Making thermal behaviour dependable in an electric truck
Electric-truck thermal management is a cross-system control problem: batteries, power electronics, charging, cabin climate, and auxiliary actuators must stay within physical limits while the vehicle remains usable. The work translated those strategies into production-oriented embedded software and validation evidence.
Overview
Coordinating thermal behaviour across a production electric truck
The work connected battery, charging, auxiliary, and power-electronics functions through model-based embedded control. It linked system intent to real-time software, diagnostics, calibration, and evidence from simulation through vehicle testing.
Focus: Battery, charging, and thermal coordination
Scope: Control architecture, embedded software, diagnostics, and validation
Leading control architecture across battery and thermal systems
Led control-software architecture and integration for battery and thermal-management functions, translating model-based strategies into real-time embedded implementation. Ownership covered controller structure, diagnostics, calibration maturity, ECU integration, supplier interfaces, and validation planning.
Developed control and diagnosis concepts for passive and active cooling across batteries, electric motors, power electronics, cabin climate, fans, heat pumps, valves, pumps, compressors, and chargers. Also contributed to estimation-oriented battery functions related to state of charge, state of power, and state of health.
Collaborators: Cross-functional work with software, systems, controls, electronics, testing, calibration, vehicle, supplier, and wider R&D teams across the truck programme.
Tools & methods
Turning physical limits into deterministic software
Software had to respect thermal limits, sensor and actuator behavior, real-time timing, charging conditions, vehicle duty cycles, and safety-related requirements. Evidence needed to remain traceable from model behavior and generated software through bench, wind-tunnel, test-track, and on-road validation.
Risks considered: The main risks were model-to-vehicle mismatch, limited fault observability, integration issues across supplier interfaces, and unsafe or inefficient fallback behavior under degraded conditions.
Moving from model-based strategy to vehicle evidence
Process
Started with system intent and functional requirements, developed and calibrated model-based control logic, integrated real-time software, and iterated across MIL, SIL, HIL, bench, wind-tunnel, test-track, and on-road testing. Data logging, experiments, analysis, debugging, issue tracking, and calibration-maturity reviews connected each stage.
Key decisions
Modularized the architecture around thermal strategies, supervision, actuator coordination, diagnostics, and fallback behavior. Model-based development and continuous integration were used to surface logic and integration issues before HIL and vehicle testing.
Delivering software, diagnostics, and validation evidence
Production-oriented C++ and model-based software; controller architecture and integration artifacts; diagnostic and fallback behavior; calibration and data-analysis support; ECU-ready software; and validation evidence across simulation, bench, and vehicle environments. The work also supported functional-safety analysis and patent-application activity.
Evidence of release-ready engineering
- Production context
- Heavy-duty EV
- Safety context
- ISO 26262 ASIL-B
- Validation path
- MIL / SIL / HIL to vehicle
- Control scope
- Battery, charging, and thermal systems
System-level control leadership in a production environment
This work demonstrates system-level control leadership: connecting physical limits and uncertain sensor evidence to software architecture, diagnostics, validation, and delivery.
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