Grigorii's Portfolio

CAN Bus for Electric Go-Kart

Problem Statement

The electric go-kart development platform required reliable communication between distributed electronic subsystems (VCU, BMS, steering, drivetrain) for safe operation and realistic vehicle simulation.

What I did

I contributed to the collective project of E-Go Electric Cart developed by the students of Rhine-Waal University of Applied Sciences.

My part was to:

Design CAN Bus communication protocol for the Battery Management System of the cart, ensuring that all the values: velocity, steering wheel angle, etc. are sent and recieved correctly
Revise the previous models developed by other students.
Enhance Simulink and CAN Bus integration.

As a result:

Developed a structured CAN communication scheme in Simulink enabling reliable transmission of key vehicle signals such as throttle position (mapped to velocity), steering angle, and BMS parameters (voltage and current).
Improved model consistency by refining previously developed subsystems and enhancing CAN-Simulink integration for clearer signal flow and easier future expansion.
Implemented signal scaling (LSB resolution, offsets, ID structuring) to ensure accurate physical value representation within limited CAN payload sizes for the easier future scalability of the project.
Added persistent fault-detection logic for communication monitoring, reducing false warnings caused by transient disturbances and improving robustness of simulated vehicle control communication prior to hardware testing.

Project Overview

This project focuses on transforming a former pedal-driven go-kart into a fully functional electric mobility development platform for teaching, research, and student engineering projects. Initiated in 2022 by faculty members from the Faculty of Technology and Bionics, the primary goal was to increase hands-on practical experience beyond standard coursework and stimulate student curiosity and innovation.

Over several years of collaborative student development, the kart has evolved into a system comparable in structure to a modern electric vehicle. Key components include:

Lithium-ion battery system
CAN bus communication infrastructure
Multiple electronic control units
Independent electric motors on all four wheels
Torque-vectoring capability for advanced vehicle dynamics research

This architecture enables realistic experimentation with distributed vehicle control systems and advanced drive functions.

For students in mechatronics, control engineering, or related fields at HSRW, the platform provides a rare opportunity to work on a near-real automotive system. It bridges the gap between theory and practical implementation, especially in areas like embedded control, communication networks, and electric mobility systems.

CAN Bus communication

The implementation focused on transmitting structured CAN messages containing both ID fields and corresponding physical values relevant for vehicle control, diagnostics, and monitoring.

Main Communication Signals Implemented

Throttle input
- Conversion of pedal angle into a percentage of maximum vehicle velocity
- Encoded into CAN frames for drivetrain control simulation
Steering angle feedback
- Transmission of steering angle signals for vehicle control and modeling tasks
Battery Management System (BMS) communication
- Monitoring of battery voltage and current
- Signals transmitted via dedicated CAN IDs for energy management and safety monitoring

Technical CAN Communication Details

Implementation of CAN frame packaging and unpacking using Simulink subsystems
Assignment of unique CAN identifiers (IDs) to each message type for prioritization and filtering
Signal scaling using to convert physical values into integer CAN payload data.
For example, physical quantities scaled to fit into 8- or 16-bit CAN signals with defined resolution
Use of typical CAN signal conventions:
- Offset and scaling factors for physical unit representation
- Bit positioning inside CAN payload bytes
- Consideration of message timing and update rates
Simulation of realistic transmit/receive behavior similar to distributed automotive ECUs

Implementation Approach in Simulink

Modular subsystems for:
- CAN message encoding (signal → CAN frame)
- Transmission over simulated CAN network
- Reception and decoding back into physical signals
Structured architecture allowing easy integration
Testing of communication reliability and signal consistency prior to hardware implementation

This work provided hands-on experience with automotive communication protocols and strengthened the project’s capability to simulate realistic electric vehicle control architectures before physical testing.

Fault Signal Handling

The implemented logic combines multiple potential transmission fault signals using an OR block, passes the result through a chain of memory (delay) blocks to track signal persistence over several time steps, and evaluates them with an AND block to generate a TX warning only when a communication issue persists rather than appearing as a short disturbance.

This approach improves robustness of distributed vehicle control communication between components such as the vehicle control unit, battery management system, and motor controllers, helping prevent false alarms while supporting safer and more reliable system operation.

Challenges I Faced

Signal scaling and LSB definition: ensuring correct conversion between physical values and CAN payload data without precision loss
Message timing and synchronization: maintaining stable communication rates to avoid inconsistent control responses
Debugging communication chains: identifying whether issues originated from signal conditioning, encoding, or subsystem interactions
System modularity: designing subsystems flexible enough for future expansion while keeping the model manageable

References and Links

This project is a part of Controls Lab at Rhine-Waal University of Applied Sciences. Every content mentioned belongs to the mentioned institution.

LinkedIn | +1 (917) 916 4549 | Brooklyn, NY 11226 | feduakov17@gmail.com

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