Multi‐objective optimization of lithium‐ion battery pack casing for electric vehicles: Key role of materials design and their influence

2019 ◽  
Vol 44 (12) ◽  
pp. 9414-9437 ◽  
Author(s):  
Yihui Zhang ◽  
Siqi Chen ◽  
M.E. Shahin ◽  
Xiaodong Niu ◽  
Liang Gao ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Materials Design ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-Objective Optimization to Minimize Battery Degradation and Electricity Cost for Demand Response in Datacenters

2015 ◽  
Author(s):  
Abdullah-Al Mamun ◽  
Iyswarya Narayanan ◽  
Di Wang ◽  
Anand Sivasubramaniam ◽  
Hosam K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Optimization Problem ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Electricity Cost ◽  
De Algorithm

This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multi-objective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.

Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 443-451 ◽  
Author(s):  
Yujie Wang ◽  
Jiaqiang Tian ◽  
Zonghai Chen ◽  
Xingtao Liu
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Model Based

scholarly journals Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage

2016 ◽  
Vol 7 ◽  
pp. 258-269 ◽  
Author(s):  
A. Mamun ◽  
I. Narayanan ◽  
D. Wang ◽  
A. Sivasubramaniam ◽  
H.K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Lithium Ion ◽  
Multi Objective Optimization ◽  
Battery Storage ◽  
Multi Objective

Metallurgical and mechanical methods for recycling of lithium-ion battery pack for electric vehicles

2018 ◽  
Vol 136 ◽  
pp. 198-208 ◽  
Author(s):  
Liu Yun ◽  
Duy Linh ◽  
Li Shui ◽  
Xiongbin Peng ◽  
Akhil Garg ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Mechanical Methods

Temperature Distribution Optimization of an Air-Cooling Lithium-Ion Battery Pack in Electric Vehicles Based on the Response Surface Method

2019 ◽  
Vol 16 (4) ◽  
Author(s):  
Xiangping Liao ◽  
Chong Ma ◽  
Xiongbin Peng ◽  
Akhil Garg ◽  
Nengsheng Bao
Keyword(s):  
Temperature Distribution ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Cooling System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Maximum Temperature ◽  
Air Cooling ◽  
Optimized Design ◽  
Original Design

Electric vehicles have become a trend in recent years, and the lithium-ion battery pack provides them with high power and energy. The battery thermal system with air cooling was always used to prevent the high temperature of the battery pack to avoid cycle life reduction and safety issues of lithium-ion batteries. This work employed an easily applied optimization method to design a more efficient battery pack with lower temperature and more uniform temperature distribution. The proposed method consisted of four steps: the air-cooling system design, computational fluid dynamics code setups, selection of surrogate models, and optimization of the battery pack. The investigated battery pack contained eight prismatic cells, and the cells were discharged under normal driving conditions. It was shown that the optimized design performs a lower maximum temperature of 2.7 K reduction and a smaller temperature standard deviation of 0.3 K reduction than the original design. This methodology can also be implemented in industries where the battery pack contains more battery cells.

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