Data Driven Estimation of Electric Vehicle Battery State of Charge Informed By Multi-Physics Modeling

2020 ◽  
Vol MA2020-02 (21) ◽  
pp. 1598-1598
Author(s):  
Marco Ragone ◽  
Vitaliy Yurkiv ◽  
Ajaykrishna Ramasubramanian ◽  
Babak Kashir ◽  
Farzad Mashayek
Keyword(s):  
Electric Vehicle ◽  
State Of Charge ◽  
Data Driven ◽  
Battery State Of Charge ◽  
Electric Vehicle Battery ◽  
Physics Modeling

Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling

2021 ◽  
Vol 483 ◽  
pp. 229108
Author(s):  
Marco Ragone ◽  
Vitaliy Yurkiv ◽  
Ajaykrishna Ramasubramanian ◽  
Babak Kashir ◽  
Farzad Mashayek
Keyword(s):  
Electric Vehicle ◽  
State Of Charge ◽  
Data Driven ◽  
Battery State Of Charge ◽  
Electric Vehicle Battery ◽  
Physics Modeling

Electric Vehicle Battery SOC Estimation Based on EKF

2014 ◽  
Vol 926-930 ◽  
pp. 927-931 ◽  
Author(s):  
Hui Bao ◽  
Wei Jiang ◽  
Dan Wei
Keyword(s):  
Electric Vehicle ◽  
Good Accuracy ◽  
State Of Charge ◽  
Model Parameters ◽  
Static Characteristics ◽  
Soc Estimation ◽  
Mathematical Relation ◽  
Battery State Of Charge ◽  
Electric Vehicle Battery ◽  
Simulation Results

In order to estimate the battery state of charge (SOC) accurately, an improved Thevenin model of a battery is established, its mathematical relation is very simple, and also it is easy to realize. In addition, we identify the model parameters, and then use extended Calman filter algorithm to estimate the battery state of charge. The simulation results show that, this model can well reflect the dynamic and static characteristics of a battery, and the Calman algorithm can keep good accuracy in the estimation process.

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