Anatomy of an Electric Wheel Loader - Design & Maintenance

Abstract

The successful adoption of electrified Non-Road Mobile Machinery (NRMM) on a commercial scale is critically dependent on ensuring the reliability of its highcost electric powertrain components. This neccesitates understanding the pattern of system-level interactions that result in a slue of cascading failures. However, a significant research gap exists in this particular area of understanding systemlevel failure dynamics, where failures arise not from isolated component breakdowns but from the complex, multi-physics interactions between subsystems under harsh, variable operating conditions. This thesis addresses this challenge by establishing a foundation for practical and robust Predictive Maintenance (PdM) strategies for an Electric Wheel Loader (EWL). The research employs a holistic, systems-level investigation that deconstructs the EWL’s operational anatomy through a phase-byphase analysis of the standardized V-type work cycle. This methodology quantifies the unique and often conflicting demands placed on the powertrain during distinct operational phases. Key findings present an optimized EWL design centered on an asymmetric dualmotor powertrain. This architecture assigns a robust, high-torque motor to the front axle to handle the shock loads of digging, and a high-efficiency motor to the rear axle for energy-optimized transport. The design is mechanically amplified by differentiated transmission ratios, providing a torque advantage to the front axle. Complementing this advanced design, the thesis introduces a novel ”context-aware” PdM framework. This PdM framework dwells on initially identifying the machine’s current work cycle phase using kinematic data. With the operational context established, the system applies distinct, phase-specific anomaly detection models to fused multi-modal sensor data. This dramatically reduces false alarms and sensitivity to incipient faults. Ultimately, this work delivers an integrated technical blueprint for the design of a next-generation EWL and its intelligent health management system. The symbiotic relationship between the purpose-built hardware and the context-aware maintenance strategy provides a pathway to enhanced efficiency, stability, and reliability, establishing a foundational architecture for the future development of autonomous wheel loaders and intelligent fleet management systems.