An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles

Yang, Chao; Liu, Kaijia; Jiao, Xiaohong; Wang, Weida; Chen, Ruihu; You, Suya

doi:10.1016/j.energy.2021.122120

Cited by 33 publications

(8 citation statements)

References 29 publications

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“…en, the master controller analyzes, calculates, and corrects the error, nally assisting the driver to complete the operation of the actuator [5,6].…”

Section: Introductionmentioning

confidence: 99%

Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition

Li¹,

Zhao²,

Zhu³

et al. 2022

Computational Intelligence and Neuroscience

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To improve the braking performance and braking energy feedback rate of extended-range electric vehicles, a driving intention recognition model is established based on Markov theory with brake pedal displacement, pedal displacement change rate, and pedal force as parameters, and the validity of the model is verified by actual vehicle test data. Based on the driving intention recognition model, a compound braking control strategy for extended-range electric vehicles is established with the constraints of braking force distribution and motor and battery characteristics. Cruise and MATLAB are used for joint simulation. The simulation results show that the compound braking system of extended-range electric vehicles with the compound braking control strategy based on brake intention recognition can work stably and effectively. On the premise of ensuring braking stability and safety, the braking energy recovery efficiency can be increased by 0.36% and the recovery rate can reach 12.88%. The compound braking system can effectively recover braking energy, improve the energy utilization rate of extended-range electric vehicles, and increase driving range.

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“…en, the master controller analyzes, calculates, and corrects the error, nally assisting the driver to complete the operation of the actuator [5,6].…”

Section: Introductionmentioning

confidence: 99%

Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition

Li¹,

Zhao²,

Zhu³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Similarly, to enhance regenerative braking performance in EVs with SRM, 111 the MOOS 112,113 is proposed to improve regenerative braking performance under sliding braking conditions. Under sliding braking situations, this MOOS‐based management technique may substantially improve brake smoothness, expand working distance, and lengthen the battery lifespan of EVs 90,114 . This control approach can attain maximum energy efficiency and performance in different braking conditions.…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

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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“…The algorithm achieves efficient optimization of control parameters. [ 32 ] Du proposed a multi‐objective EMS based on particle swarm optimization (PSO) algorithm. The energy consumption of the vehicle was reduced and the problem of excessive battery discharge was alleviated.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Optimization for a Novel Electric‐hydraulic Power Coupling Vehicle Considering the Influence of Multiple Driving Cycles

2023

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Hydraulic hybrid technology can further improve the economy of electric vehicles (EV). This paper investigates a novel electro‐hydraulic power coupling vehicle (EHPCV), which is endowed with multiple drive modes and energy regenerative braking modes. The electric‐hydraulic power coupler (EHPC) is an innovative device for realizing power coupling and conversion in the powertrain. The design and optimization of energy management strategies (EMS) are key to ensuring the efficient and stable operation of hybrid electric vehicles (HEV). Based on the analysis of energy flow, a rule‐based energy management strategy (RB‐EMS) is built. To achieve more reasonable energy management of the EHPCV in random working environments, the article proposes an optimization framework for the EMS based on multiple driving cycles. More precisely, a multi‐objective optimization mathematical model is established with the goal of maximizing the battery state of charge (SOC) and minimizing speed error. Moreover, use the optimal Latin hypercube design (OLHD) to select the design variables that have a significant impact on the optimization objective. Definitively, the NSGA‐II algorithm combines with the RB‐EMS to optimize the control parameters. The verification results show that the optimized EMS enables the EHPCV to have greater economic advantages. The research achievements of this paper provide theoretical support and important reference for the energy management optimization of electric‐hydraulic hybrid vehicles (EHHV) in the future.This article is protected by copyright. All rights reserved.

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An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles

Cited by 33 publications

References 29 publications

Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition

Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition

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

Energy Management Strategy Optimization for a Novel Electric‐hydraulic Power Coupling Vehicle Considering the Influence of Multiple Driving Cycles

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