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Robust and Intelligent Control of Electric Vehicles: From Theory to Severe Constraints
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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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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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