A Variational Theorem-Based Uncertain Initialization SOC Estimation Technique in Li-Ion Battery Applications.

Area of Science:

• Battery Technology
• State Estimation
• Control Systems

Background:

• Accurate state of charge (SOC) estimation is crucial for reliable lithium-ion (Li-ion) battery operation.
• Internal variations within battery packs can lead to uncertain initial SOC values, impacting performance.
• Existing methods struggle with uncertain SOC initialization.

Purpose of the Study:

• To address the challenge of uncertain SOC initialization in Li-ion battery packs.
• To propose a novel method for deriving unbiased initial SOC expectations.
• To enhance the accuracy and reliability of SOC estimation, particularly in the early stages of battery use.

Main Methods:

• Combining the variational theorem with the extended Kalman filter (EKF) algorithm.
• Utilizing the backward smoother technique to derive unbiased initialized SOC expectations from current observations.
• Implementing a variational initialization technique to update uncertain SOC settings.

Main Results:

• The proposed variational initialization EKF (VIEKF) significantly improves SOC estimation performance, especially during initial moments.
• Experimental results using lab data, CALCE open-access data, and electric vehicle (EV) applications validate the technique's effectiveness.
• The VIEKF demonstrates superiority over other improved EKF algorithms in handling uncertain SOC initialization.

Conclusions:

• The developed VIEKF method effectively solves the problem of uncertain SOC initialization in Li-ion batteries.
• The technique enhances the tracking of true SOC values, leading to improved estimation accuracy.
• This advancement contributes to more reliable and efficient utilization of Li-ion batteries in various applications.