LTO BATTERY USEFUL LIFE PREDICTION FOR
ALWAYS ON EDGE AIoT BASED STRUCTURAL
HEALTH MONITORING

Ivan Arakistain, Diego Zamora, David Garcia-Sanchez, Alberto Armijo

► To cite this version:

Ivan Arakistain, Diego Zamora, David Garcia-Sanchez, Alberto Armijo. LTO BATTERY USEFUL LIFE PREDICTION FOR ALWAYS ON EDGE AIoT BASED STRUCTURAL HEALTH MONITORING. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0264. hal-05085224

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Distributed under a Creative Commons Attribution 4.0 International License
# LTO BATTERY USEFUL LIFE PREDICTION FOR ALWAYS ON EDGE AIoT BASED STRUCTURAL HEALTH MONITORING

Ivan Arakistain¹, Diego Zamora¹, David Garcia-Sanchez¹, Alberto Armijo¹

¹TECNALIA Basque Research and Technology Alliance “BRTA”

Parque Científico y Tecnológico de Bizkaia
Astondo Bidea, Edificio 700.

E-48160 Derio (Bizkaia), Spain

Tel.: +34 946.430.850.

E-Mail: ivan.arakistain@tecnalia.com

URL: http://www.tecnalia.com

## Acknowledgements

The authors would like to acknowledge the Horizon Europe (HE) funding program of the FUTURAL project (GA 101083958), LIAISON (GA 101103698) and ReCharged Marie Skłodowska-Curie Staff Exchanges Action (GA 101086413).

## Keywords

Batteries, aging, prognostics, sensors, TinyML

## Abstract

Although many high-sampling sensor systems tend to be power-hungry, critical monitoring applications require reliable battery-powered sensor nodes that can retrieve and compute data on the edge for years. With the advent of Tiny Machine Learning (TinyML), it is becoming increasingly feasible to deploy always-on inference Machine Learning models on constrained battery-powered microcontroller-based nodes. However, owing to unpredictable and dynamic energy harvesting availability conditions and the limitations of battery technology, long-term operation is still challenging.

In this paper, we present a hardware and software solution for long term continuous solar operation of power-hungry wireless sensor nodes with Lithium titanate oxide (LTO) batteries.

## Introduction

Within a microcontroller, the CPU is typically the component that uses the most current. Peripherals

certainly contribute to energy consumption, but one of the first steps to optimise a device for energy efficiency is to keep the CPU in its lowest state for as long as possible. Applications periodically need to be wake-up to process and move data around. However, applications that require always-on processing inherently require more energy, as the CPU power consumption is generally proportional to the operating frequency and workload.

Batteries must be dimensioned according to the power requirements of the application within the required battery life. Although degradation remains a pivotal concern in the domain of batteries for the IoT, Machine Learning has emerged as a potent tool for driving forward insights and solutions. However, the intersection of electrochemical science and Machine Learning presents several complex challenges. Machine Learning experts often grapple with the intricacies of battery science, whereas battery researchers face hurdles in adapting intricate models tailored to specific datasets [1].

## Use case: Always on sensors for the assessment of the structural condition.

The pilot structure in Durangaldea (Biscay), is a 3-span road bridge with high piers and a central span resting on half-joint (or dapped-end) supports, which require repair. This bridge is also vulnerable in its foundation because of torrential runoff under the structure, as well as being located in an area prone to a violent natural phenomenon called ‘debris flow’. Thus, because this structure is a critical node of the transport
network of Durangaldea, providing access to the main communication routes, and is particularly vulnerable to climatic and natural phenomena, it was chosen as a pilot case for the development of Structural Health Monitoring (SHM) algorithms for its control before, during, and after significant events.

<figure><img src="image_2.png" /><figcaption>Fig. 1: Bridge SHM use case</figcaption></figure>

Various sensors were evaluated: the LM6DSOX MEMS accelerometer and OV2640 camera module.

No bridge damage was synthetically recreated in this early phase (but anomalous vibration data were obtained from misplaced sensors). In a more advanced stage of the developments, the sensitivity to damage will be tested after the signals are affected by a damage ratio obtained from computational models.

## Always-on TinyML inferencing

With TinyML [2], it is now possible to implement AI models using inexpensive 32bit microcontrollers. Training the inferencing engine may take less time than developing firmware based on mathematical modelling and equations.

Some of the applications that may require always on inferencing are image and motion classification sensor nodes. In the last 10 years, the power consumption of advanced MEMS motion sensors has reduced by over 90%. Smart motion sensors enable distributed machine learning to significantly reduce bandwidth for more responsive, energy-conscious edge computing solutions. Latest ultra-low-power 6-axis inertial sensors come with a Machine Learning Core (MLC) and Finite State Machine (FSM) to enable motion pattern recognition or

vibration detection. These smart inertial sensors can execute decision trees in the built-in MLC of the sensor which is ideal for always-on applications with current consumption in the range of microamperes.

Also, image sensors have a power reduction trend similar to that of motion sensors. In addition to the microcontroller power reduction and techniques that enable low latency, reduced bandwidth, and quantisation.

<figure><img src="image_3.png" /><figcaption>Fig. 2: System architecture comprising an ESP32-S3 MCU, BMS for LTO battery and sensors (accelerometer and camera).</figcaption></figure>

## ESP32-S3 TinyML and algorithmics power profiling

The natural vibration modes of the bridge structure and visual defect images were analyzed in search of anomalies using accelerometer and camera image data, respectively.

### TinyML vibration classification model

<figure><img src="image_4.png" /><figcaption>Fig. 3: Accelerometer signal values of a bridge and natural vibration frequency FFT calculation for anomaly detection</figcaption></figure>

Different supervised learning methods have been analysed for vibration, such as linear regression,
decision tree regression, stepwise regression, Gaussian process regression and neural networks. A numerical model was used for accurately reproducing the dynamic characteristics of the undamaged bridge. A lookup table was constructed using anomalies created by misplaced sensors for structural damage detection. Noise was also added to the generated signal to test the algorithms with a more realistic signal, where the maximum amplitude of the noise corresponded to 90% of the maximum amplitude of the generated signal.

In the process of training the model with an accuracy of 86.57%, 510 iterations were necessary, and the training required 371 minutes.

<figure><img src="image_5.png" /><figcaption>Fig. 4: Power-profiling sensor nodes during sensor reading, FFT, and data transmission.</figcaption></figure>

## TinyML image classification with FOMO

Edge Impulse FOMO and FOMO-AD (Faster Objects, More Objects) are new machine learning algorithms designed to enable object detection and spotting visual anomalies on devices with limited resources. They can detect multiple objects, determine their location in an image in real-time and identify anomalies such as cracks in concrete structures, using up to 30 times less processing power and memory than MobileNet SSD or YOLOv5.

FOMO processes images in grayscale and divides them into blocks of pixels using a factor of 8. For example, with an input of 96x96, the grid would be 12x12 (96/8=12). FOMO then runs a classifier through each pixel block to calculate the probability of the presence of an anomaly or target object. The core idea is to automate the inspection process, allowing for quicker and more accurate identification of close concrete cracks,

holes and cold joints in bridges in hard-to-reach areas that might be missed by human observers.

The overall current consumption was measured by sourcing 3.3 V and the reported consumption for vibration and image classification is described in Table I. Therefore, the average daily battery capacity consumed was 1,684 mAh and 3,455 mAh respectively out of the battery capacity of 5,200 mAh.

Even if further power consumption techniques could be applied [3] to the edge device, these figures clearly show that a durable solar-charging battery technology is required to operate reliably for years.

<table><caption>Table I: Energy consumption of the system</caption><thead><tr><td>Routines</td><td>Sleep</td><td>Acquire data, inferencing</td><td>Wi-Fi</td></tr></thead><tbody><tr><td>ESP32-S3 with LM6DSOX<br>Average current (mA)</td><td>25 uA</td><td>69.34 mA</td><td>129.32 mA</td></tr><tr><td>ESP32-S3 with OV2640<br>Average current (mA)</td><td>25 uA</td><td>143.25 mA</td><td>272.01 mA</td></tr></tbody></table>

## LTO battery technology for IoT

Lithium titanate oxide (LTO) is a rechargeable battery based on or modified from the lithium-ion (Li-ion) battery technology. Li-titanate oxide (LTO) replaces graphite in the anode of a typical Li-ion battery and forms materials with a spinel 3D crystal structure. With a nominal cell voltage of 2.40V, it releases a high current discharge current that is ten times the capacity of other types of lithium batteries. Instead of using carbon particles on the surface, as in other lithium batteries, LTO uses lithium titanate nanocrystals. The effect and benefit of this alteration and the inclusion of lithium-titanate nanocrystals is that the surface area of the anode is approximately 100 square meters per gram, in contrast to only 3 square meters per gram of Li-ion batteries. The enlarged surface area of the lithium-titanate nanocrystals enables electrons to enter and leave
the anode much more rapidly, leading to fast recharging and an enhanced battery lifetime. Given the basic scientific advantages of high-tech nanotechnology involved in producing LTO batteries, a few benefits for IoT range from a long lifetime to enhanced safety and low-temperature performance are described.

## 1. Extremely Long Lifetime

As discussed above, advanced nanotechnology consisting of lithium-titanate nanocrystals and their increased surface area is designed to enhance the lifetime of these batteries. As a result, the cycle count of a Lithium Titanate battery is more than 20,000 in comparison with only 2,000 in a regular lithium battery, marking a revolutionary approach to energy storage for long-term IoT monitoring.

<figure><img src="image_6.png" /><figcaption>Fig. 5: Commercial grade lithium battery cycle aging benchmark from MATR1 dataset degradation curve with Battery ML.</figcaption></figure>

Figure 5 clearly demonstrates that even at a constant 25°C temperature, most regular lithium batteries can hardly reach 2,000 cycles while maintaining over 80% of their original capacity available. This clarifies the need for more durable technologies such as LTO.

## 2. Low-Temperature Performance

Another advantage of using Lithium Titanate batteries is that, owing to the nanotechnology employed, these batteries have a much better low-temperature performance than other battery technologies, and owing to these low-temperature discharge characteristics, it is possible to obtain up to 80% of its full capacity at a mere -30°C. This is of particular benefit to companies that employ Lithium Titanate technology at lower temperatures in cold winter regions.

As discussed later, an LTO battery life cycle simulation was performed for both northern and southern European climatic areas with a custom application profile for IoT and comparison with other battery chemistries.

<figure><img src="image_7.png" /><figcaption>Fig. 6: Climatic areas and yearlong temperature profiles for battery aging simulation</figcaption></figure>

## 3. Rapidly charging

LTO batteries provide significantly accelerated charging compared to traditional lithium-ion batteries. While a typical lithium-ion battery takes approximately an hour to charge, our LTO batteries can achieve full charging within approximately 3 min. This feature makes our LTO batteries ideal for applications that require compact yet high-power energy sources.

## 4. Sustainable solutions

These batteries are designed to be environmentally responsible and adhere to international standards, such as RoHS regulations, meeting stringent operational standards.

## 5. High safety

LTO batteries demonstrate exceptional safety performance, enabling flawless operation even under harsh physical and environmental conditions.

## State of art in LTO battery technology for IoT

The LTO batteries from Nichicon [4] can last for more than 25,000 charge/discharge cycles before experiencing any degradation to their
performance in contrast to standard lithium-ion batteries, which may lose performance after 1000 charge/discharge cycles. In fact, LTO batteries may maintain 80% or more of their discharge capacity, even after 25,000 cycles [4].

These small capacity rechargeable batteries are available in five different sizes: 0.35mA, 0.80mAh, 4mAh, 14mAh and 150mAh.

## Battery ML simulation

Battery ML [5] is a state-of-the-art Machine Learning technique developed by Microsoft to optimise battery performance and enhance battery life. Battery ML generates a predictive model that predicts battery performance under diverse conditions by analysing battery-related data, such as temperature, charge and discharge rates, and usage patterns. This innovative technique is employed to extend the battery life of a range of devices including smartphones, laptops, tablets, wearables, and electric vehicles. Battery ML distinguishes itself from existing machine learning techniques by being specifically designed to optimise battery performance, considering a broader range of factors, and possessing the ability to learn and adapt continuously.

The implementation of Battery ML [6] offers numerous advantages, such as extended battery life, enhanced battery performance, reduced battery drain, improved charging efficiency, and diminished battery degradation. Device manufacturers such as Microsoft, Samsung, and Tesla have integrated Battery ML into their products, including Surface laptops, Galaxy smartphones, and electric vehicles, respectively, to capitalise on these benefits.

## Developed solar battery solution

The solar LTO battery charging circuit uses SPV1040 step-up from ST Microelectronics for the solar panel's power MPPT, BQ34Z100 from Texas Instruments as a battery fuel gauge, and a TPS63060 Buck/Boost converter from ST for battery power output. A portion of the schematic is shown in Figure 9:

<figure><img src="image_8.png" /><figcaption>Fig. 7: LTO battery solar node top view</figcaption></figure>

Therefore, the device can work in outdoor conditions year-round. It's features include:

* 4 x LTO battery cells with 5,200 mAh total capacity (1,300 mAh per cell).

* MPPT tracker for LTO batteries charging from solar cell.

* Photovoltaic source is comprised of 4 solar cells in series with a peak power of 4,4 W and 2,5 Voc output.

* BQ34Z100 battery gauge for battery charge state measurement. It includes a LED charge condition indication and I2C interface with host MCU.

* Output switching voltage regulator with standard output voltage of 3.3V.

<figure><img src="image_9.png" /><figcaption>Fig. 8: Bottom view of operational LTO battery solar node</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 9: BQ34Z100 battery fuel gauge schematic within LTO battery solar node</figcaption></figure>

## Discussion of battery solution aging

LiPo, Li-Ion, LiFePo4 and LTO batteries were modelled, and a battery life simulation was performed for each of them, according to climatic data and discharge profile. Results are described in Figure 10.

<figure><img src="image_11.png" /><figcaption>Fig. 10: Battery useful life prediction results for LTO, LiFePo4, Li-ion and LiPo battery technologies</figcaption></figure>

Even considering most adverse yearlong climatic temperature conditions, LTO batteries outperform other battery technologies and make always on inference for IoT nodes possible for over a decade.

## Conclusion

Supervised methods, a subset of Machine Learning algorithms, present an innovative

approach to SHM. These methods excel in learning patterns and relationships within complex datasets, making them well-suited for optimizing the performance of advanced monitoring technologies. This training process enables the algorithm to create a robust and accurate model for predicting the behaviour of the structure under various loading or environmental conditions.

However, always on inference systems require durable and robust solar batteries to provide energy. LTO batteries are better suited for these applications than LiPo, Li-ion and LifePo4 battery technologies. It was possible to model, simulate and validate a relatively large capacity 5,200 mAh LTO battery-BMS set.

The outstanding characteristics of LTO batteries, along with the ability of supervised algorithms to continuously learn and adjust based on real-time data ensures that the sensor system remains responsive and reliable, even as the structure undergoes variations in load, temperature, or environmental conditions.

## References

[1]. Rahmanian, F., Lee, R.M., Linzner, D. et al. Attention towards chemistry agnostic and explainable battery lifetime prediction. *npj Comput Mater* 10, 100 (2024). https://doi.org/10.1038/s41524-024-01286-7

[2]. Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, and Athanasios Kakarountas. 2024. A Review on the emerging technology of TinyML. ACM Comput. Surv. 56, 10, Article 259 (October 2024), 37 pages. https://doi.org/10.1145/3661820

[3]. Fanariotis, A., Orphanoudakis, T., & Fotopoulos, V. (2024, March 12). Reducing the power consumption of edge devices supporting ambient intelligence applications. MDPI. https://www.mdpi.com/2078-2489/15/3/161

[4]. Long Cycle Life Battery Cells – Nichicon LTO batteries. Nichicon Small Lithium Titanate Rechargeable Batteries. (2024, March 8). https://nichiconbattery.com/long-life-batteries/

[5]. Microsoft. (n.d.). Microsoft/BatteryML. GitHub. https://github.com/microsoft/BatteryML

[6]. Zhang, H., Gui, X., Zheng, S., Lu, Z., Li, Y., & Bian, J. (2023). BatteryML: An Open-source platform for Machine Learning on Battery Degradation. *arXiv preprint arXiv:2310.14714v5*