A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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Joint structure for the real-time estimation and control of automotive dry clutch engagement

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.09.053 ◽

2018 ◽

Vol 51 (15) ◽

pp. 1062-1067 ◽

Cited By ~ 2

Author(s):

Mojtaba Sharifzadeh ◽

Mario Pisaturo ◽

Arash Farnam ◽

Adolfo Senatore

Keyword(s):

Real Time ◽

Time Estimation ◽

The Real ◽

Joint Structure ◽

Clutch Engagement ◽

Real Time Estimation ◽

Dry Clutch ◽

Estimation And Control ◽

And Control

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Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4034801 ◽

2017 ◽

Vol 139 (3) ◽

Cited By ~ 9

Author(s):

Satadru Dey ◽

Beshah Ayalew

Keyword(s):

Real Time ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

Time Estimation ◽

Li Ion Battery ◽

Battery Cell ◽

Thermal Dynamics ◽

Estimation Scheme ◽

Real Time Estimation ◽

Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

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