Electric Powertrain Predictive Diagnosis

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.


Contact us
Alma Automotive s.r.l.

Via Terracini 2/c - 40131 Bologna - Italy
Tel. +39.051.9923806 / +39.051.0548470
Fax +39.051.0544839

PEC amministrazione@pec.alma-automotive.it

Our locations

Via Terracini 2 , 40131 Bologna

Via Provinciale Bologna 28/30
40066, Pieve di Cento (BO)


© Copyright 2021 - Alma Automotive s.r.l. - P.IVA e C.F. 02315721205 - Cap. soc. 10.000€ - Privacy Policy - GDPR - Credits  - Sitemap

Gli aiuti di Stato e gli aiuti de minimis ricevuti dalla nostra impresa sono contenuti nel Registro nazionale degli aiuti di Stato di cui all’art. 52 della L. 234/2012 e consultabili qui

linkedin facebook pinterest youtube rss twitter instagram facebook-blank rss-blank linkedin-blank pinterest youtube twitter instagram