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Optimal Design of Lithium Battery Energy Storage Systems (LiB-ESS)
Thermal Considerations
Jorge Reyes Marambio
Centro de Energía Universidad de Chile
PRESENTATION TOPICS
• Joint Venture
• Problem Description
• Methodology and Results
• Conclusion and next steps
Joint Venture
Problem Description
Thermal Management For Hybrid
Vehicles BEHR, 2009.A123 Systems Grid Solutions*NanophosphateTM Lithium Ion Enabling
New Possibilities for the Electric Grid
Battery Management
System
Thermal Management
SystemCell Module Pack
Recent Progresses of LG Chem’s Large-Format Li ion polymer batteriesMohamed Alamgir, Satish Ketkar and KwanghoYooLG Chem Power, Inc., Troy, Michigan, USA
Problem Description
22 kWh LPF Battery ESS forKombi EV Conversion. 2013
3.7 kWh NCM Battery ESS forEolian III. 2012
Battery Management
System
Thermal Management
SystemCell Module Pack
296 Wh NMC Battery for eSEEDElectric Bicycle
Problem Description
21˚C
45˚C
Power and Capacity v/s TemperatureShort-Term Evaluation
Cycle Number v/s TemperatureLong-Term Evaluation
Handbook of Battery. Third Edition. 2002
Problem Description
Chengke Zhou; Kejun Qian; Allan, M.; Wenjun Zhou, "Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems," Energy Conversion, IEEE Transactions on , vol.26, no.4, pp.1041,1050, Dec. 2011
Thermal Management System
Goals
T max < 60˚C
25˚C < T optimal < 40˚C
T between cells < 5˚C
T min > 0˚C
T within cells < 5 - 10 ˚C
Problem Description
Thermal Goals
Min T max
Min ∆T max
Min Volumen
Trade-off between space and temperature in the cells arrangement problem
� Constant cooling air inlet
� Maximum area restriction
� Constant cell heat generation Q = R I^2
� Adiabatic Walls
Problem Description
Arrangement #1
Arrangement #2
39 ºC 74 ºC
59 ºC
44 ºC
Metodology
Multi-objetive problem
Due the no linear behavior of the problem,
the approach is to solve it using Genetic Algorithms
no
OkEND
Initialize
Population
Evaluate obj.
Function of each
individual
Multi-Objective
Genetic
Algorithm
yes
Metodology
Using ANSYS to evaluate each individual in each generation
Challenges
✓✓✓✓ Fast evaluation
✓✓✓✓ Good Precision
✓✓✓✓ Add more and more cells
Approach 1
no
OkEND
Initialize
PopulationANSYS
Multi-Objective
Genetic
Algorithm
yes
Preliminary Results
Approach 1
Metodology
Using ANSYS to fit parametric or black box models
Challenges
✓✓✓✓ Modeling Complex and Non- linear
phenomena
✓✓✓✓ Include all relevant variables
✓✓✓✓ Processing huge amount of data
Approach 2
no
OkEND
Initialize
Population
Parametric
Model
Multi-Objective
Genetic
Algorithm
yes
Preliminary Results
Approach 2
Conclusion and next steps
� Thermal management is a critical aspect in battery systems
� ANSYS enables the undestanding of thermal influence in battery systems.
� There exist a trade-off between thermal goals, space and power consumption of the cooling system
� Periodic boundary conditions will allow to predict the thermal behavior in longer and wider battery packs.
� CFD simulations to determine relationships between heat transfer patterns and packaging strategies.
Optimal Design of Lithium Battery Energy Storage Systems (LiB-ESS)
Thermal Considerations
Thanks !