article · Energy Reports
This study proposes a reduced model based on state space representation to accurately identify the electric equivalent circuit of Lithium-Polymer battery cells. A three-stage non-linear optimisation process is used for parameter extraction. The first stage estimates the state of charge, followed by open circuit voltage estimation in the second stage. The third stage develops and uses an Equilibrium Algorithm (EA) for optimal battery parameter identification. The EA's parameters are fine-tuned using Taguchi's design of experiment to minimise computational time and experimental requirements. Numerical simulations and experimental implementation on Li-Ion batteries demonstrated the EA's efficiency and high accuracy, outperforming several recent optimisation algorithms. The reduced model achieved high agreement with experimental measurements for battery voltage and state of charge, offering 16% less computational time and 12% more accuracy than linear and non-linear models.
Accurate modelling and parameter identification are crucial for effective battery management systems, enabling better performance prediction, extended lifespan, and safer operation of Li-Ion batteries. This research offers a more efficient and precise method for achieving these goals, which is vital for various applications.
This research provides an efficient and accurate method for identifying battery parameters, which is fundamental for developing advanced battery management systems. Such systems could be used by battery manufacturers, electric vehicle developers, or energy storage solution providers to improve battery performance, reliability, and safety. This appears to be applied research, offering a refined tool for battery characterisation and control.
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This study proposes a reduced model based on the state space representation for identifying an accurate electric equivalent circuit of Lithium-Polymer Battery Cells. The parameter extraction process is formulated as non-linear optimization problem via three-stage procedure. The first stage estimates the state of charge (SoC) based on the non-linear characteristics associated with the battery current and the initial SoC condition. In the second stage, the open circuit voltage is estimated in terms of the resulted SoC that is employed in the first stage with varied linear and non-linear models. In the third stage, an Equilibrium Algorithm (EA), a recent optimizer, is developed for optimally identifying the battery parameters. The EA’s parameters are adjusted based on Taguchi’s design of experiment approach to reduce the computational time as well as the number of experiments that are required to get the optimum possible parameter arrangement Numerical simulations associated with experimental implementation are emulated on Li-Ion Battery to prove the high capability of the proposed EA an as efficient identification procedure. In Addition, the proposed EA is characterized with high accuracy compared with several recent optimization algorithms for ARTEMIS driving cycle profile. The solution quality improvement of the proposed reduced model is achieved with high closeness to the experimental measurements for battery voltage and SoC. Furthermore, 16 % less computational times 12 % more accuracy are obtained by the proposed reduced model compared with linear and non-linear models.
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DOI: 10.1016/j.egyr.2021.10.086
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