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a b s t r a c tIn order to achieve near-optimal fuel economy for plug-in hybrid electric vehicles (PHEVs) using the equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely used model-based approaches, this paper proposes a data-driven ECMS that determines the EF using an artificial neural network (ANN). First, by comparing Pontryagin's Minimum Principle (PMP) with the ECMS, one can find that the EF is related to the co-state value of the PMP method. Then, an ANN is constructed with three accessible input variables, including the current demanded power, the ratio of the distance travelled to the total distance, and the battery State of Charge (SOC). The neural network is subsequently trained using real-world speed profiles. Simulations are performed considering different initial SOC values. The results reveal that the proposed data-driven ECMS demonstrates satisfactory fuel economy compared to global optimization methods like dynamic programming and PMP methods. The computational time of the proposed method relative to the duration of the entire trip indicates a great potential for the development of a time-conscious energy management strategy. Moreover, the impact of training sample size on the ANN performance is discussed.

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Pontryagin’s Minimum Principle (PMP) has a significant computational advantage over dynamic programming for energy management issues of hybrid electric vehicles. However, minimizing the total energy consumption for a plug-in hybrid electric vehicle based on PMP is not always a two-point boundary value problem (TPBVP), as the optimal solution of a powering mode will be either a pure-electric driving mode or a hybrid discharging mode, depending on the trip distance. In this paper, based on a plug-in hybrid electric truck (PHET) equipped with an automatic mechanical transmission (AMT), we propose an integrated control strategy to flexibly identify the optimal powering mode in accordance with different trip lengths, where an electric-only-mode decision module is incorporated into the TPBVP by judging the auxiliary power unit state and the final battery state-of-charge (SOC) level. For the hybrid mode, the PMP-based energy management problem is converted to a normal TPBVP and solved by using a shooting method. Moreover, the energy management for the plug-in hybrid electric truck with an AMT involves simultaneously optimizing the power distribution between the auxiliary power unit (APU) and the battery, as well as the gear-shifting choice. The simulation results with long- and short-distance scenarios indicate the flexibility of the PMP-based strategy. Furthermore, the proposed control strategy is compared with dynamic programming (DP) and a rule-based charge-depleting and charge-sustaining (CD-CS) strategy to evaluate its performance in terms of computational accuracy and time efficiency.