Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles

Rahmeh, Hadi; Bonfitto, Angelo; Ruzimov, Sanjarbek

doi:10.1115/1.0002137v

2021

DOI: 10.1115/1.0002137v

|View full text |Cite

Preprint

Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles

Hadi Rahmeh¹,

Angelo Bonfitto²,

Sanjarbek Ruzimov³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Ezemobi

Yakhshilikova

Ruzimov

et al. 2022

WEVJ

View full text Add to dashboard Cite

The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

show abstract

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Ezemobi

Yakhshilikova

Ruzimov

et al. 2022

WEVJ

View full text Add to dashboard Cite

show abstract

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

Haspolat

Yalçın

2023

WEVJ

View full text Add to dashboard Cite

Hybrid electric vehicles (HEVs) are used as a bridge during the transition to battery electric vehicles (BEVs) and to make energy consumption more efficient. The main problem in improving the efficiency of HEV energy consumption is torque management. In this study, a novel approach based on a nonlinear model predictive controller to solve the reference tracking and torque distribution problem is proposed. That is to say, in order to increase the efficiency of torque distribution, the weights of nonlinear model predictive control (NMPC) are trained with a Deep Q Network (DQN), and an event-triggered mechanism is designed with DQN to reduce the computational cost of MPC. The considered torque distribution problem varies according to the type and structure of the HEV. In this study, a parallel type 2 hybrid electric vehicle (P2 HEV) is considered and modeled via publicly shared passenger vehicle data of the engine, motor, high-voltage battery, transmission, clutch, differential, and wheel characteristics. NMPC is formulated so that the torque values remain within the physical limits of the engine, and the battery also operates at its physical limits. Namely, it is guaranteed that the battery works according to a certain state of charge (SOC) window and current limits. The state of health (SOH) of the battery is also considered in the optimization. The motor and engine efficiencies increase by 3.61% and 2.86%, respectively, with the proposed control structure, while the computational cost is reduced by 52.01% when utilizing the proposed event-triggering mechanism in the NMPC controller.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles

Cited by 2 publications

References 0 publications

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

Contact Info

Product

Resources

About