Innovation in Energy Management Strategy for Hybrid Electric Vehicles

Dear Colleagues,

The continuous development of hybrid electric vehicles (HEVs) plays a crucial role in addressing global energy challenges, reducing greenhouse gas emissions, and improving transportation efficiency. Within this context, energy management strategies are fundamental to achieving optimal coordination between the internal combustion engine and the electric propulsion system. Effective energy management directly influences fuel consumption, emission levels, battery degradation, and overall vehicle performance. As driving conditions become increasingly complex and varied, conventional energy management approaches often fail to fully utilize the potential of hybrid powertrains, highlighting the need for innovative and adaptive control solutions.

This Special Issue of Energies, entitled “Innovation in Energy Management Strategy for Hybrid Electric Vehicles”, aims to collect high-quality research contributions that address recent advances and emerging trends in HEV energy management. The focus is placed on innovative control strategies that enhance system efficiency, robustness, and real-world applicability. Contributions are encouraged that explore both classical and advanced methodologies, including rule-based control improvements, optimization-based approaches, and real-time control frameworks capable of operating under practical computational constraints. In particular, optimization-based energy management strategies such as dynamic programming, equivalent consumption minimization strategies, and model predictive control have demonstrated strong potential for improving fuel economy and reducing emissions. However, their performance is often limited by uncertainties in driving patterns, system modelling, and environmental conditions. To overcome these challenges, this Special Issue emphasizes adaptive and learning-based methods that can dynamically adjust control decisions based on historical data, online measurements, and predictive information. Machine learning techniques, including reinforcement learning and neural network-based controllers, offer promising solutions for capturing complex nonlinear system behaviour while maintaining adaptability to diverse driving scenarios.

Another important research direction addressed in this Special Issue concerns multi-objective energy management, where fuel efficiency, emission reduction, battery ageing, thermal management, and drivability are considered simultaneously. Balancing these competing objectives requires accurate modelling of powertrain components and advanced control architectures capable of handling trade-offs in real time. Studies focusing on battery health-aware energy management, lifecycle-oriented optimization, and thermal–electrical coupling are particularly relevant to improving long-term system reliability and reducing operational costs. Moreover, the increasing integration of connected vehicle technologies and intelligent transportation systems provides new opportunities for predictive energy management strategies. By utilizing information such as traffic conditions, road topology, and vehicle-to-infrastructure communication, energy management systems can more effectively anticipate future power demands and optimize energy distribution. This Special Issue welcomes research that leverages connectivity and predictive control to enhance the performance of hybrid electric vehicles in real-world driving environments. Finally, it places strong emphasis on the practical implementation and validation of proposed methods. Therefore, experimental studies, hardware-in-the-loop testing, and real-world case studies are highly encouraged. By bringing together theoretical developments, simulation-based analyses, and experimental results, this Special Issue aims to provide a comprehensive overview of state-of-the-art and emerging energy management strategies for hybrid electric vehicles, contributing to the advancement of efficient, intelligent, and sustainable mobility solutions.

Prof. Dr. Roberto Capata
Guest Editor

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• hybrid electric vehicles
• energy management strategy
• powertrain control
• optimization-based control
• intelligent energy management
• machine learning
• reinforcement learning
• model predictive control
• battery health management
• fuel efficiency
• emission reduction
• predictive energy management
• connected vehicles
• sustainable mobility