Premium
Fast computational framework for optimal life management of lithium ion batteries
Author(s) -
Mandli Aravinda R.,
Ramachandran Sanoop,
Khandelwal Ashish,
Kim Ki Young,
Hariharan Krishnan S.
Publication year - 2018
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3996
Subject(s) - battery (electricity) , computer science , genetic algorithm , lithium ion battery , mathematical optimization , reliability engineering , degradation (telecommunications) , engineering , mathematics , machine learning , power (physics) , telecommunications , physics , quantum mechanics
Summary The determination of optimal charging profiles over cycle life of a lithium ion battery is a challenging problem that is extremely important for commercial applications. It is a difficult problem to solve owing to the complex degradation processes occurring inside the battery. Further, modeling of a realistic battery operation, let alone the degradation mechanisms, results in computationally expensive mathematical models. In the present study, a framework is developed towards addressing this problem by (1) developing a method to formulate extremely fast and accurate algebraic models that capture essential features such as charging time and aging characteristics described by battery models and (2) utilizing these algebraic models in an optimization framework involving genetic algorithms for determining the optimal charging profiles over the cycle life of the battery. The utility of the present framework in determining the optimal charging solutions is illustrated with various real‐life usage scenarios such as fast charging and extension of cycle life. The proposed solution can be utilized onboard for generating the optimal charging profiles over cycle life of the battery.