Predictive maintenance in the industrial machinery sector is a crucial strategy for ensuring plant reliability and efficiency. Its primary goals include reducing operational disruptions and costs associated with unexpected failures. By constantly monitoring the condition of machines through sensors and advanced technologies, any signs of malfunction or wear and tear can be identified early, enabling targeted and planned interventions to prevent serious and costly failures. This helps extend the useful life of machines and optimize the use of human and material resources.

Intelligent Monitoring

The WARNN system, developed by Alma Automotive, allows key values of machine operation to be monitored.

• Vibration
• Temperature
• Lubricant quality
• Operating points
• Machine data

Anticipate the Problem

Using Artificial Intelligence, WARNN builds a digital twin of the observed machine and compares all acquired data with the corresponding reference values in case of optimal operation.

Thanks to this process, it is possible to anticipate faults and understand when the machine is deviating from its standard operation, triggering a whole series of alarms that enable its maintenance.

• Cost reduction
• Damage containment
• Lowering risk
• Organized downtime

Predictive maintenance greatly improves workplace safety, as it reduces the risk of sudden failures and accidents. This approach promotes continuous, high-quality production, ensuring maximum operational efficiency. The implementation of advanced technologies such as IoT and data analytics enables the collection of detailed information on machine performance, identifying significant trends and providing valuable information for future decisions.

The advantages for companies are significant, as predictive maintenance promotes sustainable production with lower environmental impacts and optimized maintenance costs. This approach improves company reputation, attracts new customers, and stimulates industry growth through a constant search for technological innovation to optimize industrial plant management.

The transition to electric powertrains brings about new challenges in predicting when something might go wrong and in keeping the powertrain healthy. While traditional diagnosis methods have their merits, they often provide only a limited opportunity to address the dynamic evolution of potential dangerous conditions in electric powertrains, where the interplay of numerous intricate electronic components and complex systems requires a more sophisticated approach to monitoring and diagnostics.

Purpose of the system

ESACO (Electric powertrain System Anomaly detector and Conditions Observer) has been developed in response to the urgent need for improving the safety, reliability, and performance of electric powertrains. As electric vehicles (EVs) become increasingly prevalent in the automotive landscape, ensuring their safety and reliability is paramount.

ESACO's scope extends beyond just enhancing individual electric powertrains; its goal is to optimize their usage and integration into the transportation landscape. Utilizing advanced diagnostic and predictive technologies, ESACO aims to enhance the reliability and performance of electric vehicles, thus facilitating their wider adoption. This contributes to mitigating environmental impact by reducing emissions and advancing sustainable transportation solutions.

Ai based alghoritms

The availability of comprehensive electric vehicle powertrain data, along with the capacity to effectively manage it, enables the utilization of AI-based trained algorithms. This integration represents a powerful and innovative approach for real-time monitoring of powertrain health, allowing for the early detection of anomalies and potential issues.

By leveraging advanced artificial intelligence techniques, such as machine learning and predictive analytics, these algorithms can analyze vast amounts of data to identify patterns and trends indicative of developing problems. This proactive approach not only enhances the reliability and performance of electric vehicles but also contributes to minimizing downtime and maintenance costs, thereby optimizing the overall efficiency and usability of electric powertrains in various mobility applications.

Test Bench Case Studies

li-Ion cell Thermal runaway

• Anomalous variation in maximum cell temperature trend from 500s.
• Anomaly score rises even if cell temperature value is only 5°C above the nominal one.

Battery Cell Temperature
Anomalous Operation

• 5s freezing of the maximum battery cell temperature during DC current request.
• Anomaly score during freezing shifts to high values.

Stator Temperature
Anomalous Operation

• Electric motor stator temperature, with sudden oscillation from 440s to 445s.
• Strong non-correlation between stator temperature and the operating points.
• Anomaly has been suddenly detected.

Battery health and energy saving are crucial factors to consider for any electric vehicle powertrain systems. A battery that fails unexpectedly can be a major inconvenience, leading to huge performance degradation, costly repairs and lost time. To address this issue, AI-powered algorithms have been developed to monitor battery State of Health and operating conditions in Real-Time. These algorithms provide alerts for battery issues and even predict potential failures before they occur.

Our battery performance and health predictor algorithm has been developed to release power and usable energy increase. Implementing advanced State of Health estimation and reliable safety logics, this AI-based system makes the Electric Powertrain Management System more powerful.

AI algorithms need a huge amount of data to well train the models. Indeed, data acquisition and management for AI based applications are usually critical issues for the model development. This is the reason why, as explained in our recent news, R&D engineers’ efforts have been firstly dedicated to the implementation of reliable and accurate models. In our approach, simulation results can be a source of data to train the AI model, improving and speeding up its training process.

A sneak peek of our Battery Performance Algorithm application is here reported. In the experiment performed, a battery pack internal resistance variation has been triggered. The algorithm analyses the input signals during a specified time buffer and from that moment on, it is able to estimate an overall warning index related to the operating and health conditions of the battery pack.   

Even if the battery electrical and thermal features continue to be inside the acceptable range, our AI algorithm detects the anomaly operation and it is able to trigger both alarms and safety logics if needed.

Alma Automotive provides electrified test cells and its expertise to offer testing services dedicated to electric powertrains. Our test engineers are ready to supervise the tests and create accurate reports at the end of each one, following the customers in every step.

The company has developed a model capable to perfectly simulate the battery pack, starting from the characterization of the cells used or supposed to be used. The system can emulate the electrical and thermal behavior of the device under test. Having the ability to drive power electronics from several suppliers, our system is very flexible and accurate. The common 600V DC bus is used to drive a 200kW battery emulator up to 1050V.

The electric machine to be used as a dynamometer is chosen according to the specific needs of the test: this can be supplied directly from the Borghi&Saveri catalog or purchased ad hoc from our selected suppliers.

Various tests can be performed for the characterization of the electric powertrain, including:

• Performance verification of the electric motor in specific operating points and in regeneration/brake mode
• Motor electrical check and Back EMF (before and after the test)
• Performance characterization of the electric motor in various operating modes (locked rotor, regeneration, max torque)
• Endurance, duty cycle and speed test
• Complete inverter calibration

Each test cell is equipped with an acquisition system based on the National Instruments platform, featured with temperature, pressure and accelerometer sensors. The test management system can directly or remotely control every component of the test cell, such as ventilation, power supplies, safety systems and it can digitize any type of test cell transducer.

Thanks to our custom-made thermal management system, we are able to simulate and reproduce the thermal management of the electric motor and inverter. We are able to perform motor and inverter tests by managing the coolant temperature in real time, emulating the temperature profile that would actually occur in the driving cycle or by testing the components at a desired temperature.

The system installed in the test room allows to the mechanical characterization of the system, by measuring:

• Torque and cogging torque
• Speed
• Vibrations
• Temperatures
• Flow rates and pressures (H2O/Glycol cooling system)

Drag tests can be conducted to measure mechanical losses and we are able to electrically characterize the system under test, using precision power analyzers.

• Accurate measures of efficiency
• AC/DC Current and Voltage measurement
• Motor winding resistance

We can also supply our Predictive Maintenance tool to determine deviations from healthy conditions of the device under test in working conditions.
The A.I. based system uses input data to estimate expected indicators levels (plant model) and then compares them with actual indicators intensities. The approach can be extended to estimate the remaining useful life or optimal maintenance intervals.