Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation

This study simulates the polarization effect during the process of battery charging and discharging, and investigates the characteristics of the process. A fractional-order model (FOM) is established and the parameters of the FOM are identified with the adaptive genetic algorithm. As Kalman filter estimation causes error accumulation over time, using the fractional-order multi-innovation unscented Kalman filter (FOMIUKF) is a better choice for state of charge (SOC) estimation. A comparative study shows that the FOMIUKF has higher accuracy. A multiple timescales-based joint estimation algorithm of SOC and state of health is established to improve SOC estimation precision and reduce the amount of computation. The FOMIUKF algorithm is used for SOC estimation, while the UKF algorithm is used for SOH estimation. The joint estimation algorithm is then compared and analyzed alongside other Kalman filter algorithms under different dynamic operating conditions. Experimental results show that the joint estimation algorithm possesses high estimation accuracy with a mean absolute error of under 1% and a root mean square error of 1.35%.

[1]  Zhi Li,et al.  Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems , 2022, Applied Energy.

[2]  Xiangbo Cui,et al.  State of Charge Estimation of Lithium-Ion Battery Using Robust Kernel Fuzzy Model and Multi-Innovation UKF Algorithm Under Noise , 2022, IEEE Transactions on Industrial Electronics.

[3]  X. Lai,et al.  Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing , 2022, Energies.

[4]  Yonghong Xu,et al.  Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms , 2022, International Journal of Energy Research.

[5]  Lili Ma,et al.  State of charge estimation of supercapacitors based on multi‐innovation unscented Kalman filter under a wide temperature range , 2022, International Journal of Energy Research.

[6]  Rui Huang,et al.  State of charge estimation of Lithium-ion batteries based on the probabilistic fusion of two kinds of cubature Kalman filters , 2021, Journal of Energy Storage.

[7]  Jiuchun Jiang,et al.  A modified-electrochemical impedance spectroscopy-based multi-time-scale fractional-order model for lithium-ion batteries , 2021 .

[8]  Shuzhi Zhang,et al.  Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures , 2021 .

[9]  Piyush Girade,et al.  Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles , 2021 .

[10]  Chao Sun,et al.  Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection , 2021 .

[11]  Chunhui Wang,et al.  State of health estimation for lithium-ion battery based on the coupling-loop nonlinear autoregressive with exogenous inputs neural network , 2021 .

[12]  Mingyu Gao,et al.  Improved parameter identification and state-of-charge estimation for lithium-ion battery with fixed memory recursive least squares and sigma-point Kalman filter , 2021 .

[13]  H. Ahmad,et al.  Lithium-Ion Battery State of Charge (SoC) Estimation with Non-Electrical parameter using Uniform Fiber Bragg Grating (FBG) , 2021 .

[14]  Hongchun Shu,et al.  Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey , 2021, Journal of Energy Storage.

[15]  Daniel-Ioan Stroe,et al.  Effects of open-circuit voltage tests and models on state-of-charge estimation for batteries in highly variable temperature environments: Study case nano-satellites , 2021 .

[16]  Xiaoli Hao,et al.  Thermodynamic study on power and refrigeration cogeneration Kalina cycle with adjustable refrigeration temperature , 2021 .

[17]  Liang Tong,et al.  Experimental study on small power generation energy storage device based on pneumatic motor and compressed air , 2021 .

[18]  G. Shu,et al.  Applicability analysis of waste heat recovery technology and strategy exploration for hybrid electric vehicles under diverse road conditions , 2021 .

[19]  Hao Mu,et al.  Co-Estimation of State of Charge and Capacity for Lithium-ion Batteries with Multi-Stage Model Fusion Method , 2021 .

[20]  Nabil Derbel,et al.  A real-time estimator for model parameters and state of charge of lead acid batteries in photovoltaic applications , 2021 .

[21]  F. Mashayek,et al.  Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling , 2021 .

[22]  Cheng Zhang,et al.  State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator , 2021, Energy.

[23]  Jindong Tian,et al.  Multi-state joint estimation for a lithium-ion hybrid capacitor over a wide temperature range , 2020 .

[24]  Fatima Errahimi,et al.  State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications , 2020 .

[25]  Qi Zhang,et al.  Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter , 2020 .

[26]  Xu Guo,et al.  Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter , 2020, International Journal of Energy Research.

[27]  A. Luviano‐Juárez,et al.  Robust State of Charge estimation for Li-ion batteries based on Extended State Observers , 2020 .

[28]  Dirk Uwe Sauer,et al.  Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation , 2020 .

[29]  Giovanni Lutzemberger,et al.  Luenberger-based State-Of-Charge evaluation and experimental validation with lithium cells , 2020 .

[30]  José Francisco Gómez-Aguilar,et al.  Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory , 2020 .

[31]  Jun Xu,et al.  State-of-health estimation of lithium-ion battery based on fractional impedance model and interval capacity , 2020, International Journal of Electrical Power & Energy Systems.

[32]  Balakumar Balasingam,et al.  A scaling approach for improved state of charge representation in rechargeable batteries , 2020 .

[33]  Boyang Liu,et al.  Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model , 2020 .

[34]  Penghua Li,et al.  State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network , 2020, Journal of Power Sources.

[35]  Inés Couso,et al.  Health assessment of LFP automotive batteries using a fractional-order neural network , 2020, Neurocomputing.

[36]  Jonghoon Kim,et al.  Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH , 2020, Energies.

[37]  Datong Liu,et al.  A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries , 2020 .

[38]  Yujie Wang,et al.  A framework for state-of-charge and remaining discharge time prediction using unscented particle filter , 2020 .

[39]  Yujie Wang,et al.  A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory , 2020 .

[40]  Jianwen Meng,et al.  A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation , 2020 .

[41]  Cheng Cheng,et al.  Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression , 2020, Neurocomputing.

[42]  Chenghui Zhang,et al.  A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries. , 2020, ISA transactions.

[43]  M. Marinescu,et al.  Improved state of charge estimation for lithium-sulfur batteries , 2018, Journal of Energy Storage.

[44]  Mohd Yamani Idna Idris,et al.  Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries , 2019, Renewable and Sustainable Energy Reviews.

[45]  Zheng Liu,et al.  A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter , 2019, IEEE Access.

[46]  P. Bača,et al.  Determination of state of charge of lead-acid battery by EIS , 2019, Journal of Energy Storage.

[47]  Stefano Longo,et al.  Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation , 2018, IEEE Transactions on Power Electronics.

[48]  Thierry Poinot,et al.  Initialization of a fractional order identification algorithm applied for Lithium-ion battery modeling in time domain , 2018, Commun. Nonlinear Sci. Numer. Simul..

[49]  Michael Pecht,et al.  A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors , 2018, Journal of Power Sources.

[50]  Feng Ding,et al.  Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering , 2017, Digit. Signal Process..

[51]  Stephen Duncan,et al.  Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases , 2015, IEEE Transactions on Control Systems Technology.

[52]  Chao Wang,et al.  A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty , 2016 .

[53]  Stefano Longo,et al.  Electric Vehicle Battery Parameter Identification and SOC Observability Analysis: NiMH and Li-S Case Studies , 2016 .

[54]  Baojin Wang,et al.  Fractional-order modeling and parameter identification for lithium-ion batteries , 2015 .