This book explores how advanced control theories and artificial intelligence can be integrated to enhance the stability, adaptability, and efficiency of electric vehicles operating under harsh real-world conditions. It bridges classical and modern approaches - from PID and robust H control to neural networks and reinforcement learning - offering a unified framework for handling nonlinearities, uncertainties, and severe constraints such as aerodynamic drag, tire slip, and battery degradation, while envisioning resilient, autonomous, and sustainable EV systems connected to smart grids.
This book explores how advanced control theories and artificial intelligence can be integrated to enhance the stability, adaptability, and efficiency of electric vehicles operating under harsh real-world conditions. It bridges classical and modern approaches - from PID and robust H control to neural networks and reinforcement learning - offering a unified framework for handling nonlinearities, uncertainties, and severe constraints such as aerodynamic drag, tire slip, and battery degradation, while envisioning resilient, autonomous, and sustainable EV systems connected to smart grids.
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