@ARTICLE{Yüksek_Gökhan_An_Early,
 author={Yüksek, Gökhan and Lale, Timur},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e151378},
 howpublished={online},
 year={Early Access},
 abstract={Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress on the LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from the LBS is increased by 14.18% compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. In this way, the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste later.},
 type={Article},
 title={An Improved Thermal Management of Lithium Based Batteries Employing Genetic Algorithm Optimization},
 URL={http://czasopisma.pan.pl/Content/132316/PDF/BPASTS-04576-EA.pdf},
 doi={10.24425/bpasts.2024.151378},
 keywords={lithium-ion battery, temperature, energy management system, renewable energy, genetic algorithm},
}