Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

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 study, 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 SOC, three key control parameters are determined, and their affects on fuel consumption are analyzed. Third, an improved particle swarm optimization (IPSO) algorithm is proposed to overcome the disadvantage of PSO trapping into “local optimum” and improve optimization efficiency. To fully exploit the fuel-saving capability of HEV, driving cycle segmention is also considered. 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 urban section, 6.40% in roadway section and 5.40% in freeway section.

In this paper, based on the research of the first generation of a hybrid electric city bus, principle researches have been offered on the parameters matching of the power-train and main components for the new generation of hybrid electric city bus. The vehicle system was designed integrated, and then simulation platform was developed and established. The dynamic and economy performance can be predicted based on the above work, results show that the designed hybrid electric city bus can meet the performance demands and validate rationality of the parameters matching.