Statistics

Algorithm

Square root

Electrical engineering

Physics

Engineering

Endocrinology

Artificial intelligence

Kalman filter

Power (physics)

Organic chemistry

Chemistry

Extended Kalman filter

Ionic bonding

Geometry

Control (management)

State of charge

Lithium (medication)

Lithium battery

Lithium-ion battery

Mathematics

Computer science

Control theory (sociology)

State (computer science)

Battery (electricity)

Root mean square

Medicine

Thermodynamics

Energy

The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm.

A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter