Estimation of SI Engine Load Torque: Adaptive Kalman Filter vs. Luenberger Estimator

2004 ◽  
Author(s):  
Josˇko Deur ◽  
Danijel Pavkovic´ ◽  
Davor Hrovat
Keyword(s):  
Kalman Filter ◽  
Detection Algorithm ◽  
Si Engine ◽  
Adaptive Kalman Filter ◽  
Third Order ◽  
Load Torque ◽  
Adaptation Mechanism ◽  
Engine Load ◽  
Train Control ◽  
On Line

The SI engine load torque is key information for many engine and power train control systems. Since the torque is not measured in production vehicles, it needs to be estimated on-line. The paper presents design and analysis of second-order and third-order Luenberger load torque estimators. With the aim to reduce the estimator noise sensitivity without deteriorating its transient performance, an adaptive Kalman filter is proposed and compared with the Luenberger estimator. The adaptation mechanism is based on a load torque change detection algorithm. The estimators are examined by computer simulations and experiments.

scholarly journals Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 540 ◽  
Author(s):  
Filip Maletić ◽  
Mario Hrgetić ◽  
Joško Deur
Keyword(s):  
Kalman Filter ◽  
Time Estimation ◽  
Detection Algorithm ◽  
Open Circuit ◽  
Battery Management ◽  
Adaptation Mechanism ◽  
Sigma Point ◽  
Estimation Algorithms ◽  
Electric Scooter ◽  
Remaining Capacity

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

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