Modeling and Simulation of Lithium-Ion Battery Pack Using Modified Battery Cell Model

2019 ◽  
Author(s):  
Meilisa Dewi Kharisma ◽  
Muhammad Ridwan ◽  
Arinata Fatchun Ilmiawan ◽  
Ferdaus Ario Nurman ◽  
Saiful Rizal
Keyword(s):  
Modeling And Simulation ◽  
Lithium Ion Battery ◽  
Cell Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Cell

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

scholarly journals Three-Dimensional Thermal Simulations of 18650 Lithium-Ion Batteries Cooled by Different Schemes under High Rate Discharging and External Shorting Conditions

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6986
Author(s):  
Yang Li ◽  
Zhifu Zhou ◽  
Jian Zhao ◽  
Liang Hao ◽  
Minli Bai ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Battery Pack ◽  
High Rate ◽  
Air Cooling ◽  
Two Phase ◽  
Battery Cell ◽  
Short Period ◽  
Thermal Simulations

In this work, three-dimensional thermal simulations of single 18650 lithium-ion battery cell and 75 V lithium-ion battery pack composed of 21 18650 battery cells are performed based on a multi-scale multi-domain (MSMD) battery modeling approach. Different cooling approaches’ effects on 18650 lithium-ion battery and battery pack thermal management under fast discharging and external shorting conditions are investigated and compared. It is found that for the natural convection, forced air cooling, and/or mini-channel liquid cooling approaches, the temperature of battery cell easily exceeds 40 °C under 3C rate discharging condition. While under external shorting condition, the temperature of cell rises sharply and reaches the 80 °C in a short period of time, which can trigger thermal runaway and may even lead to catastrophic battery fire. On the other hand, when the cooling method is single-phase direct cooling with FC-72 as coolant or two-phase immersed cooling by HFE-7000, the cell temperature is effectively limited to a tolerable level under both high C rate discharging and external shorting conditions. In addition, two-phase immersed cooling scheme is found to lead to better temperature uniformity according to the 75 V battery pack simulations.

scholarly journals Lithium-Ion Battery Cell-Balancing Algorithm for Battery Management System Based on Real-Time Outlier Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Changhao Piao ◽  
Zhaoguang Wang ◽  
Ju Cao ◽  
Wei Zhang ◽  
Sheng Lu
Keyword(s):  
Lithium Ion Battery ◽  
Outlier Detection ◽  
Management System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Online Clustering ◽  
Cell Balancing

A novel cell-balancing algorithm which was used for cell balancing of battery management system (BMS) was proposed in this paper. Cell balancing algorithm is a key technology for lithium-ion battery pack in the electric vehicle field. The distance-based outlier detection algorithm adopted two characteristic parameters (voltage and state of charge) to calculate each cell’s abnormal value and then identified the unbalanced cells. The abnormal and normal type of battery cells were acquired by online clustering strategy and bleeding circuits (R= 33 ohm) were used to balance the abnormal cells. The simulation results showed that with the proposed balancing algorithm, the usable capacity of the battery pack increased by 0.614 Ah (9.5%) compared to that without balancing.

scholarly journals Improving Lithium-Ion Battery Pack Diagnostics by Optimizing the Internal Allocation of Demand Current for Parameter Identifiability

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Michael J. Rothenberger ◽  
Jariullah Safi ◽  
Ji Liu ◽  
Joel Anstrom ◽  
Sean Brennan ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Pack ◽  
Simulation Studies ◽  
External Current ◽  
Battery Cell ◽  
Parameter Identifiability ◽  
Health Related ◽  
Speed And Accuracy ◽  
Local Outlier

This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC–DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.

Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure

Nature Energy ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 123-134
Author(s):  
Fabian Duffner ◽  
Niklas Kronemeyer ◽  
Jens Tübke ◽  
Jens Leker ◽  
Martin Winter ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Cell ◽  
Cell Production ◽  
Lithium Ion Cell

scholarly journals Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi
Keyword(s):  
Lithium Ion Battery ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Drive Cycle ◽  
Cell Dynamics ◽  
Battery Cell ◽  
Parameter Identifications

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

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