Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

Duan, Bin; Wang, Q. N.; Wang, J. N.; Li, X. N.; Ba, Te

doi:10.1007/s12239-017-0034-4

Cited by 29 publications

(11 citation statements)

References 17 publications

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“…For a fair comparison, all simulation scenarios are run for the same driving cycle. The used driving cycle is the New European Driving Cycle (NEDC) [14], [33].…”

Section: Simulation Resultsmentioning

confidence: 99%

Energy Management System for Fuel Cell-Battery Vehicles Using Multi objective Online Optimization

et al. 2022

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An Energy Management system for fuel cell/battery vehicles manages and controls the fuel consumption and affects the lifetime of the powertrain system. This paper proposes a new Energy Management System that is based on a multi-objective online optimization technique. The proposed Energy Management System objective is to minimize a cost function that is composed of four terms: actual fuel consumption, two terms representing the stresses on the lifetimes of the powertrain system components ( the fuel cell and the battery), and a fourth term representing a penalty on the deviation from a desired SOC of the battery. This minimization is performed with no need for prior knowledge of the driving cycle. The proposed Energy Management System couples fuel consumption minimization with a frequency-decoupling-based technique to split the power demand among available power sources to relieve the stresses on those sources. The proposed Energy Management System shows flexibility to address multiple objectives and can simply be deployed as a real-time strategy. An analytical derivation is given, and simulation results show the effectiveness of the proposed approach. A comparison is performed between the proposed Energy Management System and a Dynamic-Programming-based Energy Management System. The comparison shows that the proposed Energy Management System achieves a near-optimal solution without the need for prior knowledge of the driving cycle.

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“…For a fair comparison, all simulation scenarios are run for the same driving cycle. The used driving cycle is the New European Driving Cycle (NEDC) [14], [33].…”

Section: Simulation Resultsmentioning

confidence: 99%

Energy Management System for Fuel Cell-Battery Vehicles Using Multi objective Online Optimization

et al. 2022

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“…EVs have a separate hydraulic system consisting of brake fluid, master cylinder, pistons, and fluid flow channels for this type of friction braking actuation. 164,165 Depending on the output from the brake controller as per the driver demand, the brake fluid is displaced from the master cylinder. This further engages the pistons at the brake calipers, which holds the disc brakes and brings the vehicle to a halt immediately.…”

Section: Hydraulic Braking Systemmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…Using Equations ( 2), ( 7), (8), and ( 16) into (10), expansions of the Hamiltonian (10) are reformulated by…”

Section: Construction Of Optimal Databasementioning

confidence: 99%

Adaptive equivalent consumption minimization strategy and its fast implementation of energy management for fuel cell electric vehicles

Cheng

Zhu

et al. 2022

Intl J of Energy Research

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The equivalent consumption minimization strategy is widely used in real-time energy optimization for fuel cell electric vehicles due to its approximate optimality and good real-time performance. However, equivalent factors have always been difficult to select in the research of this algorithm, and the existing methods have insufficient adaptability and robustness. In this paper, an equivalent factor predictor based on a neural network is proposed, which can predict the equivalent factor in real time by considering various operating conditions and vehicle states. In addition, we improve the real-time performance of the equivalent consumption minimum strategy. A new method to quickly solve the continuous optimal solution of the algorithm by transforming the objective function into the quadratic function of the control variable is studied. Simulation and experimental results show that the designed equivalent consumption minimization strategy can ensure state of charge of the battery in a better range, and the energy saving effect is approximately 3% more than that of dynamic programming and 8% to 10% less than that of the traditional rule algorithm. Finally, the proposed method saves 2 to 3 orders of magnitude in computation time compared with the traditional equivalent consumption minimum strategy.

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Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

Cited by 29 publications

References 17 publications

Energy Management System for Fuel Cell-Battery Vehicles Using Multi objective Online Optimization

Energy Management System for Fuel Cell-Battery Vehicles Using Multi objective Online Optimization

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Adaptive equivalent consumption minimization strategy and its fast implementation of energy management for fuel cell electric vehicles

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