Battery (electricity)

Gene

Quantum mechanics

Algorithm

Recursive least squares filter

Control (management)

Voltage

Engineering

State of charge

Lithium (medication)

Statistics

Materials science

Robustness (evolution)

Endocrinology

Systems engineering

Power (physics)

Biochemistry

Computer science

Acoustics

Battery capacity

Adaptive filter

Estimation

Physics

Mathematics

Optics

Estimator

Image (mathematics)

Electronic engineering

Equivalent circuit

Attenuation

Artificial intelligence

Noise measurement

Medicine

Chemistry

Control theory (sociology)

Noise reduction

Noise (video)

Electrical engineering

IEEE Transactions on Industrial Electronics

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.

Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery