What question did this study set out to answer?

The aim is to develop a predictive system for temperature control and torque adaptation of electric two-wheeler motors.

March 16, 2026Open Access

Predictive temperature control of electric two wheeler hub motor using gradient aware neural regulation with degradation tracking and fault tolerant multi condition torque adaptation

Key Points

The aim is to develop a predictive system for temperature control and torque adaptation of electric two-wheeler motors.
Introduced a Hybrid Gradient-Aware Neural Regulation framework for motor temperature control.
Implemented a Motor Health Index for tracking thermal degradation.
Developed a multi-condition torque derating logic based on environmental and operational factors.
Integrated a physics-based thermal model with a neural network for real-time adjustments.
Conducted detailed simulations using various driving profiles and thermal cycles.
Torque derating was delayed by up to 14%.
Energy efficiency improved by approximately 7.4%.
Achieved a prediction RMSE below 2.1 °C in multiple sensor failure scenarios.

Abstract

Thermal stress in electric two-wheeler hub motors is a major bottleneck affecting drivetrain reliability, safety, and efficiency-especially under varied terrain, aggressive riding profiles, and sensor reliability degradation. This paper presents a novel Hybrid Gradient-Aware Neural Regulation (GANR) framework designed to enable predictive motor temperature control, real-time thermal degradation monitoring, and fault-tolerant torque shaping. The system integrates three distinct yet interlinked control layers: (i) gradient-sensitive neural temperature estimation, (ii) cumulative thermal damage indexing through a Motor Health Index (MHI), and (iii) multi-condition torque derating logic that adapts to ambient temperature, health state, and usage intensity. The controller combines a physics-based thermal model with a lightweight neural network estimator capable of learning under sensor drift, latency, or complete failure. A fallback and safety-mode flowchart ensure robust operation by switching to boundary-scaled torque caps and enabling a real-time decision layer that balances energy consumption, performance demand, and thermal safety. The system's intelligence also extends to an ambient-aware derating boundary and a thermal margin estimator to prevent runaway heating events. A detailed simulation framework is developed using real vehicle specifications, encompassing city, aggressive, and worst-case thermal cycles. Results reveal that the proposed control system can delay torque derating by up to 14%, improve energy efficiency by ~ 7.4%, and achieve prediction RMSE below 2.1 °C even under multiple sensor fault scenarios. In addition, a full real-time deployment analysis is presented using a mid-tier embedded platform with CPU-cycle, memory, and scheduling footprint assessments. This study offers an original, fault-resilient, and hardware-feasible solution to a largely unaddressed domain-intelligent thermal-aware torque control in electric two-wheelers, creating a new pathway toward smarter, safer, and more robust electric mobility systems.

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