Parameter identification method for lithium-ion batteries based on recursive least square with sliding window difference forgetting factor

2021 ◽  
Vol 44 ◽  
pp. 103485
Author(s):  
Jinjin Shi ◽  
Haisheng Guo ◽  
Dewang Chen
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Sliding Window ◽  
Least Square ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Identification Method

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

State of charge estimation of lithium-ion batteries based on an improved parameter identification method

Energy ◽  
2015 ◽  
Vol 90 ◽  
pp. 1426-1434 ◽  
Author(s):  
Bizhong Xia ◽  
Chaoren Chen ◽  
Yong Tian ◽  
Mingwang Wang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Method ◽  
State Of Charge Estimation

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

scholarly journals Energy Modeling and Parameter Identification of Dual-Motor-Driven Belt Conveyors without Speed Sensors

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3313 ◽  
Author(s):  
Chunyu Yang ◽  
Jinhao Liu ◽  
Heng Li ◽  
Linna Zhou
Keyword(s):  
Parameter Identification ◽  
Energy Model ◽  
Least Square ◽  
Recursive Least Square ◽  
Extended Kalman Filtering ◽  
Identification Method ◽  
Driven System ◽  
New Energy ◽  
Least Square Approach ◽  
Belt Conveyors

The energy model of belt conveyors plays a key role in the energy efficiency optimization problem of belt conveyors. However, the existing energy models and parameter identification methods are mainly limited to single-motor-driven belt conveyors and require speed sensors. This paper will present an energy model and a parameter identification method for dual-motor-driven belt conveyors whose speed sensors are not available. Firstly, a new energy model of dual-motor-driven belt conveyors is established by combining the traditional energy model with the dynamic model of a dual-motor-driven system. Then, a parameter identification method based on an extended Kalman filtering algorithm and recursive least square approach is proposed. Finally, the feasibility and effectiveness of the method are demonstrated by simulation experiments.

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