Drivetrain Smart Sensor System Overview

• The drivetrain smart sensor system is an integrated framework combining various MEMS sensors, real-time data acquisition, and embedded processing to monitor tire–road interactions.
• It employs acoustic, inertial, temperature/pressure, and battery sensors with high-throughput wireless communication to achieve sub-millisecond data capture and reliable signal transmission.
• The system underpins adaptive traction control, digital twin synchronization, and predictive maintenance by utilizing advanced state estimation and machine learning for enhanced energy efficiency and safety.

A drivetrain smart sensor system is an integrated framework that combines advanced sensing, real-time data acquisition, embedded processing, and wireless communication to directly monitor and interpret the physical interaction between a vehicle’s drive system and the road or terrain. These systems constitute the technological foundation for next-generation traction control, health monitoring, and digital-twin applications in automotive and heavy-duty vehicle contexts. They typically unite heterogeneous sensing modalities—including acoustic, inertial, temperature, pressure, load, and electrical state sensors—and leverage high-throughput communication platforms, estimation theory, and machine learning to yield actionable insights for both onboard control and remote diagnostics (Yordanov et al., 4 Sep 2025, Kobelski et al., 2020, Dogan et al., 2019).

1. Sensor Architectures and Signal Domains

Modern drivetrain smart sensor systems often deploy fully integrated, wheel-mounted sensor packages combining multiple MEMS devices managed by a real-time microcontroller (notably, the ESP32 dual-core SoC running FreeRTOS with Micro-ROS and EmbeddedRTPS/DDS middleware (Yordanov et al., 4 Sep 2025)). The sensor suite may include:

• Acoustic module (AM, e.g., VM2020): PDM acoustic sensor (sampling at $f_{AM}=32\,000$ Hz, 32-bit precision) mounted on the inner liner, capturing structural and cavity-borne vibration modes relevant to tire–road interaction.
• Inertial Measurement Units (ICM-20649 IMU): 3-axis accelerometer and 3-axis gyroscope (sampling at $f_{IMU}=562.5$ Hz, 16-bit), fixed to the rim to resolve local deformation and rotational dynamics.
• Temperature and Pressure (MS5803-14BA TP): Combined sensor (sampling at $f_{TP}=5$ Hz, 32-bit) in the valve stem, supporting thermal/inflation state estimation.
• Battery state-of-charge monitor (BSoC, 12-bit, 1 Hz): Supervises onboard Li-ion cell health.

Data from the AM is acquired via I²S/PDM, IMU via SPI, and TP/BSoC over I²C/ADC. In prototyping, core 2 of the ESP32 handles acquisition; core 1 orchestrates Wi-Fi and DDS-based publish–subscribe orchestration (Yordanov et al., 4 Sep 2025). Sensor readout rates are systematically mapped to computational priority to maintain sub-millisecond jitter (Table 1).

Sensor	Precision	Sample Rate	Raw Data Rate
AM (VM2020)	32 bit	32,000 Hz	1,024 kbit/s
IMU	16 bit	562.5 Hz	54.0 kbit/s
TP	32 bit	5 Hz	0.32 kbit/s
BSoC	12 bit	1 Hz	0.012 kbit/s

Auxiliary sensor modules in heavy-duty applications can encompass wheel–speed encoders, drive–torque sensors, vertical suspension force sensors, and drawbar-pull sensors. Smart sensing for road-type recognition may utilize acoustic microphones positioned near the driven wheels and digitized at 44.1 kHz/16 bit (Dogan et al., 2019).

2. Communication and Real-Time Data Acquisition

Smart sensor systems require robust, high-bandwidth, low-latency comms for multi-sensor data streaming. IEEE 802.11n Wi-Fi is adopted for ≥1 Mbit/s throughput, outclassing Bluetooth piconet brokers in scalability and latency. Data is transmitted over TCP/IP (Espressif's LwIP stack), with EmbeddedRTPS providing ultra-lightweight DDS/RTPS real-time peer-to-peer publish–subscribe patterns (Yordanov et al., 4 Sep 2025). Each sensor publishes a well-defined ROS/DDS topic, e.g.,:

• /wheel/AM (acoustic data)
• /wheel/IMU (inertial data)
• /wheel/TP (temperature and pressure)
• /wheel/BSoC (battery state)

Message structuring provides explicit sequence numbers and nanosecond timestamps, supporting reliable, in-order delivery with DDS QoS "reliable", obviating the need for a central broker.

System throughput is the sum of all sensor channels:

$R = \sum_i f_{s,i} \cdot S_i$

For the reference prototype, the system achieves $R \approx 1.033 \times 10^6$ bit/s (~129 kB/s payload, rising to ~150 kB/s on the wire with headers).

Testing on a tire drum rig (load 2.6–9.2 kN, speed up to 100 km/h, 1 Hz–32 kHz sampling) demonstrates ≤0.1% packet loss and negligible jitter (<1 ms), both with RAM-cached and disk-logging receiver modes. Timing analysis yields mean–min–max inter-message intervals tightly tracking theoretical sample periods (Yordanov et al., 4 Sep 2025).

3. Data Processing, Estimation, and Mapping

Drivetrain smart sensor systems implement both classical and machine-learning-based estimators for traction, terrain, and friction parameters:

• Adaptive Unscented Kalman Filtering (AUKF) processes torque, speed, force, and drawbar signals to estimate state vectors $x_k$ and unknown parameters $(\mu_i, \rho_s)$—tire–road adhesion and soil resistance—using a nonlinear state-space model. The AUKF deploys standard 2n+1 sigma points and adaptively tunes process covariance $Q$ by a fuzzy-logic supervisor responsive to vehicle dynamics (Kobelski et al., 2020).
• Road-Type Estimation (ARTE) leverages windowed acoustic features (linear-predictive coefficients, band energies, cepstral values) distilled into a 7-dimensional feature vector, processed by an MLP or SVM for real-time surface classification (asphalt, gravel, stone, snow) with ~85% accuracy (MLP) or perfect true-positive rates (SVM, at the cost of higher false positives) (Dogan et al., 2019).

Resultant traction parameters $(\mu, \rho_s)$ are interpolated onto spatial maps (cellwise update, 1 m × 1 m grids, weighted neighborhood smoothing), revealing hard transitions and accurate field delineations (≤5% mean absolute error, R² of 0.86–0.99 vs. ground truth) (Kobelski et al., 2020).

Friction estimates obtained from ARTE are linked to curated $\mu$–$\lambda$ lookup tables parameterized by slip ratio $s$, supporting direct torque command synthesis for TCS loops (Dogan et al., 2019).

4. Integration with Control and Digital Twin Systems

Smart sensor systems interface directly with advanced control modules and holistic vehicle models:

• Traction Control (TCS): Sensor-driven TCS employs torque controllers (Model-Following, Slip-Ratio PI, Maximum Transmissible Torque Estimation) that close feedback on real-wheel and chassis velocity, using online road-type classification and $\mu(s)$ curve selection. ARTE-driven TCS delivers substantial reductions in slip ratio (e.g., SRC: from 0.0654→0.0237), torque/energy (SRC: 258.2→33.4, −87%), and improved stability margins (SRC gap: 0.91→0.28) (Dogan et al., 2019).
• Digital Twin Synchronization: DDS-published sensor streams are aggregated via MATLAB/ROS Toolbox, synchronized by timestamps, and archived as rosbag files. These are ingested into a digital twin model for real-time estimation of tire–road interaction: acoustic spectra inform contact-patch friction, high-rate IMU data supports force prediction, and TP data refines thermal/inflation state models. This infrastructure supports health monitoring (e.g., detection of under-inflation, delamination), adaptive vehicle control, and predictive maintenance (Yordanov et al., 4 Sep 2025).
• Fleet/Vehicle Scaling: Each ESP32 node is a DDS participant; scaling is achieved by assigning namespace topics per wheel (e.g., /vehicle/front_left/IMU), enabling fleet-level deployments with seamless data fusion (Yordanov et al., 4 Sep 2025).

5. Performance Metrics, Validation, and Practicalities

Real-time operation is maintained by mapping sensing priority to task scheduling in FreeRTOS (tick rate 1 kHz), using ring buffers to decouple interrupt-level acquisition from network stack communication. Lock-free inter-core queues (ESP32) prevent network stack bottlenecks from impacting sample collection (Yordanov et al., 4 Sep 2025).

Measured streaming achieves consistent message gaps (e.g., mean 6.999 ms for AM at 32 kHz), with observed packet loss ≤0.1% under high-throughput conditions (Yordanov et al., 4 Sep 2025). In mapping scenarios, the mean absolute error of real-time adhesion $\mu$ estimation is ≤5%, and cellwise R² for soil-type boundaries reaches up to 0.996 (Kobelski et al., 2020). Acoustic road-type recognition maintains >95% accuracy under additive noise, with robust detection of abrupt surface transitions (<0.2 s latency) (Dogan et al., 2019). Energy and robustness gains in TCS scenarios using ARTE are observed across all control strategies (slip reduction up to 75%) (Dogan et al., 2019).

6. Applications and Research Context

Drivetrain smart sensor systems are pivotal in the development of digital-twin-enabled mobility, energy optimization for heavy-duty vehicles, and robust, low-cost traction control for EVs (Yordanov et al., 4 Sep 2025, Kobelski et al., 2020, Dogan et al., 2019). By directly exposing the tire–road interface and enabling real-time parameter mapping at the road surface, these systems facilitate terrain-adaptive operation, predictive maintenance, health monitoring, and significantly lower energy consumption.

Deployment scenarios range from fully instrumented test vehicles (drum rigs, direct-drive EVs, agricultural tractors) to production-level architectures where wheel-embedded wireless nodes autonomously ingest and broadcast high-dimensional sensor streams. Such integrated frameworks represent a convergence of embedded systems, robust communications, estimation theory, and machine-learned analytics within the automotive and heavy-duty drivetrain landscape.