Abstract
A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP) technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.
Highlights
Commercial transport vehicles, especially heavy-duty trucks, play an important role in the socialism construction of China
Compared to the preliminary rule-based control strategy, the fuel economy under the Dynamic programming (DP) optimal control law is improved by 15.9%
A feed-forward model of a heavy-duty parallel hybrid electric truck is established for studying the optimal energy management strategy
Summary
Commercial transport vehicles, especially heavy-duty trucks, play an important role in the socialism construction of China. More than a half of the freight transported in China is carried by heavy-duty trucks. The increasing number of merely gasoline/diesel-propelled trucks brings some negative impacts, such as excessive fuel consumption and severe air pollution. In order to reduce the dependence on carbonbased fuel and poisonous emissions, hybrid powertrain has been widely studied recently. Owing to the dual-powersource nature, the complex configuration, and the operation modes, the control strategy of hybrid electric vehicle (HEV) is typically more complicated than that of traditional enginebased vehicle. System-level vehicle simulation methodology is often applied to implement accurate sizing and matching studies, as well as to develop effective energy control algorithms, before the final design and physical prototyping
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