Hierarchical Battery Pack Integration and Parametric Analysis for eBAJA Electric All-Terrain Vehicle Powertrain Simulation in MATLAB/Simulink

Abstract

This paper presents the design, implementation, and parametric analysis of a hierarchical battery pack integration for the eBAJA electric all-terrain vehicle (eATV) powertrain simulation in MATLAB/Simulink. Working from the MathWorks eBAJA reference model, the lumped equivalent circuit model (ECM) battery is replaced with a hierarchical Battery Builder pack comprising five series-connected module assemblies (4S16P cell configuration), each containing a first-order Thevenin ECM cell parameterised with SOC-dependent open-circuit voltage, terminal resistance, and RC polarisation branch. A signal instrumentation framework enables pack-level monitoring and per-module acquisition of SOC, voltage, current, and cycle-related parameters. An energy analytics subsystem integrates instantaneous pack power to yield cumulative energy consumption in kWh with dual-method range estimation. The vehicle model is reparameterised with competition-representative specifications: 270 kg total mass, 72 V nominal pack voltage, 80 Ah capacity (5.76 kWh), 8:1 gear reduction, and 8.4 kW peak motor power. A 1000 s simulation under a custom 600 s ATV competition drive cycle confirms stable pack operation. Cumulative energy consumption reached 0.149 kWh, and the linear range estimator projected a full-charge range of approximately 95 km. A parametric study covering gear ratio, vehicle mass, and battery configuration provides quantitative design guidance for competition vehicle development. The work was shortlisted among the Top 10 teams in the MATLAB simulation round of the MathWorks eBAJA Challenge 2026.

Introduction

This research focuses on enhancing the MathWorks eBAJA electric ATV simulation model by improving battery modeling, energy monitoring, and range estimation for off-road competition vehicles. Existing eBAJA models use a simplified lumped battery model that provides limited information about battery performance under demanding competition conditions.

The proposed system replaces the conventional battery with a hierarchical five-module battery pack created using the MathWorks Battery Builder toolbox, allowing detailed monitoring of voltage, current, and state of charge (SOC) at both pack and module levels. Additional instrumentation enables real-time battery diagnostics and supports Battery Management System (BMS) analysis. An energy analytics subsystem is also introduced to calculate cumulative energy consumption and estimate remaining driving range using both SOC-based and energy-based methods.

The vehicle model is updated with realistic eBAJA specifications, including a 72 V, 80 Ah battery pack, 8.4 kW motor, 270 kg vehicle mass, and an 8:1 gear ratio. Mathematical models describing vehicle dynamics, battery behavior, power consumption, and range estimation are integrated within the Simulink environment.

Simulation results demonstrate stable vehicle performance, accurate velocity tracking, reliable battery operation, and enhanced visibility of key parameters such as voltage, current, torque, energy usage, SOC, distance traveled, and estimated range. The model consumed approximately 0.149 kWh during a 1000-second competition drive cycle while maintaining stable battery discharge characteristics.

A parametric study evaluated the effects of gear ratio, vehicle mass, and battery configuration. Results showed that an 8:1 gear ratio provides the best balance between tractive force, efficiency, and competition performance. Increasing vehicle mass reduced energy efficiency and hill-climbing capability, while larger battery configurations increased available range.

Conclusion

This paper has presented, implemented, and analysed a hierarchical battery pack integration framework for the eBAJA electric all-terrain vehicle Simscape simulation platform. The key contribution is the replacement of a single lumped ECM battery block with a five-module Battery Builder hierarchical pack (4S16P cell configuration), expanding battery observability from one signal to 39 per-cell and per-module states. Pack-terminal voltage and current sensing, kWh energy integration, distance tracking, and dual-method range estimation collectively address the observability, analytics, and visualisation gaps identified in the eBAJA simulation literature. The simulation results indicate stable vehicle operation throughout the analysed drive cycle. Pack terminal voltage remained within the expected operating range for a near fully charged 20-series lithium-ion battery pack, peak discharge current remained within acceptable limits, and the motor operated consistently with the expected torque–speed characteristics. The hierarchical battery architecture significantly improved system observability by enabling detailed monitoring of voltage, current, energy consumption, state-of-charge, travelled distance, and range estimation. During result analysis, a discrepancy was identified between the reported SOC depletion and the depletion implied by measured energy consumption. This observation suggests that further verification of battery model parameterisation and SOC estimation behaviour is warranted. Consequently, SOC-dependent metrics and range estimates should be interpreted within the context of the present simulation model. Future work will focus on refining battery calibration, quantifying controller tracking performance using objective metrics, and extending the framework through comparative drivetrain and range-estimation studies. The framework provides eBAJA teams with a simulation-ready tool for powertrain design, battery sizing, and BMS observability architecture development.

References

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Copyright

Copyright © 2026 Mrunal Dhawade, Parth Dhage. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.