Motion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models

Joonhee Kim; Sanghyun Park; Donghyeong Kim; Eunseon Choi; Soohee Han

cs.ROMar 13, 2026

Motion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models

Authors: Joonhee Kim, Sanghyun Park, Donghyeong Kim, Eunseon Choi, Soohee Han

Organizations: Department of Convergence IT Engineering, Pohang University of Science and Technology, Choengam-ro 77, Nam-gu, Pohang-si, Gyeongsangbuk-do, Republic of Korea. · Department of Electrical Engineering, Pohang University of Science and Technology, Choeng-am-ro 77, Nam-gu, Pohang-si, Gyeongsangbuk-do, Republic of Korea.

Abstract

Quadrotor endurance is ultimately limited by battery behavior, yet most energy aware planning treats the battery as a simple energy reservoir and overlooks how flight motions induce dynamic current loads that accelerate battery degradation. This work presents an end to end framework for motion aware battery health assessment in quadrotors. We first design a wide range current sensing module to capture motion specific current profiles during real flights, preserving transient features. In parallel, a high fidelity battery model is calibrated using reference performance tests and a metaheuristic based on a degradation coupled electrochemical model.By simulating measured flight loads in the calibrated model, we systematically resolve how different flight motions translate into degradation modes loss of lithium inventory and loss of active material as well as internal side reactions. The results demonstrate that even when two flight profiles consume the same average energy, their transient load structures can drive different degradation pathways, emphasizing the need for motion-aware battery management that balances efficiency with battery degradation.

Figures & tables

Electrochemical parameters
Symbol	Description
$\theta_{p}^{\text{max}},\theta_{p}^{\text{min}},\theta_{n}^{\text{max}},\theta_{n}^{\text{min}}$	Stoichiometry limit
$R_{p},R_{n}$	Solid particle radius
$\sigma_{p},\sigma_{n}$	Solid-phase conductivity
$\epsilon_{p},\epsilon_{\text{sep}},\epsilon_{n}$	Electrolyte porosity
$\epsilon_{f,p},\epsilon_{f,n}$	Filler fraction

TABLE I: Battery model parameters to be estimated

Fig. 1 : Pipeline of motion-specific battery health assessment: reference tests calibrate an electrochemical model, flight data are acquired with a custom module, and virtual experiments evaluate degradation.

Fig. 2 : Experimental setup of the battery-in-the-loop system, consisting of a programmable power supply, an electronic load, and a battery pack under test.

Fig. 3 : Calibration results of the battery electrochemical model. (Top) Comparison between measured and simulated voltage responses under 0.1 C, 2 C, and pulse reference performance tests. (Bottom) Corresponding error profile, with segments distinguished by load conditions.

Fig. 4 : Custom current sensing module integrated into the quadrotor platform. The module consists of a wide-range current sensor, an STM32 Nucleo board for control and data processing, and an SD card module for onboard data logging.

Quadrotor specification
Parameter	Description
Frame	S500 Quadrotor
Controller	Pixhawk 6C
Motor	DJI NAZA 2212 Brushless Motor 920KV
Propeller	DJI 1045 propeller
Weight	975 g

TABLE II: Detailed specifications of the quadrotor platform and the 4S1P battery pack employed in this study.

Fig. 5 : Procedure of extraction and processing of motion-specific current profiles from quadrotor flight data. (a) Raw current signal with a moving-average filter. (b) Segmentation of the filtered data. (c) Periodic reconstruction of the segmented profiles. (d) Simulated voltage responses under the reconstructed profiles.

Fig. 6 : Motion-aware battery health assessment results under motion-specific (stars) and constant-current (circles) profiles with C-rates of 16, 18, 20, and 22. (a) Normalized degradation modes (b) Normalized capacity loss induced by side reactions.

Quadrotor specification
Parameter	Description
Frame	Custom carbon frame (arm length 300mm)
Controller	Custom
Motor	EMAX ECO2 2306 1700KV
Propeller	Gemfan 7040-3 7inch
Weight	455 g

TABLE III: Specification of the secondary quadrotor platform

Fig. 7 : Health assessment results for a secondary 455 g quadrotor platform. (a) Hardware configuration. (b) Segmented motion-specific current profiles. (c) Normalized degradation modes.

Lithium-Ion Batteries

Lithium-ion batteries are rechargeable energy-storage systems studied through their materials, performance, aging, safety, and recycling.

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Sep 10, 2026J/K move · Enter open · S save

Battery-Aware Predictive Trajectory Planning and Control for Multirotors Under Disturbances

Krishna Bhavithavya Kidambi

department of Mechanical and Aerospace Engineering at University of Dayton, Dayton, OH.

Trajectory Optimization Model Predictive Control UAV Trajectory Optimization Quadrotor Control Motion Planning

Open paper AskPDF arXiv

This paper presents a battery-aware predictive trajectory-planning and control framework for multirotors operating under spatially localized disturbances. Candidate trajectories are evaluated through closed-loop vehicle--motor--battery propagation, allowing disturbance-induced control demand, electrical energy, battery evolution, and terminal-voltage-dependent actuator capability to enter the planning process. % A reduced-order battery model is numerically benchmarked against an independently implemented Simscape equivalent-circuit reference, with a power NRMSE of

0.64\%

and a cumulative-energy discrepancy below

0.7\%

. % In a

150

-s,

640

-m mission containing three disturbance regions, the selected trajectory reduces electrical energy consumption by

7.46\%

and position-tracking RMSE by approximately

72\%

relative to the disturbance-aware fixed-reference baseline. % Planner ablations show that battery-dependent terms are nonbinding at nominal SOC but alter the selected trajectory under a depleted-battery stress condition. % Execution with multiple feedback controllers further demonstrates that controller selection changes the tradeoff among tracking accuracy, energy consumption, and actuator utilization. % The results demonstrate the benefit of accounting for predicted closed-loop energetic and battery--actuator consequences during trajectory selection.

Abstract

Lithium-Ion Batteries

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