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.
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.
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.
Introducing the DC-DC 800V-48V electronic converter, our powerhouse solution specifically developed for motorsport with voltage conversion up to 550W.
This cutting-edge device features two specialized boards: the High-Voltage Board orchestrates an isolated conversion from 800VDC to 48VDC using advanced LLC resonant converter technology, while the Low-Voltage Board leverages GaN Mosfets Technology to efficiently transform 48VDC to 14VDC, all packed into a sleek, compact design.
Experience power and efficiency like never before.
Our DC-DC 800V-48V is built with two different boards:
Alma Automotive power converter has been designed to be vibration resistant, with particular focus for severe ambient use.
The DC-DC is equipped with on-board data acquisition such as: load current, bus voltages, environmental temperature, pressure and VOC, actuator status and other. It is also able to drive and communicate with the actuator for the charge port using the CCS2 protocol. This DC-DC also has a 48V PWM out to supply a pump driver up to 400W and a 12VPWM. The DC-DC has an integrated BOOST module, so it is possible to power-on and supply load on 12V Chassis up to 80W with an external Kicker Cell. The LV board provides an on-board charger to recharge the Kicker Cell when the DCDC is powered by High-Voltage or external power supply (Jump Battery).
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.
Battery pack performances are widely affected by the temperature of the cells. It is basically all related to their chemical and physical nature. Moreover, cell temperature relies on several factors such as cell internal resistance and cell heat exchange.
An accurate battery pack thermal model implies a deep knowledge on thermo and fluid dynamics of the system components, not only inside the battery but also in all the sub-systems working with the heat transfer medium.
The figure below shows the results of an experiments carried out during our validation activities of a racing electric powertrain with a liquid cooled battery pack. Cell and coolant temperatures are modeled with a maximum error of ±1.5°C.
Model inputs:
The model is able to simulate the temperature of each cell inside the battery pack, keeping the computational effort low, and allowing the use of the model even in Real-Time applications. The model is flexible and easily tunable to cover the whole range of thermal management solutions.
Compact models with accurate results is the main goal we aim to accomplish.
In this case, a brief comparison between simulated battery pack voltage and State of Charge with experimental data is shown. Our battery pack model is an easly tunable model that can be used to simulate the electrical performances of HV battery packs composed with different Li-ion cell types.
Thanks to several calibration activities, our model allows us to simulate the battery pack voltage with a relative error lower than 1% with respect to the experimental data and the battery State of Charge with an absolute error even lower than 1%.
The variety of applications in which the model has been deployed enables us to check simulations reliability and precision, verifying the goodness of the results in 800V automotive powertrains architectures and many other.
What distinguishes us from existing solutions:
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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