Segemented Driving Cycle Based Optimization of Control Parameters for Power-Split Hybrid Electric Vehicle With Ultracapacitors

Abstract

As energy storage device of hybrid electric vehicles (HEVs), ultracapacitors feature the advantages of higher power density and longer life cycle compared with batteries. However, when ultracapacitors of the same power level are used instead of batteries, fuel consumption becomes more sensitive to changes in control parameters as ultracapacitors store much less energy, and the state of charge (SOC) is power-sensitive. In this paper, optimization of control parameters for a power-split HEV with ultracapacitors is addressed to achieve better fuel economy. First, a power-split HEV model and a corresponding control strategy are established under the MATLAB script environment for convenient analysis and application of an optimization algorithm. Second, focusing on the power-sensitive characteristic of the SOC, three key control parameters are determined, and their effects on fuel consumption are analyzed. Third, an improved particle swarm optimization (IPSO) algorithm is proposed to overcome the disadvantage of the PSO trapping into “local optimum” and improve optimization efficiency. To fully exploit the fuel-saving capability of the HEV, driving cycle segmentation is also considered. The IPSO is used to optimize three key control parameters under the segmented adapted world transient vehicle cycle. Finally, the optimal results are applied to hardware-in-the-loop test to verify the effectiveness of the proposed optimization method. Compared with the fuel consumption before optimization, the fuel saving rate reaches 9.20% in the urban section, 6.40% in the roadway section, and 5.40% in freeway section.

Keywords

• Control strategy optimization
• ultracapacitors
• power-split hybrid system
• improved particle swarm algorithm