Summary The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU‐recurrent neural network (GRU‐RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li‐ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre‐trained models are then converted into highly optimized C‐codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32‐bits floating‐point to 8‐bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU‐based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB.

<span lang="EN-US">The emerging vehicle-to-grid (V2G) technology has gained a lot of praise in the last few years, following its experimental validation in several countries. As a result, this technology is being investigated for standalone houses under the name of vehicle-to-house (V2H). This latter proposes a two-way power transfer between the electric vehicles and isolated houses relying on renewable sources for power supply. In this paper an implementation of the V2H technology is investigated, using the adaptive backstepping control approach for the bidirectional half-bridge and the integral sliding mode control for the DC-DC converter. The robustness of the controller and its capability to respond to the desired performances were tested using different realistic scenarios. The obtained results yielded, a perfect sinusoidal output voltage with a voltage level of 220 V and a frequency of 50 Hz. This is further been validated by a frequency analysis resulting in a THD of 0.25%.</span>

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Objective: To evaluate the performance of two open-source real-time operating systems (RTOSs), Keil RTX5 and FreeRTOS. Besides, a comparison between them has been also established based on four timing metrics: task switching time, pre-emption time, semaphore shuffling time, and inter-task messaging latency. All the tests have been performed on an STM32F429 discovery board based on Cortex-M4 MCUs. Methods: To measure the timing metrics, the ARM cycle counter register implemented in the DWT unit was used. Findings: The DWT cycle counter allows us to capture the number of cycles that occurred in the execution of a part of the code. Therefore, the time measurements of the metrics selected show that FreeRTOS has good performance with the lowest value of switching time, preemption time, and semaphore shuffling time. Instead, Keil RTX5 has fast message passing. Novelty: The study provides an evaluation and comparison of the latest version of the most used open-source RTOSs, Keil RTX5 and FreeRTOS v10.2.0. Furthermore, the timing metrics have been measured accurately with the ARM cycle counter register without using any other hardware or GPIO pin that may disturb the measurement. The comparison is based on four critical timing metrics that affect mostly the performance of any RTOS and define their time capability. Finally, the tests have been made on a low-power ARM MCU.