3‑Axis Magnetometer

3‑Axis Magnetometer — triaxial magnetometer for geomagnetic vehicle detection and single‑space parking

Practical, procurement‑oriented primer for municipal parking engineers, integrators and procurement teams. This article focuses on how a 3‑axis (triaxial) magnetometer senses vehicle presence, how it fits into a complete parking node, the real battery‑life drivers (radio + message cadence), and field evidence from multi‑site pilots.

Why a 3‑Axis Magnetometer matters in smart parking

A 3‑axis magnetometer measures X/Y/Z components of the local magnetic field and detects disturbances caused by ferromagnetic vehicle mass — giving robust single‑space detection while requiring minimal optics and wiring. For on‑street single‑space detection it is typically the most reliable low‑profile sensor because it is resilient to light and precipitation, has a tiny footprint, and is low‑power when duty‑cycled correctly. For city planners, the overall market context matters: the installed base of connected ground parking sensors is growing fast (Berg Insight forecasts the installed base to reach ~3.2M units by 2028), which increases procurement pressure to require clear battery‑life disclosure in tenders. (iotbusinessnews.com)

Key implication: the magnetometer IC is only one part of the energy budget — the radio (and its configuration: SF/DR, Tx power, ADR/PSM/eDRX, ACK behaviour) determines node lifetime for field installations. See the radio design guidance below and the LoRaWAN spec for how ADR and link-layer timing affect energy usage. (lora-alliance.org)

Standards and procurement checklist

When you write a tender or technical spec, require the vendor to publish measured values (not only marketing claims) and to provide the assumptions used to compute battery‑life. The brief procurement table below shows the common standards and why they matter.

Types of 3‑Axis Magnetometer sensor ICs

• Hall‑effect / Triaxis (low‑power, programmable duty cycle): common in single‑space nodes because they balance sensitivity and ultra‑low standby currents. Use IC datasheets (e.g., Melexis MLX90395) for power budgeting — IC standby and run currents feed your battery model. (melexis.com)
• Magneto‑resistive (AMR/GMR / MI): higher sensitivity, good when background noise is high but typically costlier.
• Fluxgate / Optically pumped magnetometers: extremely sensitive (geophysics), not used for municipal single‑space sensors due to cost and power.
• Hybrids: combine magnetometer + short‑range radar or ultrasonic for edge cases (adjacent stalls, low‑ferrous EVs) — the hybrid approach increases detection accuracy but typically raises average power consumption. See vendor customer stories for hybrid adoption. (acconeer.com)

Key design axes when selecting an IC: sensitivity vs noise floor, programmable duty‑cycle, supported interfaces (SPI/I2C), temperature range (–40°C..+125°C for automotive variants), and documented current figures you can use in a node energy model. Use the IC datasheet figures as deterministic inputs when you compute node lifetime. (melexis.com)

System components (single‑space node)

Typical block diagram and procurement notes:

• Magnetometer IC (triaxial) — the physical X/Y/Z sensor and its baseline drift characteristics.
• Low‑power MCU (wake/duty control) — implement event logic & edge filters.
• Radio module (LoRaWAN / NB‑IoT / Sigfox / BLE for commissioning) — choose based on coverage and energy profile. See LoRaWAN connectivity and NB‑IoT connectivity design considerations.
• Power (primary cell Li‑SOCl2 D‑cell or alternative chemistries; or rechargeable + energy harvesting). For large deployments, require explicit battery chemistry and measured discharge curves.
• Enclosure & mechanical kit — specify surface-mounted or in‑ground options; require IP68 + IK10 proof.
• Commissioning & calibration tools (Bluetooth provisioning / autocalibration) — demand production tools and OTA firmware update capability (firmware‑over‑the‑air).

How a 3‑Axis Magnetometer node is installed and commissioned — step‑by‑step

1. Site survey & radio planning: map gateway coverage, expected RSSI, and RSSI margin for LoRa/NB‑IoT. Use measured margins in energy model to estimate retransmit rate. See LoRaWAN connectivity. (lora-alliance.org)
2. Select sensor variant (surface vs inground) considering snow, ploughing, flood risk and local maintenance practice. Prefer surface versions where water pooling is likely. See easy installation.
3. Mechanical install: place and orient sensor so X/Y axes align with the lane and follow the manufacturer drill template; avoid mounting near large ferrous objects. Use surface-mounted parking sensor guidance.
4. Power & radio config: define ADR policy, SF, TX power, PSM/eDRX (NB‑IoT) and estimate expected messages/day; include ACK% in the model, then compute battery life from real MCU/radio currents and message cadence. LoRaWAN ADR and regional parameters materially affect energy use. (lora-alliance.org)
5. Local magnetometer calibration: run autocalibration (offset & tilt), zeroing and set detection thresholds; enable self‑calibrating features where available to reduce field service.
6. Commissioning: verify detection across typical vehicles (EVs, SUVs, vans) and worst‑case scenarios. Record a 2–4 week validation log against a ground truth source (video/manual counts) to compute detection accuracy and false positives.
7. Cloud mapping & analytics: map single‑space IDs to zone and enforcement policy; enable parking occupancy analytics for turnover and enforcement insights.
8. Define maintenance & firmware policy: frequency of battery‑health reporting, OTA updates, and scheduled calibration checks. Require remote configuration and sensor health monitoring.

Maintenance and performance considerations

• Battery modelling must treat the whole node (MCU + radio + sensor + periodic telemetry) not only the magnetometer IC. Use IC datasheet currents as inputs to a node model then run scenarios for heavy/medium/light message cadences.
• Thermal cycles and permanent ferrous changes in the environment cause calibration drift — require automated field recalibration to reduce truck rolls. See autocalibration.
• Water/ice covering the sensor affects onboard nano‑radar performance (if used). Design the mechanical and cleaning/maintenance plan accordingly.

Practical rule: always ask vendors for at least three measured battery‑life scenarios (low/typical/high activity) with explicit assumptions (messages/day, SF, TX power, ACK rate, retries) and the raw logs used to generate them.

Typical vendor claims vs realistic field ranges (summary)

Current trends & engineering takeaways

• Hybrid sensing (magnetometer + short‑range radar) is increasingly used to hit >99% accuracy in mixed fleets; customer stories show hybrid adoption for surface devices. (acconeer.com)
• IC vendors now offer micropower triaxial magnetometers with programmable duty cycles; use published µA standby and active mA figures as inputs to the node model. For IC‑level numbers see the MLX90395 datasheet. (melexis.com)
• Standardisation & lab testing (LoRaWAN certification, EN/ETSI tests) are important procurement filters to avoid unrealistic claims. Ask for third‑party test reports and raw battery drain logs.

Key Takeaway — Pardubice 2021 pilot

• Deployment: Pardubice 2021 — 3,676 SPOTXL NB‑IoT sensors deployed (city pilot). Observed / logged life: 1,904 days (~5.2 years). Practical lesson: large NB‑IoT pilots can achieve multi‑year lifetimes when traffic cadence and network settings are conservative and battery chemistry is sized accordingly (see "Referencies" below for project data).

Key Takeaway — Chiesi HQ (Parma)

• Deployment: Chiesi HQ White, Parma — 297 sensors (SPOT MINI + SPOTXL LoRa). Logged life in dataset: 650 days (~1.8 years). Useful reminder: off‑street and indoor projects often pick smaller sensor variants with lower battery capacities — adjust TCO for earlier replacements.

Referencies

Below are selected real projects drawn from our deployments and dataset (short, operational notes):

Pardubice 2021 (Czech Republic)

• Sensors: 3,676 SPOTXL NB‑IoT
• Deployed: 2020‑09‑28
• Logged lifetime (zivotnost_dni): 1,904 days (~5.2 years)
• Notes: Large NB‑IoT deployment; shows that NB‑IoT can deliver multi‑year operation with the right battery sizing and conservative uplink cadence. Link internal management to NB‑IoT connectivity & long battery life.

RSM Bus Turistici (Roma Capitale, Italy)

• Sensors: 606 SPOTXL NB‑IoT
• Deployed: 2021‑11‑26
• Logged lifetime: 1,480 days (~4.1 years)
• Notes: Good example of medium‑scale NB‑IoT deployment for private / dedicated parking assets.

CWAY virtual car park no. 5 (Famalicão, Portugal)

• Sensors: 507 SPOTXL NB‑IoT
• Deployed: 2023‑10‑19
• Logged lifetime: 788 days (~2.2 years)
• Notes: Virtual carpark use case; emphasises the impact of message cadence and local RF conditions on lifetime.

Kiel Virtual Parking 1 (Germany)

• Sensors: 326 (mixed: SPOTXL LORA / NB‑IoT)
• Deployed: 2022‑08‑03
• Logged lifetime: 1,230 days (~3.4 years)
• Notes: Mixed communications and mixed results; demonstrates choice tradeoffs between coverage and per‑message energy.

Skypark 4 — Residential underground parking (Bratislava)

• Sensors: 221 SPOT MINI
• Deployed: 2023‑10‑03
• Logged lifetime: 804 days (~2.2 years)
• Notes: Underground conditions favour sensors optimised for indoor/off‑street use; radio coverage & retries were major battery drivers.

(Full project list with raw fields available in the deployment dataset.)

Frequently Asked Questions

1. What is a 3‑Axis Magnetometer?
   A triaxial magnetic field sensor that measures X, Y and Z components of the magnetic flux; in parking it detects disturbances in the earth’s magnetic field caused by ferromagnetic parts of vehicles. See single‑space detection.

2. How is a 3‑Axis Magnetometer installed and commissioned?
   Installation requires correct orientation, autocalibration (offset & tilt), radio planning, and in‑situ validation across vehicle types. See the step‑by‑step commissioning checklist above and easy installation guidance.

3. How does a magnetometer compare to ultrasonic or camera systems?
   Magnetometers are weather‑resilient, low‑profile and highly privacy‑friendly; ultrasonic or camera systems can cover multiple bays but carry higher maintenance and privacy costs. Hybrid combos (magnetic + radar) are common to address edge cases. (acconeer.com)

4. What battery life should I expect?
   Expect a wide range — vendor claims often say 3–10 years, but rigorous modelling and field logs show realistic ranges from under 1 year to 5+ years depending on radio, message cadence, ACKs and battery capacity. Always request measured scenarios with exact assumptions. (lora-alliance.org)

5. How often should I recalibrate magnetometers?
   Perform initial calibration at commissioning, enable automated field recalibration where possible, and schedule annual checks or after major resurfacing or heavy thermal cycles. Use autocalibration features to reduce truck rolls.

6. Can magnetometers detect EVs and low‑ferrous vehicles reliably?
   Yes — with tuned engine‑section detection algorithms and calibration; hybrid sensors (radar + magnetic) are recommended for universal coverage in mixed fleets. (acconeer.com)

Optimize your parking operation (short action list)

• Require third‑party measured battery‑life scenarios in tenders (explicit: messages/day, SF/DR, TX power, ACK%).
• Request raw battery drain logs for at least 12 months from a comparable climate/cadence pilot.
• Ask for a hybrid fallback strategy for marginal stalls (magnetometer + radar or algorithmic fallback).
• Require OTA firmware updates and sensor health monitoring and a documented maintenance SLA.

Learn more

• Long battery life parking sensor — practical battery modelling tips.
• Autocalibration — field calibration best practices.
• Dual detection: magnetometer + nano‑radar — hybrid approaches and trade‑offs.

Author Bio

Ing. Peter Kovács — Technical freelance writer

Ing. Peter Kovács is a senior technical writer specialising in smart‑city infrastructure. He writes for municipal parking engineers, city IoT integrators and procurement teams evaluating large tenders. Peter combines field test protocols, procurement best practices and datasheet analysis to produce practical glossary articles and vendor evaluation templates.