State of Charge Estimation of Li-ion Battery using Extended Kalman Filter and Combined Battery Model
State of Charge Estimation of Li-ion Battery using Extended Kalman Filter and Combined Battery Model
- New search for: Mandhana, Abhishek
- New search for: Gambhir, Ameya V
- New search for: Bagade, Aniket C
- New search for: Mandhana, Abhishek
- New search for: Gambhir, Ameya V
- New search for: Bagade, Aniket C
Lithium ion (Li-ion) batteries require battery management system (BMS) for its safe operation, damage protection and prolong life. BMS must estimate state of charge (SOC) of the battery pack accurately to avoid range anxiety.This paper proposes the SOC estimation of Li ion battery using nonlinear estimator, with validation on embedded platform. In this work EKF based battery SOC estimation algorithm has been developed along with equation-based “combined model” in Simulink. 2 RC model of Li ion cell has been developed in MATLAB-Simulink. Value of SOC obtained using EKF based estimator has been compared with SOC of 2RC model in the simulation. To validate the simulation results, target specific C code has been generated from Simulink model and successfully integrated on embedded platform. Currently algorithm is validated for NMC chemistry, future scope includes validating algorithm for LFP and Lithium titanate oxide (LTO) chemistry.
Document information
State of Charge Estimation of Li-ion Battery using Extended Kalman Filter and Combined Battery Model
Sae Technical Papers
10TH SAE India International Mobility Conference ; 2022
2022-10-05
Conference paper
English
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