Modeling PWM-Time-SOC Interaction in a Simulated Robot

2026-02-27

AI Summary

This work addresses the challenge of accurately predicting battery state of charge (SOC) in autonomous robots to prevent task interruption, a problem exacerbated by the complex nonlinear dynamics between SOC, time, and motor control signals (PWM). To overcome the limitations of existing methods, the authors propose a hybrid modeling framework that integrates physical principles with data-driven techniques. Leveraging the electrical and mechanical dynamics of DC motors, they generate high-fidelity simulated SOC time-series data for a four-wheel Arduino robot across PWM inputs ranging from 1% to 100%. For the first time, they apply Sparse Identification of Nonlinear Dynamics (SINDy) combined with least-squares regression to derive an interpretable and scalable unified model, SOC(t, p), capable of adapting to arbitrary initial SOC levels and environmental conditions. This model provides a foundation for energy-aware path planning through precise power consumption forecasting and demonstrates strong potential for transfer to real-world robotic systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• State of Charge
• PWM
• Autonomous Robots
• Battery Modeling
• Energy-aware Planning

Innovation

• SINDy
• state of charge (SOC)
• PWM control
• physics-informed modeling
• energy-aware planning