Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass

Abstract

Given the characteristics of dynamic vehicle mass, multi-operation scenes, and multi-energy-source and multi-energy-consumption-source, the energy optimization problem of concrete truck mixers remains a challenge. To this end, in this paper, a novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the driving system is utilized and an adaptive online energy management strategy with multi parameters including the driving cycle, vehicle mass, battery state of charge (SOC), and remaining mileage is presented. First, a driven-data clustering method with fuel consumption differentiation is adopted. Then, using the parallel computing genetic algorithm-optimized rule-based method, a driving condition knowledge base, including optimal control models of the various typical driving conditions under multi-parameter combinations, is built to realize the adaptability and robustness. Parameter identifiers are designed, including a sparrow search-optimized-based deep mixed kernel extreme-learning machine recognizer and a vehicle mass recognizer based on sensor and working scene fusion for obtaining corresponding information online. Finally, simulation experiments are conducted to validate the performance and robustness of the presented control strategy. Results indicate it can reduce fuel consumption by an average value of 15.17% and performs better in the convergence level of terminal SOC in five real composite driving conditions.