Adaptive hierarchical energy management design for a novel hybrid powertrain of concrete truck mixers

Abstract

Due to the characteristics of a significant difference between vehicle no-load and full load, time-varying double driving conditions, two-energy-source and two-energy-consumption-unit, and high coupling electromechanical-hydraulic system, this work studies two critical technologies, including powertrain design and energy management strategy development, to solve the tricky energy optimization problem of concrete truck mixers. A novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the propulsion system is proposed. On that basis, an adaptive hierarchical energy management strategy is developed including two efforts. First, a rule-based hierarchical strategy with auxiliary power unit multi-mode switching for the upper and power splitting of system power units for the lower is designed. Second, a driving condition recognizer constructed by a hybrid-optimization-based random forest algorithm and a genetic algorithm optimization-based differentiated controller is designed, which can automatically switch to the optimal strategy under current driving conditions according to recognition results. Simulation results manifest that the designed optimized driving condition recognizer performance outperforms unoptimized by 2.50%. Compared with dynamic programming and the presented rule-based hierarchical strategy, the proposed adaptive hierarchical strategy with terminal SOC converging to the lowest level has better adaptability, economy, and real application performance under a real-world composite driving condition.