Simulation of battery management system using CANoe

Abstract

The rapid electrification of the automotive sector has increased demand for early-stage validation tools that can verify the behaviour of battery-related electronic control units before physical prototypes are available. This thesis presents the design and implementation of a simulation environment for a Battery Management System (BMS) built on the Vector CANoe platform, developed in collaboration with Improve Engineering. The simulation environment models a distributed battery network consisting of sixteen Battery Pack Controller (BPC) nodes communicating over a CAN FD bus. The network architecture is formally described using an AUTOSAR XML system description (ARXML), which defines all ECU nodes, CAN frames, Protocol Data Units (PDUs), and individual signals. Active ECU behaviour is implemented for one BPC node (BPC_00) using CAPL (Communication Access Programming Language), while the remaining fifteen nodes provide the structural topology of a scalable distributed battery system, supported by a pre-existing Vector simulation environment. Battery-related signals, including minimum and maximum cell voltages, pack voltage, temperature, capacity, pack energy, and state of charge, are generated dynamically within the active CAPL node and transmitted at regular intervals over the CAN network. A fault detection subsystem monitors temperature and cell voltage imbalance, triggering internal alert states that are visualised through a custom CANoe panel. Diagnostic services based on the Unified Diagnostic Services (UDS) protocol are implemented using a CANdela diagnostic description file, enabling the simulated ECU to respond to requests for ECU identification, serial number, battery voltage, and odometer readings. Verification of the simulation is performed using CANoe monitoring tools, including the Trace Window, the Diagnostic Console, and logged measurement data. Analysis of measurement log files confirms that all sixteen BPC nodes transmit CAN FD frames at a mean cycle time of 100.00 ms across a 21-second observation period, demonstrating correct and deterministic communication behaviour. Fault alerting and diagnostic response handling are confirmed through targeted test scenarios. The results demonstrate that a structured, AUTOSAR-based CANoe simulation environment can effectively serve as a validation platform for CAN communication, diagnostic services, and fault detection logic in early automotive development phases.