Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle

Silva, Samuel Filgueira da; Eckert, Jony Javorski; Corrêa, Fernanda Cristina; Silva, Fabrício Leonardo; Silva, Ludmila C. A.; Dedini, Franco Giuseppe

doi:10.1016/j.apenergy.2022.119723

Cited by 31 publications

(9 citation statements)

References 118 publications

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“…As a result of their simulation study, Song et al [61] argued that the total cost of the HESS during a 10year operation, including capital costs, electricity costs, and battery replacement costs, is 25.9% less than current battery prices. Da Silva et al [62] developed a multi-objective optimization dual HESS and achieved a 19.57% range increase and 22.88% battery life extension compared to a single HESS-equipped EV. In the existing literature studies, a HESS The ADVISOR program, on the other hand, starts the battery's initial SoC value with only 70%.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Şen,

Özcan,

Eker

2024

Applied Sciences

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Electric vehicles (EVs), which are environmentally friendly, have been used to minimize the global warming caused by fossil fuels used in vehicles and increasing fuel prices due to the decrease in fossil resources. Considering that the energy used in EVs is obtained from fossil resources, it is also important to store and use energy efficiently in EVs. In this context, recovery from a regenerative braking system plays an important role in EV energy efficiency. This paper presents a fuzzy logic-based hybrid storage technique consisting of a supercapacitor (SC) and battery for efficient and safe storage of a regenerative braking system. First, the constraints of the battery to be used in the EV for fuzzy logic control are identified. Then, the fuzzy logic system is created and tested in the ADVISOR and Siemens Simcenter Flomaster programs in the New European Driving Cycle (NEDC) driving cycle. A SC was selected for primary storage to prevent the battery from being continuously charged from regenerative braking, thus reducing its lifetime. In cases where the vehicle consumes more energy than the average energy consumption, energy consumption from the battery is reduced by using the energy stored in the SC, and the SC energy is discharged, making preparations for the energy that will come from the next regenerative braking. Thus, the high current values transferred to the battery during regenerative braking are effectively limited by the SC. In this study, the current values on the battery in the EV with a hybrid storage system decreased by 29.1% in the ADVISOR program and 28.7% in the Simcenter Flomaster program. In addition, the battery generated 46.84% less heat in the hybrid storage system. Thus, the heating and capacity losses caused by this current on the battery were minimized. The presented method provides more efficient energy management for EVs and plays an important role in maintaining battery health.

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Section: Resultsmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Şen,

Özcan,

Eker

2024

Applied Sciences

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“…To cater to diverse EV tractive requirements, an effective power electronics-controlled SR motor is employed in EVs as a substitute for multi-speed transmissions, opting for gearless or single-speed gear transmission [31]. In the context of this study, the transmission model is structured around a single-speed transmission with a gear ratio of 7.8:1.…”

Section: Transmission Modelmentioning

confidence: 99%

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Saiteja,

Ashok,

Upadhyay

2024

Vehicles

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The performance of an electric vehicle (EV) notably depends on an energy management controller. This study developed several energy management controllers (EMCs) to optimize the efficiency of EVs in real-time driving conditions. Also, this study employed an innovative methodology to create EMCs, efficiency maps, and real-time driving cycles under actual driving conditions. The various EMCs such as PID, intelligent, hybrid, and supervisory controllers are designed using MATLAB/Simulink and examined under real-time conditions. In this instance, a mathematical model of an EV with a switched reluctance (SR) motor is developed to optimize energy consumption using different energy management controllers. Further, an inventive experimental approach is employed to generate efficiency maps for the SR motor and above-mentioned controllers. Then, the generated efficiency maps are integrated into a model-in-loop (MIL)-based EV test platform to analyze the performance under real-time conditions. Additionally, to verify EV model, a real-time driving cycle (DC) has been developed, encompassing various road conditions such as highway, urban, and rural. Subsequently, the developed models are included into an MIL-based EV test platform to optimize the performance of the electric motor and battery consumption in real-time conditions. The results indicate that the proposed supervisory controller (59.1%) has a lower EOT SOC drop compared to the PID (3.6%), intelligent (21.5%), and hybrid (44.9%) controllers. Also, the suggested controller achieves minimal energy consumption (44.67 Wh/km) and enhances energy recovery (−58.28 Wh) under different real-time conditions. Therefore, it will enhance the driving range and battery discharge characteristics of EVs across various real-time driving conditions.

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“…They also investigated the influence of different energy storage systems' operating behaviors on their performance. This approach includes the work of Da silva et al [17], who proposed an interactive adaptive GA combined with FLC for multiobjective optimization and power control of a hybrid UC-battery system. This method improved the driving range and extended the battery lifespan by 22.88%.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Omakor,

Alzayed,

Chaoui

2024

Energies

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A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 ∘C, which is the highest operating temperature attained under the proposed strategy.

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Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle

Cited by 31 publications

References 118 publications

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Evaluation of Electric Vehicle Performance Characteristics for Adaptive Supervisory Self-Learning-Based SR Motor Energy Management Controller under Real-Time Driving Conditions

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

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