Abstract
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.
Highlights
Due to increasingly severe air pollution and energy crisis, as well as growingly stringent emission regulations, automotive manufactures over the world have been paying enormous attention to electrified vehicles [1,2,3]
Both global optimal methods (DP and Pontryagin’s Minimum Principle (PMP)) produce almost the same total cost, and the slight difference between them is due to the fact that the solution accuracy of dynamic programming (DP) is influenced by the discretization scale of the battery SOC and the interpolation method used to estimate the cost-to-go; for the latter, the converging factor exerts a direct effect on the calculative precision
To make a PMP-based energy management strategy for plug-in hybrid electric vehicles (PHEVs) resilient against scenarios with different driving distances, this paper proposes an integrated control strategy by meticulously manipulating the electric-only mode and the hybrid powering mode
Summary
Due to increasingly severe air pollution and energy crisis, as well as growingly stringent emission regulations, automotive manufactures over the world have been paying enormous attention to electrified vehicles [1,2,3]. For a PHEV, the PMP-based energy management issue over a known driving cycle will exhibit a completely different feature, because the lower SOC boundary value highly related to the driving distance cannot be set in advance [23,24]. Various PMP-based control strategies that focus on the hybrid discharging mode and are examined by running cycles with definitive upper and lower SOC boundaries have been proposed. We propose a flexible PMP-based energy management controller by integrating electric-only and hybrid powering modes, so as to adapt to any given testing cycle. A comprehensive comparison with existing methods is perfoTrhme eredmianinSdeerctoifonthi6s,pfaoplelor wis eodrgbanyizmedaains fcoollnowclsu: sSieoctniosnin Sdeescctriiboens 7th.e powertrain configuration and its modeling, and we formulate the energy management problem based on PowertrainPexMaMmP oiinndeSseelctithinoengp3r.oApnosiendtegmraettehdodconwtirtohl strategy and its solution different scenarios.
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