scholarly journals State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6371
Author(s):  
Lan Li ◽  
Minghui Hu ◽  
Yidan Xu ◽  
Chunyun Fu ◽  
Guoqing Jin ◽  
...  
Keyword(s):  
Fractional Order ◽  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation ◽  
Battery Model ◽  
Hybrid Particle Swarm Optimization

To accurately estimate the state of charge (SOC) of lithium-ion power batteries in the event of errors in the battery model or unknown external noise, an SOC estimation method based on the H-infinity filter (HIF) algorithm is proposed in this paper. Firstly, a fractional-order battery model based on a dual polarization equivalent circuit model is established. Then, the parameters of the fractional-order battery model are identified by the hybrid particle swarm optimization (HPSO) algorithm, based on a genetic crossover factor. Finally, the accuracy of the SOC estimation results of the lithium-ion batteries, using the HIF algorithm and extended Kalman filter (EKF) algorithm, are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within ±0.02 in any case, and is slightly affected by environmental and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system.

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

State of Charge Estimation Error due to Parameter Mismatch in a Generalized Explicit Lithium Ion Battery Model

2011 ◽  
Author(s):  
Xinfan Lin ◽  
Anna Stefanopoulou ◽  
Patricia Laskowsky ◽  
Jim Freudenberg ◽  
Yonghua Li ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Hybrid Electric Vehicle ◽  
Estimation Error ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Parameter Mismatch ◽  
Linear Diffusion ◽  
Soc Estimation ◽  
Battery Model

Model-based state of charge (SOC) estimation with output feedback of the voltage error is steadily augmenting more traditional coulomb counting or voltage inversion techniques in hybrid electric vehicle applications. In this paper, the state (SOC) estimation error in the presence of model parameter mismatch is calculated for a general lithium ion battery model with linear diffusion or impedance-based state dynamics and nonlinear output voltage equations. The estimation error due to initial conditions and inputs is derived for linearized battery models and also verified by nonlinear simulations. It is shown that in some cases of parameter mismatch, the state, e.g. SOC, estimation error will be significant while the voltage estimation error is negligible.

scholarly journals Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ting Zhao ◽  
Jiuchun Jiang ◽  
Caiping Zhang ◽  
Kai Bai ◽  
Na Li
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Error ◽  
Estimation Method ◽  
Dynamic Test ◽  
Lithium Ion ◽  
Vital Role ◽  
State Of Charge ◽  
Battery Pack ◽  
Estimation Accuracy ◽  
Soc Estimation

Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

scholarly journals State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2491 ◽  
Author(s):  
Wenhui Zheng ◽  
Bizhong Xia ◽  
Wei Wang ◽  
Yongzhi Lai ◽  
Mingwang Wang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Sliding Mode Observer ◽  
State Of Charge ◽  
Battery Management System ◽  
Test Results ◽  
Soc Estimation

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

2021 ◽  
pp. 095440702199434
Author(s):  
Wei Yue ◽  
Cong-zhi Liu ◽  
Liang Li ◽  
Xiang Chen ◽  
Fahad Muhammad
Keyword(s):  
Fractional Order ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Open Circuit ◽  
Design Criterion ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Battery Pack ◽  
Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

scholarly journals A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 394 ◽  
Author(s):  
Qi Zhang ◽  
Yan Li ◽  
Yunlong Shang ◽  
Bin Duan ◽  
Naxin Cui ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Lithium Ion ◽  
Absolute Error ◽  
Battery Management System ◽  
Order Kinetic ◽  
Battery Management ◽  
Nonlinear Capacity ◽  
Battery Model ◽  
Parameter Identifications

Accurate battery models are integral to the battery management system and safe operation of electric vehicles. Few investigations have been conducted on the influence of current rate (C-rate) on the available capacity of the battery, for example, the kinetic battery model (KiBaM). However, the nonlinear characteristics of lithium-ion batteries (LIBs) are closer to a fractional-order dynamic system because of their electrochemical materials and properties. The application of fractional-order models to represent physical systems is timely and interesting. In this paper, a novel fractional-order KiBaM (FO-KiBaM) is proposed. The available capacity of a ternary LIB module is tested at different C-rates, and its parameter identifications are achieved by the experimental data. The results showed that the estimated errors of available capacity in the proposed FO-KiBaM were low over a wide applied current range, specifically, the mean absolute error was only 1.91%.

A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs

2010 ◽  
Vol 152-153 ◽  
pp. 428-435 ◽  
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery Packs ◽  
Novel Method ◽  
Road Data

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

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