Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management 1st Edition

Capture the future of sustainable mobility with Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management, 1st Edition by Jili Tao, Ridong Zhang, and Longhua Ma. This authoritative title bridges cutting-edge AI methods and practical energy-management strategies for hybrid electric vehicles (HEVs), offering a clear roadmap for engineers, researchers, and advanced students looking to improve efficiency, range, and lifecycle performance.

Begin with a compelling overview of why intelligent control matters today: rising emissions regulations, battery-cost pressures, and the need for smarter energy allocation. The authors unpack core AI techniques — from machine learning and neural networks to reinforcement learning and model predictive control — and show how these tools reshape real-world HEV energy-management systems. Each chapter balances theoretical rigor with engineering relevance, translating algorithms into actionable control strategies for battery management, power-split optimization, and fuel-economy improvement.

What sets this book apart is its practical orientation and global applicability. Case studies and comparative analyses highlight implementations across diverse driving conditions and regional markets—useful for practitioners in North America, Europe, and Asia. Clear diagrams, performance metrics, and implementation notes help teams accelerate development cycles and reduce testing costs.

Whether you’re optimizing supervisory control, designing adaptive controllers, or evaluating AI-driven energy strategies for fleet deployment, this volume is an essential reference. Forward-looking yet grounded in proven methods, it equips professionals and academics to lead the next wave of efficient hybrid transportation.

Add this indispensable resource to your library and empower your projects with intelligent, data-driven energy management. Order your copy today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.