Robustness comparison of battery state of charge observers for automotive applications

• This paper compares the robustness of three different battery State of Charge (SoC) estimation algorithms: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the H∞ filter. Their performance when subject to disturbances such as parameter uncertainties, different sensor noise characteristics and sensitivity to tuning of the filter are examined. Simulations show that the appropriate choice of observer algorithm will depend on battery chemistry as well as on the intended application. For batteries with a strong correlation between SoC and OCV, the UKF is robust to disturbances such as sensor bias. The H∞ observer shows performance on par with the UKF but the variability of the estimation errors are larger. The EKF is a good all-round choice.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Automotive

H-infinity

Hybrid vehicles

Kalman filters

Robustness

State estimation

Automation

Bearings (machine parts)

Charging (batteries)

Extended Kalman filters

Robustness (control systems)

Secondary batteries

Automotive applications

Battery chemistries

Battery state of charge

Observer algorithms

Parameter uncertainty

Unscented Kalman Filter

Battery management systems

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