Review on energy management strategies for hybrid tractors

The article provides a comprehensive review of energy management strategies for hybrid tractors, elaborating on the current research status, key issues, and future directions in this field. Against the backdrop of global sustainable development and agricultural energy conservation and emission reduction, hybrid tractors, which integrate the advantages of internal combustion engines and electric motors, have become a research hotspot. Energy management strategies, as the core technology, directly affect vehicle performance, economy, and environmental friendliness. The article classifies existing energy management strategies into three categories: rule-based strategies (such as thermostat-based, power-following, and fuzzy strategies), which are simple to implement but have limited adaptability to complex operating conditions; optimization-based strategies (such as dynamic programming, Pontryagin’s minimum principle, equivalent fuel minimization, and model predictive control), which enhance energy efficiency through mathematical modeling but are constrained by computational complexity and real-time requirements; and learning-based strategies (such as reinforcement learning and neural networks), which have strong adaptability due to data-driven approaches but rely on large amounts of high-quality training data. Furthermore, the article delves into the key issues in strategy development, including operating condition identification (such as methods based on K-means clustering and neural networks), operating condition prediction (such as adaptive triple exponential smoothing prediction), algorithm optimization and adaptive control deepening, as well as the influence of battery health status and temperature. Current research faces challenges such as the complexity and variability of farm operating conditions, the balance between model accuracy and real-time performance, and conflicts in multi-objective optimization. However, it also benefits from advancements in intelligent algorithms and hardware technology. In the future, the development of energy-efficient tractors will become an inevitable trend in the field of agricultural engineering. This review provides an outlook on the opportunities and challenges for future development, and also provides theoretical support for the wide application of hybrid tractors and sustainable agricultural development.