Engine Control of Power-Split Hybrid Vehicle

2013 ◽  
Vol 392 ◽  
pp. 267-271 ◽  
Author(s):  
Guang Ping Wang ◽  
Qing Nian Wang ◽  
Peng Yu Wang ◽  
Wen Wang
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Engine Speed ◽  
Loading Rate ◽  
Hybrid Vehicle ◽  
Experimental Results ◽  
Engine Control ◽  
Common Structure ◽  
Power Split

Planetary power-split hybrid vehicle is a very common structure of hybrid electric vehicle (HEV), which achieves speed and torque double decoupled between engine and wheels. Engine always working on optimal curve is an important reason for low fuel-efficient. Engine speed is controlled by PID, and the optimal loading rate of engine is solved from engine characteristic curves; finally realization of engine working on optimal curve. Offline simulation is used for this algorithm, and comparing with many experimental results demonstrates engine works on operating point; fuel consumption is close to the official fuel consumption of Prius.

An Energy Management Strategy for Power-split Hybrid Electric Vehicle using Model Predictive Control

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Muhammad Zahid ◽  
Naseer Ahmad
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Specific Fuel Consumption ◽  
Power Split ◽  
Hybrid Electric

To fulfil future demand for energy and to control pollution, a power-split hybrid electric vehicle is a promising solution combining attributes of a conventional vehicle and an electric vehicle. Since energy is available from two subsystems i.e, engine and battery, there is the freedom to manage it optimally. In this work, model predictive control strategy, that has the constraint handling which makes it a better choice over other strategies for efficient energy management of hybrid electric vehicles. A detailed mathematical model of the power split configured hybrid electric vehicle is developed that encompasses the engine, planetary gear, motor/generator, inverter, and battery. An interior-point optimizer based-nonlinear model predictive control strategy is applied to the developed model by incorporation of operational constraints and cost function. The objective is to curtail fuel consumption while the battery’s state of charge should be maintained within predefined limits. The complete developed model was simulated in MATLAB for motor, generator, engine speed, and battery SoC. Computed specific fuel consumption from the proposed MPC during the NEDC and the HWFET cycles are 4.356liters/100km and 2.474 litres/100 km, respectively. These findings are validated by the rule-based strategy of ADVISOR 2003 that provides 4.900 litres/100 km and 3.600 litres/100 km over the NEDC and the HWFET cycles, respectively. This indicates that the proposed MPC shows 11.11 % and 31.26 % improvement in specific fuel consumption in the NEDC and HWFET drive cycles respectively.

An adaptive equivalent consumption minimization strategy considering catalyst temperature for plug-in hybrid electric vehicle

2021 ◽  
pp. 095440702110434
Author(s):  
Tao Deng ◽  
Ke Zhao ◽  
Haoyuan Yu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Test Procedure ◽  
Catalyst Temperature ◽  
Equivalent Factor ◽  
Simulation Results ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).

scholarly journals Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

2018 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Nicolas Sockeel ◽  
Jian Shi ◽  
Masood Shahverdi ◽  
Michael Mazzola
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Control Solution ◽  
Current Time ◽  
Battery Model ◽  
Hybrid Electric

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

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