Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

2021 ◽  
Author(s):  
Pu Ren ◽  
Shunli Wang ◽  
Junhan Huang ◽  
Xianpei Chen ◽  
Mingfang He ◽  
...  
Keyword(s):  
Particle Filter ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
State Of Health ◽  
Forgetting Factor ◽  
Estimation Strategy ◽  
Particle Filter Algorithm

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

scholarly journals Implementation of Coulomb Counting Method for Estimating the State of Charge of Lithium-Ion Battery

2021 ◽  
pp. 1-8
Author(s):  
Kevin C. Ndeche ◽  
Stella O. Ezeonu
Keyword(s):  
Lithium Ion Battery ◽  
Coulombic Efficiency ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Counting Method ◽  
Voltage Measurement ◽  
Initial State ◽  
Battery Capacity

An accurate estimate of the state of charge, which describes the remaining percentage of a battery’s capacity, has been an important and ever existing problem since the invention of the electrochemical cell. State of charge estimation is one of the important function of a battery management system which ensures the safe, efficient and reliable operation of a battery. In this paper, the coulomb counting method is implemented for the estimation of the state of charge of lithium-ion battery. The hardware comprises an Arduino based platform for control and data processing, and a 16-bit analog to digital converter for current and voltage measurement. The embedded algorithm initializes with a self-calibration phase, during which the battery capacity, coulombic efficiency and initial state of charge are evaluated. The initial state of charge is determined at the fully charged state (100% state of charge) or the fully discharged state (0% state of charge). The cumulative error of this method was addressed by routine recalibration of the capacity, coulombic efficiency and state of charge at the fully-charged and fully-discharged states. The algorithm was validated by charging/discharging a lithium-ion battery through fifty complete cycles and evaluating the error in the estimated state of charge. The result shows a mean absolute error of 0.35% in the estimated state of charge during the test. Further analysis, considering prolonged battery operation without parameters recalibration, suggests that error in the coulombic efficiency term contributes the most to the increasing error in the estimated state of charge with each cycle.

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