Multi-objective optimization for energy-efficient management of electric Tractors via hybrid energy storage systems and scenario recognition

The promotion of electric tractors faces significant challenges, including adapting powertrain systems to diverse operational conditions and optimizing energy efficiency and battery lifespan. This paper presents a hybrid energy storage system (HESS) architecture for electric tractors. And a multi-objective energy-efficient management strategy (EMS) based on plowing operation scenario recognition is proposed. The strategy involves developing an electric tractor model and a plowing operating condition (POC) cycle using real-world plowing data. Offline classification is performed using K-means clustering and Principal Component Analysis (PCA), while a Multilayer Perceptron Neural Network (MLPNN) is employed for online real-time scenario recognition. Additionally, a Multi-Strategy Improved Black-winged Kite Algorithm (MSIBKA) is developed to efficiently derive adaptive power allocation trajectories. Simulation and Hardware-in-the-Loop (HIL) experiments demonstrate that the proposed strategy effectively extends the lifespan of the HESS, smooths battery output, and reduces operating costs. Specifically, the supercapacitor supplies over 65 % of the peak power demand, reducing the battery C-rate by more than 10 %. Furthermore, the proposed system increases the state of charge (SOC) of the battery by at least 5 %, while reducing both operational costs and battery degradation costs by over 33.3 %. These results indicate that the proposed system and strategy provide substantial benefits in extending battery lifespan and enhancing energy efficiency.