Modelling and Simulation of a State-of-Charge-Based Electric Vehicle Battery Management System with Android Monitoring

Abstract

.Existing electric-vehicle battery-management studies often treat CCCV charging, cell balancing, and mobile monitoring separately; few provide a reproducible simulation framework that couples SoC/capacity-aware discharge allocation with real-time Android monitoring. This paper presents a MATLAB/Simulink BMS for a multi-cell lithium-ion pack integrating CCCV charging, passive cell balancing, and a responsibility-based discharge allocator. Each cell is represented by an OCV-Rint/Thevenin equivalent circuit; SoC is updated by coulomb counting, and hybrid pulse power characterization (HPPC) pulses are used to verify the OCV–SoC relationship. In discharge mode, load current is distributed among cell-level DC–DC converters using weights derived from each cell’s usable energy, , subject to Kirchhoff’s current law, current limits, and SoC limits; this addresses capacity differences that SoC-only allocation ignores. The model was compared with uniform discharge under identical initial SoC, 30 A load, and cutoff conditions. The proposed method extended discharge duration from below 1,000 s for uniform discharge to 4,507.93 s, with SoC equalization at 706.82 s, 2,147.73 s, and 2,315.91 s for initial SoCs of 30%, 50%, and 90%, i.e. at least a 351% increase in runtime under the stated fixed-load simulation. A Jetpack Compose Android app using MQTT and Room/SQLite provided real-time SoC, range, energy, and runtime monitoring. Limitations include fixed-load simulation, no thermal/aging model, and the need for cell-level power converters to realize independent cell currents; experimental validation is required