Experimental validation for Li-ion battery modeling using Extended Kalman Filters

2017 ◽  
Vol 42 (40) ◽  
pp. 25509-25517 ◽  
Author(s):  
F. Claude ◽  
M. Becherif ◽  
H.S. Ramadan
Keyword(s):  
Kalman Filters ◽  
Experimental Validation ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Extended Kalman Filters ◽  
Li Ion

Development of a Two-Dimensional Thermal Model for Li-Ion Battery Pack With Experimental Validation

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Haoting Wang ◽  
Ning Liu ◽  
Lin Ma
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Experimental Validation ◽  
Computational Cost ◽  
Battery Pack ◽  
Two Dimensional ◽  
Li Ion Battery ◽  
New Approach ◽  
Battery Packs ◽  
Li Ion

Abstract This paper reports the development of a two-dimensional two states (2D2S) model for the analysis of thermal behaviors of Li-ion battery packs and its experimental validation. This development was motivated by the need to fill a niche in our current modeling capabilities: the need to analyze 2D temperature (T) distributions in large-scale battery packs in real time. Past models were predominately developed to either provide detailed T information with high computational cost or provide real-time analysis but only 1D lumped T information. However, the capability to model 2D T field in real time is desirable in many applications ranging from the optimal design of cooling strategies to onboard monitoring and control. Therefore, this work developed a new approach to provide this desired capability. The key innovations in our new approach involved modeling the whole battery pack as a complete thermal-fluid network and at the same time calculating only two states (surface and core T) for each cell. Modeling the whole pack as a complete network captured the interactions between cells and enabled the accurate resolution of the 2D T distribution. Limiting the calculation to only the surface and core T controlled the computational cost at a manageable level and rendered the model suitable for packs at large scale with many cells.

scholarly journals Identification of Li-ion battery models through monotonic echo serial networks for coarse data

2020 ◽  
Vol 28 (1) ◽  
pp. 109-120
Author(s):  
Antonio Álvarez-Caballero ◽  
Cecilio Blanco ◽  
Inés Couso ◽  
Luciano Sánchez
Keyword(s):  
First Principles ◽  
Experimental Validation ◽  
Expert Knowledge ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Monotone Transformation ◽  
Battery Model ◽  
Inaccurate Data ◽  
Li Ion ◽  
Better Than

Abstract Monotone transformation models are extended to inaccurate data and are combined with recurrent neural networks in a new battery model that is able to ascertain the health of rechargeable batteries for automotive applications. The presented method exploits the information contained in the vehicle’s operational records better than other cutting-edge models and uses a minimum amount of human expert knowledge. The experimental validation of the technique includes a comparative analysis of batteries in different health conditions, comprising first-principles models and different machine learning procedures.

Thermal management of Li-ion battery with phase change material for electric scooters: experimental validation

2005 ◽  
Vol 142 (1-2) ◽  
pp. 345-353 ◽  
Author(s):  
Siddique A. Khateeb ◽  
Shabab Amiruddin ◽  
Mohammed Farid ◽  
J. Robert Selman ◽  
Said Al-Hallaj
Keyword(s):  
Phase Change ◽  
Phase Change Material ◽  
Thermal Management ◽  
Experimental Validation ◽  
Li Ion Battery ◽  
Li Ion ◽  
Change Material

scholarly journals Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Manh-Kien Tran ◽  
Andre DaCosta ◽  
Anosh Mevawalla ◽  
Satyam Panchal ◽  
Michael Fowler
Keyword(s):  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Li Ion ◽  
And Control

Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.

Electrochemical Li-Ion Battery Modeling for Control Design With Optimal Uneven Discretization

2011 ◽  
Author(s):  
Tae-Kyung Lee ◽  
Zoran S. Filipi
Keyword(s):  
Optimization Problems ◽  
Control Design ◽  
Latin Hypercube Sampling ◽  
Solid Particles ◽  
Data Sets ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Battery Model ◽  
Diffusion Dynamics ◽  
Li Ion

This paper proposes a reduced Li-ion battery model for design optimization and control design by implementing the electrode-averaged diffusion dynamics and uneven discretization of the particle radius for fast computation and accurate prediction of the Lithium intercalation dynamics. First, the unevenly discretized dynamics model is constructed from the ordinary differential equation (ODE) derived in the electrode-averaged battery model. Then, constrained optimization problems with multi-objectives are formulated to find the optimal uneven discretization. The cost function is evaluated under the wide battery operation data sets constructed by Latin hypercube sampling (LHS) to reduce the total number of cases. The optimally determined unevenly discretized model can predict the battery electrochemical dynamics with much smaller number of discretization steps compared to the evenly discretized electrode-averaged battery model without loss of physical interpretation of the diffusion dynamics in the electrode solid particles.

scholarly journals Lithium-Ion Battery Modeling Including Degradation Based on Single-Particle Approximations

Batteries ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 37 ◽  
Author(s):  
Mostafa Al-Gabalawy ◽  
Nesreen S. Hosny ◽  
Shimaa A. Hussien
Keyword(s):  
Single Particle ◽  
Lithium Ion ◽  
Particle Model ◽  
Battery Life ◽  
Parameter Method ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Physical Chemical ◽  
Li Ion ◽  
Effects Of Aging

This paper introduces a physical–chemical model that governs the lithium ion (Li-ion) battery performance. It starts from the model of battery life and moves forward with simplifications based on the single-particle model (SPM), until arriving at a more simplified and computationally fast model. On the other hand, the implementation of this model is developed through MATLAB. The goal is to characterize an Li-ion cell and obtain its charging and discharging curves with different current rates and different cycle depths, as well as its transitory response. In addition, the results provided are represented and compared, and different methods of estimating the state of the batteries are applied. They include the dynamics of the electrolyte and the effects of aging caused by a high number of charging and discharging cycles of the batteries. A complete comparison with the three-parameter method (TPM) is represented in order to demonstrate the superiority of the applied methodology.

Sign in / Sign up

Export Citation Format

Share Document