Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

Abstract

Based on observations of the behaviour of the optimal solution to the problem of energy management for plug-in hybrid electric vehicles, a novel real-time Energy Management Strategy (EMS) is proposed. In particular, dynamic programming results are used to derive a set of rules aiming at reproducing the optimal gearshift schedule in electric mode while the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) is employed to decide the powertrain operating mode and the current gear when power from the internal combustion engine is needed. In terms of total fuel consumption, simulations show that the proposed approach yields results that are close to the optimal solution and also outperforms those of the A-ECMS, a well-known EMS. One of the main aspects that differentiates the strategy here proposed from previous works is the introduction of a model to use physical considerations to estimate the energy consumption during gearshifts in dual-clutch transmissions. This, together with a series of properly tuned fuel penalties allows the controller to yield results in which there is no gear hunting behaviour.

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

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An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

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Energy Management Strategy for P1 + P3 Plug-In Hybrid Electric Vehicles

“…In the past, diverse energy management systems for hybrid vehicles have been developed, encompassing both optimization-based and rule-based control strategies [8][9][10][11][12][13][14][15][16][17][18][19][20]. Optimization-based energy management systems derive power distribution rules among multiple energy sources by solving intricate optimization problems, whereas rule-based systems primarily rely on heuristics.…”

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