Electronic differential system based on neural networks for electric vehicles: development, adaptation and prospects of application

Aim. The analysis of possibilities and prospects of development of an electronic differential system for electric vehicles based on artificial neural networks. Materials and Methods. We discuss the key advantages of the proposed system, such as its customization capability to various vehicle designs, integration of additional sensors, support for self-driving mode and the ability to interact with the ABS system. Results. We considered the ways to improve the model, including the introduction of self-learning algorithms, optimization of inverter circuits for controlling multiple motors, and implementation of all-wheel drive configurations. In addition, we discuss the customization of the electronic differential system for operation on low-power devices using quantization, pruning and architecture simplification methods. Conclusion. The proposed approaches and algorithms have the potential for widespread deployment in the electric vehicle industry, opening new vistas for development of intelligent vehicle control systems.