Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS.

The industry demand for AI experts raised to unprecedented levels in the last years. However, the increasing demand was not met by the number of skilled professionals in this area. As an effort to mitigate this problem, many companies create AI residency programs to provide in-house practical training. However, we argue that the usual dynamics based on one-on-one mentorship in those programs is very hard to scale and insufficient to meet the demand for AI professionals. In this paper, we describe a hybrid AI residency program that connects educational institutions, partner companies, and prospective residents. This program is designed to be funded by partner companies.Residents are exposed to practical projects of industry interest and are instructed on AI techniques and tools. We describe how we implemented our program, the challenges involved, and the lessons learned after the conclusion of the first residency class. Our program was developed to be inclusive and scalable, and resulted in a high employment rate for our alumni. Furthermore, several partner companies invested in in-house AI teams after the residency, resulting in direct benefits for our local AI community.

In electric vehicles (EVs), monitoring battery cells ensures safety and longevity. The Battery Management System (BMS) is responsible for this task, using variables such as State of Charge (SoC). This paper presents a comparative study between the Unscented Kalman Filter (UFK) and Multilayer Perceptron Neural Network (PMC) methods applied to a pack using a simulation of a vehicle under the FTP-75 cycle with reference SoC using Coulomb Counting (CC). The Neural Network performed better with an RMSE value of 0.418 compared to 0.0593 for UFK.