Lithium-ion battery dynamic model for wide range of operating conditions

2017 ◽  
Author(s):  
Ana-Irina Stroe ◽  
Daniel-Ioan Stroe ◽  
Maciej Swierczynski ◽  
Remus Teodorescu ◽  
Soren Knudsen Kaer
Keyword(s):  
Lithium Ion Battery ◽  
Dynamic Model ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Wide Range

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

Extended Physics-Based Reduced-Order Capacity Fade Model for Lithium Ion Battery Cells

2021 ◽  
pp. 1-12
Author(s):  
Zachary Salyer ◽  
Matilde D'Arpino ◽  
Marcello Canova
Keyword(s):  
Lithium Ion Battery ◽  
Cell Model ◽  
Active Material ◽  
Lithium Ion ◽  
Calibration Procedure ◽  
Layer Growth ◽  
Operating Conditions ◽  
Capacity Fade ◽  
Computationally Efficient ◽  
Reduced Order

Abstract Aging models are necessary to accurately predict the SOH evolution in lithium ion battery systems when performing durability studies under realistic operatings, specifically considering time-varying storage, cycling, and environmental conditions, while being computationally efficient. This paper extends existing physics-based reduced-order capacity fade models that predict degradation resulting from the solid electrolyte interface (SEI) layer growth and loss of active material (LAM) in the graphite anode. Specifically, the physics of the degradation mechanisms and aging campaigns for various cell chemistries are reviewed to improve the model fidelity. Additionally, a new calibration procedure is established relying solely on capacity fade data and results are presented including extrapolation/validation for multiple chemistries. Finally, a condition is integrated to predict the onset of lithium plating. This allows the complete cell model to predict the incremental degradation under various operating conditions, including fast charging.

scholarly journals Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model

2020 ◽  
Vol 10 (5) ◽  
pp. 1896 ◽  
Author(s):  
Chi Nguyen Van ◽  
Thuy Nguyen Vinh
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Lithium Ion ◽  
Second Order ◽  
Battery Pack ◽  
Sigma Point ◽  
Soc Estimation ◽  
Cell Modules

This paper deals with the state of charge (SoC) estimation of a lithium-ion battery pack (LiBP) connected by some cells in series and parallel. The voltage noise, noise and current bias of current through the LiBP are taken into account in the SoC estimation problem. In order to describe the cell dynamic more accurately, especially for practical applications with charge and discharge amplitude varying suddenly, in this paper we use the second dynamic order model of the cell to estimate the SoC of the LiBP. By applying the sigma point Kalman filter (SPKF), the average SoC of the pack and bias current of current measurement are estimated by first estimator; the second estimator estimates the SoC differences of the cell modules from average SoC of the pack. The SoC of the cell modules are the sum of average SoCs of the pack and the SoC differences. By only using two estimators, the calculation complexity for SoC estimation is more reduced; this is very useful for the LiBP, which has the number of cells connected in a large series. This method was applied for the pack of SAMSUNG ICR18650-22P connected by seven cell modules; the cell modules were connected by nine cells in parallel; the LiBP was charged and discharged with amplitude varying suddenly. The estimated SoC of seven cell modules is smaller than 2% for a temperature operating range typically −5 °C to 45 °C. The comparison of the accuracy of SoC estimation based on the first and the second order dynamic models is made; the result shows that the SoC estimation used the second order dynamic model is more exact.

Maximum Power Estimation of Lithium-Ion Batteries Accounting for Thermal and Electrical Constraints

2013 ◽  
Author(s):  
Youngki Kim ◽  
Shankar Mohan ◽  
Jason B. Siegel ◽  
Anna G. Stefanopoulou
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Thermal Systems ◽  
Model Inversion ◽  
Dynamic Constraints ◽  
Time Scale Separation ◽  
Wide Range ◽  
Temperature Constraint ◽  
Power Capability

Enforcement of constraints on the maximum deliverable power is essential to protect lithium-ion batteries from over-charge/discharge and overheating. This paper develops an algorithm to address the often overlooked temperature constraint in determining the power capability of battery systems. A prior knowledge of power capability provides dynamic constraints on currents and affords an additional control authority on the temperature of batteries. Power capability is estimated using a lumped electro-thermal model for cylindrical cells that has been validated over a wide range of operating conditions. The time scale separation between electrical and thermal systems is exploited in addressing the temperature constraint independent of voltage and state-of-charge (SOC) limits. Limiting currents and hence power capability are determined by a model-inversion technique, termed Algebraic Propagation (AP). Simulations are performed using realistic depleting currents to demonstrate the effectiveness of the proposed method.

Dynamic Model of a Sac-Type Pneumatically Driven Artificial Ventricle

1977 ◽  
Vol 99 (1) ◽  
pp. 14-19 ◽  
Author(s):  
D. B. Geselowitz ◽  
G. E. Miller ◽  
W. M. Phillips
Keyword(s):  
Dynamic Model ◽  
Circulatory System ◽  
Operating Conditions ◽  
Time Varying ◽  
Outlet Port ◽  
Mock Circulatory System ◽  
Linear Resistance ◽  
Wide Range ◽  
Artificial Ventricle ◽  
Nonlinear Resistance

Inlet and outlet pressures and flows were obtained over a wide range of operating conditions for a pneumatically driven sac-type artificial ventricle connected to a mechanical mock circulatory system. The load presented to the ventricle by the mock circulatory system was found to be characterized by a linear resistance and capacitance. A dynamic model for the ventricle which accounted for instantaneous pressures and flows was developed. The outlet port is characterized by an inertance and square law resistance; the inlet port is characterized by a nonlinear resistance dependent on the type of valve. The input to the model is the time varying sac pressure. The model predicts the fill-limited and ejection-limited modes of the artificial ventricle.

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Based on Extended Kalman Filter Algorithm

2020 ◽  
pp. 420-437
Author(s):  
Wu Xiaogang ◽  
Xuefeng Li ◽  
Nikolay I. Shurov ◽  
Alexander A. Shtang ◽  
Michael V. Yaroslavtsev ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Management ◽  
Soc Estimation ◽  
Ternary Polymer

As the core component of electric vehicle, lithium-ion battery needs to adopt effective battery management method to prolong battery life and improve the reliability and safety. The accurate estimation of the battery SOC can be used to prevent the battery over charge and over discharge, reduce damage to the battery and improve battery performance, which plays a vital role in the battery management system. The study of battery SOC estimation mainly focused on the battery model construction and SOC estimation algorithm. Aiming at the problem that the state of charge (SOC) of electric vehicle is difficult to be accurately estimated under complex operating conditions, based on the parameter identification of the equivalent circuit of a ternary polymer lithium-ion battery, an Extended Kalman Filter (EKF) algorithm was used to estimate the SOC of the ternary polymer lithium-ion battery. Simulation and experimental results show that the estimation of SOC can be carried out by using the EKF algorithm under the conditions of China Passenger Car Condition (Chinacar) and new European driving cycle (NEDC) Compared with the coulomb counting method, the average error of SOC estimation can be realized is 1.042% and 1.138% respectively, the maximum error within 4%. Application of this algorithm to achieve SOC estimation has good robustness and convergence

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