Battery (electricity)

Quantum mechanics

Algorithm

Turbulence

Control (management)

Intermittency

Engineering

State of charge

Lithium (medication)

Aerospace engineering

Lithium-ion battery

Materials science

Endocrinology

Systems engineering

Range (aeronautics)

Power (physics)

Computer science

Internal resistance

Estimation

Thermodynamics

Physics

Nonlinear system

State observer

Artificial intelligence

Observer (physics)

Medicine

Control theory (sociology)

State (computer science)

Renewable and Sustainable Energy Reviews

Lithium-ion battery is considered as one of the most successful energy storage methods which enables the sustainability of the renewable energy systems subject to high intermittency. To avoid the permanent damage and the potential explosion, the battery state-of-charge (SOC) serves as a characteristic operational parameter that should be maintained within a safe range. However, accurate SOC estimation is challenging in the presence of uncertainties, particularly due to the problems of unknown initial SOC value and the uncertain internal resistance. To address this uncertainties, this paper critically reviews the state-of-the-art of the current SOC researches and takes actions to propose a joint SOC and internal resistance estimation algorithm in a real-time data-driven manner. Both static and dynamic experiments are carried out to identify an equivalent circuit model (ECM) of the battery dynamics. The semi-empirical model's parameters are optimized using the experimental data. Sensitivity analysis is then carried out to reveal that the internal resistance is the dominating parameter that affects the model accuracy. A conventional model-based nonlinear state observer is developed to accommodate the initial value uncertainty. To deal with the uncertain internal resistance through this state observer, internal resistance is treated as an augmented state, which is estimated together with SOC based on extended state observer (ESO). The conclusions are drawn from the experimental results in two aspects, i) a 83% performance improvement of the proposed ESO method is achieved when compared with the conventional observer without internal resistance augmentation; ii) the dynamic variation of the internal resistance is captured by the proposed method, which is shown to increase with the service time. The proposed method is able to give a simultaneous estimation of SOC and internal resistance, depicting a promising prospect in the future commercial application.

Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer