State of Charge Estimation Using Extended Kalman Filter and Genetic Algorithms With Integrated Memory Analysis

This work presents a novel approach to State of Charge (SoC) estimation by integrating a dynamically adaptive Extended Kalman Filter (EKF) with a Genetic Algorithm (GA) and conducting comprehensive memory analysis alongside other battery management system (BMS) features. The EKF utilizes a variable lookup table to adjust the open-circuit voltage (OCV) and resistance-capacitance (RC) parameters in real-time, accounting for temperature and state-of-charge variations. To enhance performance, a GA optimizes the EKF's process and measurement noise covariance matrices (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex>), enabling robust estimation across diverse operating conditions. Additionally, the memory usage of the EKF and its integration with other BMS components, such as cell sensing, balancing, and pack current/voltage monitoring boards, is analyzed to evaluate its feasibility on NXP automotive-grade microprocessors. This work demonstrates a scalable and computationally efficient solution for real-time BMS applications in electric vehicles with a less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 \%}$</tex> RMSE at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0}{ }^{\circ} \mathrm{C}$</tex>.