Design and Validation of Real‐Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles

Gao, Aiyun; Deng, Xiaozhong; Zhang, Mingzhu; Fu, Zhumu

doi:10.1155/2017/3095347

Cited by 17 publications

(9 citation statements)

References 21 publications

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“…The effectiveness of the proposed control method is appraised under the real driving cycle shown in Fig. 14 by using the data acquisition system named VBOX3i based on GPS [34]. Numerical values of the vehicle parameters and main parts are listed in Table 2.…”

Section: A Simulation Results and Analysismentioning

confidence: 99%

Dynamic Coordinated Control Based on Sliding Mode Controller During Mode Switching With ICE Starting for an HEV

Gao

Tao

2020

IEEE Access

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Due to the friction-induced discontinuity of the clutch torque and ICE on/off, seamless mode transition of hybrid electric vehicles (HEVs) is difficult to achieve, which has a bad influence on the vehicle ride comfort. In the face of system uncontinuity and strong nonlinearity during mode switching with ICE starting, a control strategy of torque dynamic coordination is proposed by means of sliding mode control based on disturbance compensation. Firstly, the steady-state and transient models of parts and working modes are built, which improves modeling accuracy and adaptability to transient driving cycle. Furthermore, the switching process from pure electric driving to hybrid driving is divided into three phases including internal combustion engine (ICE) starting, speed synchronization and torque redistribution. The design of according disturbance observer and sliding mode controller is described in detail. Lastly, compared with other two control strategies, the rationality and validity of the control method designed are testified not only by computer simulations but also experimental tests under the comprehensive driving cycle of local passenger vehicles. The potential of the proposed control strategy in terms of power transfer smoothness and improving riding comfort is illustrated. INDEX TERMS Hybrid electric vehicle (HEV), coordinated control, sliding mode control, mode switching.

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Section: A Simulation Results and Analysismentioning

confidence: 99%

Dynamic Coordinated Control Based on Sliding Mode Controller During Mode Switching With ICE Starting for an HEV

Gao

Tao

2020

IEEE Access

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“…By introducing a penalty function, the strategy makes a good charging sustaining characteristic and is better suitable for charging sustaining period of the plug-in HEV, so it is widely studied. [26][27][28] As shown in figure 1, the values of charging and discharging equivalent factor must be set well before calculating ECMS. At the same time, the values are related to the driving cycle type.…”

Section: Control Flow Of Dynamic Ecms Based On the Dcrmentioning

confidence: 99%

Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

Shi

Qiu

et al. 2018

Advances in Mechanical Engineering

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For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

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“…7,8 Rule-based EMS, mainly including deterministic rules control strategy and fuzzy logic control strategy, relies on the formulation of a series of rules according to engineering experience. 9,10 It is easy to operate and more acceptable for the actual application, but the adaptability to driving cycles of it is poor. 11 To bridge the shortcoming, some research tries to adopt optimization methods to improve the control performance of rule-based EMS.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Lin

Wang

2020

Int J Energy Res

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Summary The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV.

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Design and Validation of Real‐Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles

Cited by 17 publications

References 21 publications

Dynamic Coordinated Control Based on Sliding Mode Controller During Mode Switching With ICE Starting for an HEV

Dynamic Coordinated Control Based on Sliding Mode Controller During Mode Switching With ICE Starting for an HEV

Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

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