An Adaptive Fuzzy Logic-Based Energy Management Strategy on Battery/Ultracapacitor Hybrid Electric Vehicles

Yin, He; Zhou, Wen‐Hao; Li, Mian; Ma, Chengbin; Zhao, Chen

doi:10.1109/tte.2016.2552721

Cited by 206 publications

(107 citation statements)

References 25 publications

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“…As consumer behavior is complex, this technique shows itself as a suitable instrument because, given initial parameters set, different situations can be modeled, allowing for the analysis of a particular behavior and the emergence of a global behavior [43][44][45].…”

Section: Model Specificationmentioning

confidence: 99%

A Behavioral Economics Approach to Residential Electricity Consumption

et al. 2017

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Consumer behavior is complex and is difficult to represent in traditional economic theories of decision-making. This paper focuses on the development of an agent-based approach to analyze people's behavior in consuming electricity using a behavioral economics framework, where the consumer is the main agent of power systems. This approach may bring useful insights for distribution companies and regulatory agencies, helping to shift thinking to a more user-centric approach. The emergent properties of electricity consumption are modeled by the means of consumer's heuristics, taking into account the electricity price, consumer's satisfaction level, willingness to invest in new technologies, social interactions, and marketing strategies by the power utility. Analysis on the emergent behavior of this approach through simulation studies showed that it is indeed valuable, as does not require in-depth data of all details on human behavior. However, it contributes to the understanding of relations among various objects involved in electricity consumption.

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Section: Model Specificationmentioning

confidence: 99%

A Behavioral Economics Approach to Residential Electricity Consumption

et al. 2017

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“…It is possible to divide EMS methods used in the battery/UC powered EVs as the rule‐based and optimization‐based methods . The rule‐based methods, deterministic and the fuzzy rule‐based methods, are easy for applications, and have tolerances to errors. In the deterministic rule‐based EMS, some scenarios are predetermined for the specific driving profiles.…”

Section: Introductionmentioning

confidence: 99%

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Demircali

Köroğlu

2020

Int J Energy Res

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Summary To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real‐time applications. The simulation results show that Jaya‐EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO‐EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real‐time applications.

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“…FIS-based methods manage hybrid-power sources according to formalized-expert experience and fuzzy inference. Because of their robustness and flexibility, many researchers adopted FIS-based strategies for real-time energy management [21][22][23]. Yang et al designed a two-input/one-output fuzzy-power controller with load current and battery SoC as inputs and fuel-cell reference current as output [24].…”

Section: Introductionmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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An Adaptive Fuzzy Logic-Based Energy Management Strategy on Battery/Ultracapacitor Hybrid Electric Vehicles

Cited by 206 publications

References 25 publications

A Behavioral Economics Approach to Residential Electricity Consumption

A Behavioral Economics Approach to Residential Electricity Consumption

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

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