficulties in data collection. To address this issue, a novel Bayesian deep neural network is proposed and validated on few-shot learning. Degradation patterns extracted from temporal cyclic discharge profiles are utilized for reflecting degradation mode and operation state, while the Gramian angular field is proposed for data distribution learning and information enhancement. Experimental results demonstrate the effectiveness of the proposed method for accurate estimation of lithium-ion battery SOH, irrespective of data size.
Article
Multidisciplinary Sciences
Runlin Wang, Haozhe Zhang, Qiyu Liu, Fu Liu, Xile Han, Xiaoqing Liu, Kaiwei Li, Gaozhi Xiao, Jacques Albert, Xihong Lu, Tuan Guo
Summary: Understanding ion transport kinetics and electrolyte-electrode interactions is crucial for determining the performance and state of health of batteries. However, capturing the details of surface-localized and rapid ion transport at the microscale remains challenging. This study demonstrates a promising approach using an optical fiber plasmonic sensor to monitor the electrochemical kinetics of working batteries without disturbance, providing crucial additional capabilities to battery monitoring methods.
NATURE COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Peihang Xu, Xiaoyi Hu, Benlong Liu, Tiancheng Ouyang, Nan Chen
Summary: This article proposes a hierarchical estimation model considering the current rate, using a fractional-order model for battery modeling, data-driven parameter identification, and a multiscale dual extended Kalman filter for battery states estimation. The experimental results show significant improvement in SOC and SOH estimation compared with traditional methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Electrochemistry
Thomas Puleston, Alejandro Clemente, Ramon Costa-Castello, Maria Serra
Summary: This review provides a comprehensive study of the different types of dynamic models and model-based estimation strategies for redox flow batteries. Due to the complexity of obtaining system internal states directly from experimental measures, many proposals have been developed to rely on easily measurable variables using mathematical models. Finally, the remaining challenges and possible future research directions in this field are discussed.
Article
Chemistry, Physical
Jinpeng Tian, Rui Xiong, Weixiang Shen, Fengchun Sun
Summary: The proposed method in this paper utilizes offline OCV test results to estimate aging diagnosis of lithium ion batteries at an electrode level, achieving fast diagnosis. The estimated aging parameters are close to the results obtained by offline tests, enabling reconstruction of OCV-Q curves for battery capacity estimation with high accuracy. The influence of voltage ranges on estimation results is also discussed in the study.
ENERGY STORAGE MATERIALS
(2021)
Article
Green & Sustainable Science & Technology
Huan Wang, Yan-Fu Li, Ying Zhang
Summary: This study proposes a method for battery health state monitoring based on spiking neural networks and electrochemical impedance spectroscopy, achieving accurate SOH estimation through simulating the feature processing mechanism of brain neurons and low power consumption.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Energy & Fuels
Ruohan Guo, Weixiang Shen
Summary: In this paper, an enhanced multi-constraint (MC) state of power (SOP) estimation algorithm is developed for lithium-ion batteries in electric vehicles (EVs). The algorithm improves accuracy by incorporating a regression-based algorithm and considering battery terminal voltage variation. Simulation and experimental results demonstrate its superiority over conventional algorithms.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Chemistry, Physical
Shida Jiang, Zhengxiang Song
Summary: Batteries, especially lead-acid batteries, play a crucial role in modern society. This review examines various methods for estimating the state of health (SOH) of lead-acid batteries, categorizing them into different classes and analyzing their characteristics, advantages, and limitations in practical applications. Recommended methods for different scenarios are provided, along with unresolved issues to inspire further research in this field.
JOURNAL OF POWER SOURCES
(2022)
Article
Energy & Fuels
Marco Mussi, Luigi Pellegrino, Marcello Restelli, Francesco Trovo
Summary: The paper proposes a novel data-driven optimization methodology for battery SoC estimation, VDB-SE, which provides accurate estimations without knowing battery model parameters. Experimental results show that the method's performance is comparable to state-of-the-art algorithms under various working conditions, with a SoC estimation error of less than 2.1% on a real energy storage system.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Haoxiang Xiang, Yujie Wang, Kaiquan Li, Xingchen Zhang, Zonghai Chen
Summary: This paper performs complete experimental tests on a commercial sodium-ion battery (SIB) and comprehensively compares different methods for state estimation. The results indicate that the unscented Kalman filter and ridge regression are the most suitable algorithms for estimating the state of charge (SOC) and state of health (SOH) of the SIB, respectively.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Automation & Control Systems
Yang Li, Zhongbao Wei, Binyu Xiong, D. Mahinda Vilathgamuwa
Summary: This article proposes a computationally efficient state estimation method for lithium-ion batteries based on a degradation-conscious high-fidelity electrochemical-thermal model. The algorithm uses an ensemble-based state estimator with the singular evolutive interpolated Kalman filter (SEIKF) to ease the computational burden caused by the nonlinear nature of the battery model. Unlike existing schemes, the proposed algorithm ensures mass conservation without additional constraints, simplifying the tuning process and improving convergence speed. The proposed scheme addresses model uncertainty and measurement errors through adaptive adjustment of the SEIKF's error covariance matrices. Comparisons with well-established nonlinear estimation techniques show that the adaptive ensemble-based Li-ion battery state estimator provides excellent performance in terms of accuracy, computational speed, and robustness.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Mohsen Sadeghi, Mohammad Farrokhi
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2019)
Article
Automation & Control Systems
Mohammad-Reza Rahmani, Mohammad Farrokhi
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2019)
Article
Automation & Control Systems
Mohammad-Reza Rahmani, Mohammad Farrokhi
Article
Automation & Control Systems
Surena Rad-Moghadam, Mohammad Farrokhi
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Automation & Control Systems
Reza Heydari, Mohammad Farrokhi
Summary: This paper presents a robust tube-based model predictive control method for polytopic linear parameter varying systems subject to bounded additive disturbances, utilizing the notion of polar dual set and maximizing the shape and size of the additive disturbance set online. Numerical simulation demonstrates the efficacy of the proposed approach.
Article
Computer Science, Artificial Intelligence
Farzaneh Sabbaghian-Bidgoli, Mohammad Farrokhi
Summary: This paper studies sensor and actuator Fault-Tolerant Control (FTC) of nonlinear systems, proposing a Polynomial Fuzzy Unknown Input Observer (PFUIO) for estimating system states and faults. The proposed approach shows better performance compared with the Linear Matrix Inequality (LMI) approach.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Farzaneh Sabbghian-Bidgoli, Mohammad Farrokhi
Summary: This article studies the integrated fault-tolerant control and fault estimation problem using the polynomial fuzzy model based on the sum of squares approach. It designs a polynomial fuzzy observer to estimate time-varying faults and reduces the dimensions of the problem. The proposed approach outperforms the linear matrix inequality approach in fault-tolerant and fault estimation performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Reza Heydari, Mohammad Farrokhi
Summary: This paper addresses the problem of designing robust event-triggered tube-based model predictive control (TMPC) for constrained linear parameter-varying systems. The proposed method introduces a novel modification of homothetic TMPC to consider the effect of open-loop control between triggering times, and derives the triggering condition based on the input-to-state stability (ISS) concept to reduce communication traffic. The simulation results demonstrate the effectiveness of the proposed scheme while reducing the update frequency.
SYSTEMS & CONTROL LETTERS
(2022)
Article
Automation & Control Systems
Farzaneh Sabbaghian-Bidgoli, Mohammad Farrokhi
Summary: This paper proposes a method for robust observer-based Integrated Fault-Tolerant Control (IFTC) using a homogeneous polynomial Lyapunov function (HPLF) in nonlinear systems modeled by the polynomial fuzzy model (PFM). By avoiding the appearance of non-convex terms and solving the control conditions with polynomial matrix inequalities, less conservative control results are obtained. The use of a non-quadratic Lyapunov function in designing a polynomial fuzzy unknown-input observer enables better estimation of system states and actuator faults.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Hamid Toshani, Mohammad Farrokhi
Summary: This paper proposes a strategy combining optimal discrete-time sliding-mode control and recurrent neural networks for a class of uncertain discrete-time linear systems. The dynamic and algebraic model of the neural network is derived based on the optimization conditions of the quadratic problem and their relationship with the projection theory. The convergence of the neural network is analyzed using the Lyapunov stability theory, and a singular value-based analysis is employed for robustness evaluation.
Article
Automation & Control Systems
Surena Rad-Moghadam, Mohammad Farrokhi
Summary: This paper proposes a near-optimal controller design for constrained nonlinear affine systems using a Recurrent Neural Network (RNN) and Extended State Observers (ESOs). The proposed method can handle output constraints by employing the Control Barrier Function (CBF). It aims to achieve a near-optimal performance within the constraints. The effectiveness of the proposed method is illustrated through a simulating example of a two-inverted pendulums system.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Computer Science, Information Systems
Sina Naderian, Mohammad Farrokhi
Summary: This paper proposes an Adaptive Back-stepping Data-Driven Terminal Sliding Mode Controller (ABDTSMC) for non-affine MIMO systems. The proposed controller reduces dependence on the mathematical model and eliminates chattering phenomenon. It utilizes a Disturbance Observer (DOB) based on neural network to estimate uncertainties and disturbances.
Article
Engineering, Multidisciplinary
Melika Ataollahi, Mohammad Farrokhi
Summary: This article proposes an improved Q-learning method and an adaptive artificial potential field method for online path planning in unknown environments with obstacles. The improved Q-learning accelerates the learning process and overcomes local minima, while the adaptive artificial potential field method allows for proper navigation of mobile robots. The proposed method guarantees target tracking, collision avoidance, and optimal path planning.
JOURNAL OF ENGINEERING-JOE
(2023)
Article
Robotics
Shabnam Shakourzadeh, Mohammad Farrokhi
INTELLIGENT SERVICE ROBOTICS
(2020)
Article
Automation & Control Systems
Bahareh Vatankhah, Mohammad Farrokhi
ASIAN JOURNAL OF CONTROL
(2019)