2 Wheeler Wheel Based Dyno With Driving Cycle Simulation
2 Wheeler Wheel Based Dyno With Driving Cycle Simulation - Image 2
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2 Wheeler Wheel Based Dyno With Driving Cycle Simulation

SKU EV_023

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Description

Country of Origin Made in India
Platform Type Wheel-based Chassis Dyno for 2-Wheeler (rear-wheel drive)
Loading System Hub Motor-based Variable Load (900W/1200W/1500W selectable)
Battery Pack 15Ah Li-Ion Battery with SMPS Charger
Controller Open Programmable Controller with Load Simulation & Code Upload Features
Transmission Display Full EV Transmission Layout with Component Labels
Drive Cycles Supported MIDC, FTP-72, FTP-75, WHVC, WHTC
Data Acquisition System Field Programmable DAQ - Real-time data logging, visualization, and export
This wheel-based chassis dynamometer is an advanced training and testing platform designed to simulate real-world road conditions for electric 2-wheelers. Built on a hub motor-based variable load system, it enables users to test performance, efficiency, and thermal behavior of EV drivetrains under programmable drive cycles such as MIDC, FTP-75, and WHTC.

The setup includes an open-source programmable controller, Field Programmable Data Acquisition System, and a field-configurable BMS, making it ideal for academic labs, EV skill centers, and R&D institutions. The system also supports MATLAB, Python, and custom code uploads, with a rich UI for live data analysis and export.

Key Features:

  • Full-Scale Drive Testing: Replicates on-road conditions and load responses on actual 2W drivetrain.
  • Smart User Interface: Real-time data acquisition with export, analysis, and visualization tools.
  • Programmable Drive Cycles: Simulate standard cycles (MIDC, FTP-75, WHVC, etc.) for academic experiments or industry benchmarks.
  • Open Architecture Controller: Supports embedded control training, algorithm tuning, and programming experiments.
  • Thermal & Load Testing: Analyze component behavior at varying loads, temperatures, and acceleration levels.
  • Research-Ready: Integrated scripting and simulation using MATLAB, Python for EV algorithms, SOC estimation, and motor control.
  • Battery Flexibility: Interchangeable and programmable BMS for different chemistries and use cases.
  • Modular & Expandable: Easily upgradable with new sensors, drive cycles, and data analysis tools.

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