Fuzzy Adaptive-Equivalent Consumption Minimization Strategy for a Parallel Hybrid Electric Vehicle

Wang, Shuhan; Huang, Xingshuai; López, José M.; Xu, Xiangyang; Dong, Peng

doi:10.1109/access.2019.2941399

Cited by 56 publications

(26 citation statements)

References 39 publications

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“…Out of these three, the A-ECMS was yielded better results as compared to the other two strategies in [19]. The table 5 below shows a comparison of the proposed study with the strategies implemented in [13]. In [16], another study using GA-optimized fuzzy logic controller and non optimized fuzzy logic controller, the SOC profile showed good results.…”

Section: Resultsmentioning

confidence: 96%

“…When compared to the previous studies, the ANFIS controller used in this paper yields better results. In [13], ECMS (Equivalent consumption minimization strategy), A-ECMS (Adaptive equivalent consumption minimization strategy) and Fuzzy A-ECMS strategies were implemented. Out of these three, the A-ECMS was yielded better results as compared to the other two strategies in [19].…”

Section: Resultsmentioning

confidence: 99%

“…It is the more advanced and accurate system which is based on the data pairs of the input/output of the system which is under consideration. It is used for the modeling as well as control of the systems where there is hint of uncertainty in the system [13]. Thus, this is an intelligent system which can be used as an effective controller in the HEV energy management strategy.…”

Section: Adaptive Nuero Fuzzy Inference System (Anfis) For Hybrid Electric Vehiclesmentioning

confidence: 99%

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Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

et al. 2021

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Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Adaptive Nuero Fuzzy Inference System (Anfis) For Hybrid Electric Vehiclesmentioning

confidence: 99%

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Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

et al. 2021

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“…Autonomous internal combustion (IC) engine and electric vehicles are under rapid development in automobile industry and will become the near-future transportation tools. 1–4 Four in-wheel-motor independent-drive electric vehicles (4MIDEVs) installed with four driving motors in the inner spaces of the four respective wheels have the prominent advantages of short driving chain, high efficiency, compactness, and maneuverability. 5,6 Autonomous vehicles could avail themselves of decoupled controllable actuation and fast and accurate torque response to vehicles, thanks to four wheels independently driven by four respective in-wheel motors; thereby, autonomous four in-wheel-motor independent-drive vehicles (AMIDVs) are attributed as a promising and effective approach for autonomous ground vehicles (AGVs) to make full use of road adhesion, enhance active safety, and decrease maneuverability costs on the basis of realizing the goal of path tracking.…”

Section: Introductionmentioning

confidence: 99%

Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

Chen

Peng

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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How to enhance and ensure the vehicle yaw/lateral stability and safety performance is a crucial and worthy research problem for the autonomous four in-wheel-motor independent-drive vehicle. In this paper, a hierarchical path tracking control strategy coordinated with the direct yaw moment control for autonomous four in-wheel-motor independent-drive vehicle is proposed with consideration of the lateral stability. In the upper controller, a novel model predictive control method based on the vehicle 7-degree-of-freedom dynamic model is presented to obtain the front wheel steering angle and two virtual generalized forces, the traction and yaw moment. A dynamic adjustment method of target weight based on variance adjustment factor is proposed to realize multi-objective cooperative control of maneuverability, path tracking accuracy, and yaw stability. Moreover, the tire stability margin and its standard deviation are put forward in the objective function of the lower optimal generalized force distributor, making full use of the road adhesion of each wheel. Carsim–Simulink joint simulation results illustrate that the proposed coordinated control strategy could prosper the multi-objective coordinated control. In addition, the path tracking coordinated control strategy could further improve the lateral stability and safety of the vehicle compared with the mere path tracking control strategy, when autonomous four in-wheel-motor independent-drive vehicle is placed under extremely dangerous road conditions.

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“…Furthermore, fuzzy logic controllers were proposed to improve the energy efficiency of hybrid vehicles in parallel configurations 9 even with functions of machine learning 10 and adaptive-equivalent strategy. 11 The basic principle of fuzzy rules is to classify complicated input conditions into operation intervals to optimize output efficiency. Therefore, fuzzy logic was demonstrated to have the potential to improve fuel economy which maximizes engine efficiency.…”

Section: Introductionmentioning

confidence: 99%

Development of energy control system for parallel hybrid power system using dynamic equations and fuzzy theory

Chen

Yang

Huang

2014

Advances in Mechanical Engineering

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A fuzzy control strategy is developed in this study to manage the parallel hybrid power system of internal combustion engine (ICE) and electric motor (EM) for hybrid vehicles. The rules established for the fuzzy logic are based on the conditions of vehicle pedal position, vehicle velocity, and the state of charge to control the throttle position of the ICE and the switch position of EM in low-, mid-, and high-power cruising. The optimization of the control strategy can make vehicles achieving ECE 40 driving pattern. In addition, the ICE can work in an optimal operation range, thus reducing carbon emission. The EM may provide power according to the demand, such that the torque output of the output shaft of the power split device is twice of the input of the two power sources separately.

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Fuzzy Adaptive-Equivalent Consumption Minimization Strategy for a Parallel Hybrid Electric Vehicle

Cited by 56 publications

References 39 publications

Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

Development of energy control system for parallel hybrid power system using dynamic equations and fuzzy theory

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