Recursive Least Square Estimation Approach to Real-Time Parameter Identification in Li-ion Batteries

2019 ◽  
Author(s):  
Sheikh Arif Raihan ◽  
Balakumar Balasingam
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Time Parameter ◽  
Least Square ◽  
Li Ion Batteries ◽  
Recursive Least Square ◽  
Least Square Estimation ◽  
Li Ion

scholarly journals Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 143
Author(s):  
Jose Jimenez-Gonzalez ◽  
Felipe Gonzalez-Montañez ◽  
Victor Manuel Jimenez-Mondragon ◽  
Jesús Ulises Liceaga-Castro ◽  
Rafael Escarela-Perez ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Digital Simulation ◽  
Least Square ◽  
Recursive Least Squares ◽  
Rotor Speed ◽  
Recursive Least Square ◽  
Brushless Dc ◽  
Speed Controller ◽  
Two Stages

In this article, the parameter identification of a brushless DC motor (BLDC) is presented. The approach here presented is based on a direct identification considering a three-phase line-to-line voltage electromagnetic torque as function of the electric currents and rotor speed. The estimation is divided into two stages. First, the electrical parameters are estimated by well-known no-load and DC tests. Consequently, estimation of mechanical parameters is performed using a recursive Least Square Algorithm. The proposed approach is validated by comparing model responses to motor real time responses. Additionally, the design, digital simulation and real time implementation of a PI rotor speed controller, based on the estimated model, validate the identification proposal presented here.

Real-time monitoring of stress development during electrochemical cycling of electrode materials for Li-ion batteries: overview and perspectives

2019 ◽  
Vol 7 (41) ◽  
pp. 23679-23726 ◽  
Author(s):  
Manoj K. Jangid ◽  
Amartya Mukhopadhyay
Keyword(s):  
Real Time ◽  
Electrode Materials ◽  
Electrochemical Potential ◽  
Li Ion Batteries ◽  
Stress Development ◽  
Electrochemical Cycling ◽  
Li Ion ◽  
Real Time Information ◽  
Time Information

Monitoring stress development in electrodes in-situ provides a host of real-time information on electro-chemo-mechanical aspects as functions of SOC and electrochemical potential.

Parameter Identification of Commercial Li-ion Batteries with Marine Predator Algorithm

2021 ◽  
Author(s):  
Shahenda M. Abdelhafiz ◽  
A. M. AbdelAty ◽  
M. E. Fouda ◽  
A. G. Radwan
Keyword(s):  
Parameter Identification ◽  
Li Ion Batteries ◽  
Marine Predator ◽  
Li Ion

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

Robotica ◽  
2020 ◽  
pp. 1-20 ◽  
Author(s):  
Wencen Wu ◽  
Jie You ◽  
Yufei Zhang ◽  
Mingchen Li ◽  
Kun Su
Keyword(s):  
Parameter Identification ◽  
Mobile Robots ◽  
Sensor Network ◽  
Nonlinear Partial Differential Equation ◽  
Least Square ◽  
Diffusion Field ◽  
Recursive Least Square ◽  
Robot System ◽  
Parameterized Model ◽  
Multi Robot

SUMMARY In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

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