Wei He1, Michael Pecht1, David Flynn2, and Fateme Dinmohammadi2,
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2Smart Systems Group, School of Engineering and Physical Sciences, Heriot-Watt University,
Edinburgh EH14 4AS, UK
Abstract:
State-of-charge (SOC) is one of the most critical parameters in battery management systems
(BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full
charge, and thus ranges from 0% to 100%. This percentage value provides important information
to manufacturers about the performance of the battery and can help end-users identify when the
battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or
over-discharge events with significant implications for system safety and reliability. Therefore, it is
crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents
an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal
physical and chemical properties such as lithium concentrations. To solve the computationally
complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method
based on projection with optimized basis functions is presented. Then, a novel moving-window
filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results
show that the developed electrochemical model generates 20 times fewer equations compared with
finite difference-based methods without losing accuracy. In addition, the proposed projection-based
solution method is three times more efficient than the conventional state filtering methods such as
Kalman filter.