scholarly journals A Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

2021 ◽  
Vol 12 (3) ◽  
pp. 108
Author(s):  
Tao Chen ◽  
Mengmeng Huo ◽  
Xiaolong Yang ◽  
Rui Wen
Keyword(s):  
Estimation Error ◽  
Stress Test ◽  
Estimation Method ◽  
Lithium Ion ◽  
Soc Estimation ◽  
Fast Estimation ◽  
Compensation Factor ◽  
Battery State Of Charge ◽  
Observer Model ◽  
System Compensation

Due to the complex changes in battery state, the accurate and fast estimation of battery state of charge (SOC) is still a great challenge. Here, a fast estimation method of battery impedance and SOC based on a multi-level PI observer is proposed. The observer model reflects the change of the battery state characteristics through the dynamic impedance, and then the system compensation factor is added to the observer to dynamically adjust the parameters of the battery model. The effectiveness of the algorithm is verified by the compound dynamic stress test (DST) experiment. The results show that the introduction of the compensation factor enables the system to tolerate a certain degree of impedance fluctuation and capacity attenuation and the maximum SOC estimation error can be kept within 2%.

scholarly journals Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

2021 ◽  
Vol 11 (24) ◽  
pp. 11797
Author(s):  
Dongdong Ge ◽  
Zhendong Zhang ◽  
Xiangdong Kong ◽  
Zhiping Wan
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Primary Concern ◽  
Soc Estimation

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

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 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.

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.

A Nonlinear Observer SOC Estimation Method Based on Electrochemical Model for Lithium-Ion Battery

2021 ◽  
Vol 57 (1) ◽  
pp. 1094-1104
Author(s):  
Yuntian Liu ◽  
Rui Ma ◽  
Shengzhao Pang ◽  
Liangcai Xu ◽  
Dongdong Zhao ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Nonlinear Observer ◽  
Soc Estimation ◽  
Electrochemical Model

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

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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