Extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy for power-split hybrid electric bus

2019 ◽  
Vol 234 (6) ◽  
pp. 1623-1633
Author(s):  
Xiaohua Zeng ◽  
Tong Liu ◽  
Dafeng Song ◽  
Nannan Yang ◽  
Haoyong Cui
Keyword(s):  
Kalman Filter ◽  
Model Predictive Control ◽  
Extended Kalman Filter ◽  
Predictive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Coordinated Control ◽  
Power Split ◽  
Hybrid Electric ◽  
Electric Mode

The power-split hybrid electric vehicle achieves excellent fuel economy because both the engine speed and the torque of this system are decoupled from the road load. However, for a power-split hybrid electric vehicle with multiple power sources, the inconsistency of the response characteristic of each power source seriously affects the stability control of the power system and riding comfort, so the coordinated control of the power system is particularly important. This article proposed a dynamic coordinated control strategy. First, extended Kalman filter is applied to realize robust online estimation of the engine dynamics. Then, an extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy is designed to achieve accurate reference tracking in hybrid electric mode. Considering the real-time performance for the online application of the dynamic coordinated control strategy, a fast model predictive control solver is formed based on a reasonable assumption. Offline simulation results show that accurate reference tracking is achieved in hybrid electric mode. Hardware-in-the-loop simulation is also conducted to validate the real-time performance of the proposed dynamic coordinated control strategy. This study is expected to improve the performance and robustness of the dynamic coordinated control strategy in hybrid electric mode while reducing the calibration load.

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.

Coordinated Control Strategy in Engine Starting Process for a Novel Compound Power-Split Hybrid Electric Vehicle

2018 ◽  
Author(s):  
Dengfeng Shen ◽  
Clemens Gühmann ◽  
Tong Zhang ◽  
Xizhen Dong
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuel Injection ◽  
Coordinated Control ◽  
Engine Torque ◽  
Power Split ◽  
Hybrid Electric ◽  
Starting Process ◽  
Clutch Torque

Due to the direct connection between the engine and the compound power split hybrid transmission (CPSHT) in hybrid electric vehicle (HEV), engine ripple torque (ERT) can result in obvious jerks in engine starting process (ESP). In order to improve the riding comfort, two wet clutches are mounted in this novel CPSHT. This research developed a new coordinated control strategy and its effectiveness was verified in simulation. Firstly, the mechanical and hydraulic parts of the CPSHT were introduced, and the riding comfort problem during ESP in primary design was illustrated. Secondly, the dynamic plant model including ERT, driveline model and clutch torque was deduced. Thirdly, a coordinated control strategy was designed to determine the target engine torque, motor torque, clutch torque and the moment of fuel injection. A Kalman filter based clutch torque estimator was applied with the help of electric motors information. The simulation result indicates that proposed coordinated control strategy can indeed suppress vehicle jerk and improve the riding comfort in ESP.

scholarly journals Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle With Consideration of Battery Aging

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ming Cheng ◽  
Bo Chen
Keyword(s):  
Model Predictive Control ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Nonlinear Model Predictive Control ◽  
Battery Model ◽  
Power Split ◽  
Hybrid Electric

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

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