用仿真数据建模电机占空比与电池耗电关系，助机器人节能规划
Modeling PWM-Time-SOC Interaction in a Simulated Robot
- 结合物理规律与数据，构建电池电量随时间与占空比变化的统一非线性模型
- 在1%-100%占空比下预测精度高，支持不同初始电量和环境参数
- 适合做能量感知的移动机器人路径规划，可拓展至真实场景部署
精确预测电池状态（SOC）对自主机器人规划运动至关重要，避免过度耗电。本文基于模拟的四轮Arduino机器人，建立了一个融合物理规律与数据的模型，预测SOC随时间与PWM占空比的变化。通过包含电机电气特性（电阻、电感、反电动势、转矩常数）和机械动力学（质量、气动阻力、滚动阻力、轮半径）的前向运动仿真，生成了1%-100%占空比范围内的SOC时序数据。采用稀疏非线性动力学识别（SINDy）结合最小二乘回归，构建了统一的非线性模型，捕捉SOC(t, p)关系。该框架支持类似机器人的能量感知规划，并可扩展至任意初始SOC及环境依赖参数，适用于真实世界部署。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。