An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver

Abstract

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

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

“…ECMS is a mathematical model and optimization algorithm that transform trol strategy of a vehicle powertrain into a mathematical optimization problem minimize the objective cost function and obtain the optimal control sequence the development of hybrid technology in recent years, ECMS is gradually bein creasingly in HEVs. In global terms, the algorithm's fuel economy optimization closer to those of the global optimum, but compared to the global optimum c algorithm does not require prior knowledge of the full operating conditions, ducing the amount of computation required to calculate the optimum power d and making the application of ECMS to real vehicles more possible [125][126][127]. For HEVs with dual-motor powertrains, wang et al propose an EMS that takes into account the mode transition constraints [121].…”

“…With the development of hybrid technology in recent years, ECMS is gradually being used increasingly in HEVs. In global terms, the algorithm's fuel economy optimization results are closer to those of the global optimum, but compared to the global optimum control, the algorithm does not require prior knowledge of the full operating conditions, greatly reducing the amount of computation required to calculate the optimum power distribution and making the application of ECMS to real vehicles more possible [125][126][127].…”

Research on Global Optimization Algorithm of Integrated Energy and Thermal Management for Plug-In Hybrid Electric Vehicles

“…The battery thermal model describes the heat generation, heat exchange, and temperature change processes of the battery [31]. According to Bernardi equation, the battery heat generation 𝑄 ̇𝑏𝑎𝑡 is expressed as Equation (7). The first term in Equation ( 7) is the heat generation caused by the current thermal effect.…”

“…The energy management strategies (EMS) of PHEV are used to reasonably and effectively distribute the output power between the engine and battery to meet the vehicle demand power [ 4 , 5 ], which can be divided into three categories: rule-based strategy, instantaneous optimization-based strategy, and global optimization-based strategy [ 6 , 7 ]. Rule-based strategy has the lowest computational complexity compared with other strategies, so it is widely applied in online vehicle control.…”

“…Optimization-based strategies require establishing an objective function and usually combine with vehicle speed prediction [ 11 , 12 ] and driving condition identification [ 13 ] to minimize the objective function within the optimization prediction horizon. Equivalent consumption minimization strategy [ 7 , 14 ] and model prediction control [ 5 , 15 , 16 , 17 ] are common instantaneous optimization-based strategies, which have better economic efficiency but much higher computational complexity than rule-based strategies. In particular, global optimization-based strategies consider the entire vehicle driving cycle as a predictive horizon for the optimization problem, which is committed to obtaining the global optimal solution.…”