A state of charge planning method of a plug-in hybrid electric truck with readily available navigation signals

Optimal energy management for electrified vehicles can be achieved with a prior understanding of the velocity profile, whose prediction accuracy is influenced by the stochastic uncertainty of real driving cycles. This paper proposes a new state of charge planning method for plug-in hybrid electric trucks. The strategy eliminates the need for velocity prediction and relies solely on some readily available signals, such as estimated remaining distance and travel time. This exemption from velocity prediction avoids expensive computational costs, making it possible to use a more affordable processor. To test the strategy, four random driving cycles representing two different vocational uses are selected. The results show that the proposed strategy only increases fuel consumption by 1.3% for unknown urban and long-haul delivery cycles compared to the optimal strategy. Additionally, a sensitivity study reveals the robustness of the method on inaccurate navigation signals. These findings demonstrate that the proposed method is efficient and adaptive, making it suitable for existing truck applications without the need for additional hardware.