Model Predictive Control for Nonlinear Energy Management of a Power Split Hybrid Electric Vehicle

2018 ◽  
pp. -1--1
Author(s):  
Dehua Shi ◽  
Shaohua Wang ◽  
Yingfeng Cai ◽  
Long Chen ◽  
ChaoChun Yuan ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Power Split ◽  
Hybrid Electric ◽  
Nonlinear Energy

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.

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.

A Stochastic Model Predictive Control Approach for Hybrid Electric Vehicle Energy Management With Road Grade Preview

2014 ◽  
Author(s):  
Xiangrui Zeng ◽  
Junmin Wang
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
The Road ◽  
Road Grade ◽  
Hybrid Electric ◽  
Stochastic Model Predictive Control

Road grade preview can benefit the hybrid electric vehicle (HEV) energy management because the energy efficiency performance degrades significantly when the battery state of charge (SOC) reaches its boundaries and the road grade has a great influence on the battery SOC balance. In reality the road grade in front may be a random variable as the future route may not always be known to the vehicle controller. This paper proposes a stochastic model predictive control (MPC) approach which does not require a determined route known in advance. The road grade is modeled as a Markov chain and all the possible future routes are considered in building the transition matrix. A large-time-scale HEV energy consumption model is built. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming (SDP). Simulation results show that the proposed approach can prevent the battery SOC from reaching its boundaries and maintain good fuel efficiency by the stochastic road grade preview.

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