Fuzzy energy management strategy for hybrid electric vehicles on battery state-of-charge estimation by particle filter

Feng, Na; Ma, Tiehua; Chen, Changxin

doi:10.1007/s42452-022-05131-8

Cited by 8 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6. Authors in [87] employ FLC-charge and FLC-discharge controllers for Electric Vehicles (EVs) with hybrid battery and Supercapacitors (SC), dynamically distributing power based on load power, battery SoC, and SC SoC. This adaptive scheme, utilizing particle filters for battery SoC estimation, enhances performance by minimizing charge and discharge currents.…”

Section: Figure 5 Adaptive Flc Ems Classificationmentioning

confidence: 99%

Energy Management in Hybrid Electric and Hybrid Energy Storage System Vehicles: A Fuzzy Logic Controller Review

Maghfiroh,

Wahyunggoro,

Cahyadi

2024

IEEE Access

View full text Add to dashboard Cite

The transportation sector, a significant contributor to carbon dioxide emissions as of 2020, confronts a pressing challenge in mitigating pollution. Electric Vehicles (EVs) present a promising solution, offering a cleaner alternative; however, their limited travel range poses a constraint. Hybrid Electric Vehicles (HEVs) and Hybrid Energy Storage System Electric Vehicles (HESS EVs) emerge as economically feasible compromises. Nonetheless, the effective management of energy and the optimization of power source size remain crucial challenges for both HEVs and HESS EVs. Among various Energy Management Strategies (EMS), the Fuzzy Logic Controller (FLC) stands out for its performance, simplicity, and real-time applicability. This article comprehensively explores the diverse applications of FLC as an EMS in both HEVs and HESS EVs, providing a comparative analysis with other EMS methods and delving into the advantages and challenges associated with each approach. A detailed examination of various FLC types employed as EMS has been conducted, drawing insights from a multitude of references. Each class of FLC EMS is scrutinized, presenting a broad overview of proposed methodologies within each category. By providing this comprehensive information, the article equips readers with foundational knowledge and insights for the continued development of FLC EMS in hybrid electric and hybrid energy storage system electric vehicles.INDEX TERMS energy management, hybrid vehicle, electric vehicle, fuzzy logic controller, review I. INTRODUCTION

show abstract

Section: Figure 5 Adaptive Flc Ems Classificationmentioning

confidence: 99%

Energy Management in Hybrid Electric and Hybrid Energy Storage System Vehicles: A Fuzzy Logic Controller Review

Maghfiroh,

Wahyunggoro,

Cahyadi

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Knowledge of the nervous system, particularly the human brain and its densely coupled neurons, inspired the central concept. Because of its high nonlinearity, massive volumes of data-parallel processing, and high robustness, artificial neural networks (ANN) are one of the methods that are suited for dealing with the internal relations of a complicated model [34].…”

Section: Designing and Programming Ann Modelsmentioning

confidence: 99%

Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles

Bourenane¹,

Berkani²,

Negadi³

et al. 2023

JESA

View full text Add to dashboard Cite

Integrating solar energy in electric vehicles (EV) is expected to play a dominant role in the decarbonization of the transportation sector as well as reducing the charging costs. These integrated photovoltaic automobiles are particularly adapted for urban driving that is to say the driving range is limited. On that account, the use of a feasible energy storage system is necessary to boost the driving mileage. This paper uses a hybrid energy system, containing a battery as the main storage device and a supercapacitor (SC) as a backup. This combination was suggested in order to negate the former's deficiencies. The use of a hybrid energy source leads to the necessity of introducing a robust power management strategy to guarantee the optimal power flow in electric vehicle components. An artificial neural network is then trained with model calculation for the power management approach for the traction chain. The results of the simulation indicate that the proposed system is efficient in improving the energy management of the vehicle. Moreover, the system's simplicity could potentially make it easier to implement in real-time using a DSP or a DSPACE platform.

show abstract

“…There are various power modes for an EV based on the motor, and HESS states [24][25][26]: Initialization: When the vehicle starts, HESS needs to provide high torque quickly. At this time, the power is only provided by the SC, taking advantage of its high-power density.…”

Section: Power Modesmentioning

confidence: 99%

Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control

Rimpas,

Kaminaris,

Piromalis

et al. 2023

Mathematics

View full text Add to dashboard Cite

Following the European Climate Law of 2021 and the climate neutrality goal for zero-emission transportation by 2050, electric vehicles continue to gain market share, reaching 2.5 million vehicles in Q1 of 2023. Electric vehicles utilize an electric motor for propulsion powered by lithium batteries, which suffer from high temperatures caused by peak operation conditions and rapid charging, so hybridization with supercapacitors is implemented. In this paper, a fuzzy logic controller is employed based on a rule-based scheme and the Mamdani model to control the power distribution of the hybrid system, driven by the state of charge and duty cycle parameters. An active topology with one bi-directional DC-to-DC converter at each source is exploited in the MATLAB/Simulink environment, and five power states like acceleration and coasting are identified. Results show that the ideal duty cycle is within 0.40–0.50 as a universal value for all power states, which may vary depending on the available state of charge. Total efficiency is enhanced by 6%, sizing is increased by 22%, leading to a more compact layout, and battery life is extended by 20%. Future work includes testing with larger energy sources and the application of this management strategy in real-time operations.

show abstract

Fuzzy energy management strategy for hybrid electric vehicles on battery state-of-charge estimation by particle filter

Cited by 8 publications

References 30 publications

Energy Management in Hybrid Electric and Hybrid Energy Storage System Vehicles: A Fuzzy Logic Controller Review

Energy Management in Hybrid Electric and Hybrid Energy Storage System Vehicles: A Fuzzy Logic Controller Review

Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles

Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control

Contact Info

Product

Resources

About