Abstract
This article studies the power management control strategy of electric drive system and, in particular, improves the fuel economy for electric drive tracked vehicles. Combined with theoretical analysis and experimental data, real-time control oriented models of electric drive system are established. Taking into account the workloads of engine and the SOC (state of charge) of battery, a fuzzy logic based power management control strategy is proposed. In order to achieve a further improvement in fuel economic, a DEHPSO algorithm (differential evolution based hybrid particle swarm optimization) is adopted to optimize the membership functions of fuzzy controller. Finally, to verify the validity of control strategy, a HILS (hardware-in-the-loop simulation) platform is built based on dSPACE and related experiments are carried out. The results indicate that the proposed strategy obtained good effects on power management, which achieves high working efficiency and power output capacity. Optimized by DEHPSO algorithm, fuel consumption of the system is decreased by 4.88% and the fuel economy is obviously improved, which will offer an effective way to improve integrated performance of electric drive tracked vehicles.
Highlights
Depending on its outstanding performance in power supply, energy saving, noise reduction, and environmental protection, the electric drive technology has been extensively applied in high speed railway, hybrid vehicle, and military nowadays
Real-time method is adopted in this paper and the strategy is evaluated on a driver-controller based HILS platform
This paper takes the power management control strategy of tracked vehicle electric drive system as study object; the simulation models of electric drive system are built based on theoretical analysis and experiment works
Summary
Depending on its outstanding performance in power supply, energy saving, noise reduction, and environmental protection, the electric drive technology has been extensively applied in high speed railway, hybrid vehicle, and military nowadays. In [1] a ruled based power management strategy is proposed, taking into account the state of charge of battery. In [2] a power following strategy is proposed, which forces output power of engine to follow the load requirements of vehicle. Global optimization strategy based on genetic algorithm (GA) and dynamic programming (DP) are presented in [3] and [4], respectively, which rely on given driving cycles and have huge amounts of computations. In [5, 6] an equivalent consumption minimization strategy is introduced which achieves the optimal management of power sources by establishing the conversion relationship between fuel and electricity. In [7] a control algorithm based on threelayer BP neural networks is presented, which is effective but hard to be achieved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
