Study on Driver-Oriented Energy Management Strategy for Hybrid Heavy-Duty Off-Road Vehicles under Aggressive Transient Operating Condition

Abstract

Hybrid heavy-duty off-road vehicles frequently experience rapid acceleration and deceleration, as well as frequent uphill and downhill motion. Consequently, the engine must withstand aggressive transients which may drastically worsen the fuel economy and even cause powertrain abnormal operation. When the engine cannot respond to the transient demand power quickly enough, the battery must compensate for the large amount of power shortage immediately, which may cause excessive battery current that adversely affects the battery safety and life span. In this paper, a nonlinear autoregressive with exogenous input neural network is used to recognize the driver’s intention and translate it into subsequent vehicle speed. Combining energy management with vehicle speed control, a co-optimization-based driver-oriented energy management strategy for manned hybrid vehicles is proposed and applied to smooth the engine power to ensure efficient operation of the engine under severe transients and, at the same time, to regulate battery current to avoid overload. Simulation and the hardware-in-the-loop test demonstrate that, compared with the filter-based energy management strategy, the proposed strategy could yield a 38.7% decrease in engine transient variation and an 8.2% decrease in fuel consumption while avoiding battery overload. Compared with a sequential-optimization-based energy management strategy, which is recognized as a better strategy than a filter-based energy management strategy, the proposed strategy can achieve a 16.2% decrease in engine transient variation and a 3.2% decrease in fuel consumption.