Modeling PWM-Time-SOC Interaction in a Simulated Robot

TL;DR

This paper presents a physics-informed data-driven model that predicts battery state of charge over time and PWM duty cycle for a simulated robot, aiding energy-aware planning and real-world adaptation.

Contribution

It introduces a novel combined physics and data-driven modeling approach using SINDy to accurately predict SOC dynamics in robotic systems.

Findings

Developed a nonlinear SOC prediction model from simulation data.

Integrated motor and mechanical dynamics into the SOC model.

Framework supports extension to real-world robot deployment.

Abstract

Accurate prediction of battery state of charge is needed for autonomous robots to plan movements without using up all available power. This work develops a physics and data-informed model from a simulation that predicts SOC depletion as a function of time and PWM duty cycle for a simulated 4-wheel Arduino robot. A forward-motion simulation incorporating motor electrical characteristics (resistance, inductance, back-EMF, torque constant) and mechanical dynamics (mass, drag, rolling resistance, wheel radius) was used to generate SOC time-series data across PWM values from 1-100%. Sparse Identification of Nonlinear Dynamics (SINDy), combined with least-squares regression, was applied to construct a unified nonlinear model that captures SOC(t, p). The framework allows for energy-aware planning for similar robots and can be extended to incorporate arbitrary initial SOC levels and environment-dependent parameters for real-world deployment.