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