An Adaptive PID Observer for Enhanced State Estimation of Lithium-Ion Batteries

This work presents an adaptive proportional integral derivative control-based observer for enhanced state estimation of Lithium-ion batteries (LIBs) to resolve the issues of slow convergence, and sensitivity to nonlinearities in conventional observers-based methods. Control observers-based estimatio...

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Bibliographic Details
Main Authors: Saeed, Muhammad, Jawaad, Muhammad, Lu, Shuai, Farooq, Umer, Naveed, Muhammad Ali, Riaz, Muhammad Tanveer
Format: Conference Proceeding
Language:English
Subjects:
Battery energy storage systems
control observers
Electric Vehicle
Integrated circuit reliability
Li-ion batteries
Lithium-ion batteries
Observability
Observers
Particle swarm optimization
Real-time systems
Sensitivity
SOC
state estimation
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Summary:This work presents an adaptive proportional integral derivative control-based observer for enhanced state estimation of Lithium-ion batteries (LIBs) to resolve the issues of slow convergence, and sensitivity to nonlinearities in conventional observers-based methods. Control observers-based estimation methods are getting huge appreciation in already controller-packed battery energy storage systems and electric vehicles due to their low computational complexity and high reliability which are the two most desirable characteristics of any online state estimation method. The proposed observer is designed using an equivalent circuit model of LIBs due to its strong observability and a great trade-off between fidelity and complexity. After due analysis and error convergence demonstration, the proposed algorithm is validated against the experimental multiple driving datasets mimicking real-time driving conditions. Finally, an experimental testbench, using 58Ah CALB L148N58A cells whose parameters are experimentally identified by particle swarm optimization, is set up to verify the practicability of the proposed algorithm. Experimental results show better accuracy, enhanced resilience against nonlinearities, and five times faster convergence for the proposed technique.
ISSN:2767-9829
DOI:10.1109/ICECE61222.2024.10505277