A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

Hussain, Sadam; Ali, Muhammad Umair; Park, Gwan Soo; Nengroo, Sarvar Hussain; Khan, Muhammad Adil; Kim, Sang-Yong

doi:10.3390/en12244662

Cited by 46 publications

(28 citation statements)

References 59 publications

(76 reference statements)

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“…However, the control strategy lacks the behavior of the system on degradation of EV batteries due to the effects of varying driver behaviors. Hussain et al 115 proposed two adaptive controllers for energy management of battery SC powered EV. Adaptive FLC performs the optimal power sharing among the sources considering the SoC of SC.…”

Section: Fuzzy Logic-based Energy Management Strategymentioning

confidence: 99%

A review on hybrid source energy management strategies for electric vehicle

Kashyap

Castelino

2021

Int J Energy Res

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Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro-electronic interfaces, and energy management strategies (EMS). Significant issues of battery-powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated-based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC-DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. K E Y W O R D Sbattery, electric vehicle (EV), energy management strategies (EMSs), hybrid power source systems (HPSS), supercapacitor (SC)

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Section: Fuzzy Logic-based Energy Management Strategymentioning

confidence: 99%

A review on hybrid source energy management strategies for electric vehicle

Kashyap

Castelino

2021

Int J Energy Res

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“…The ratio of the EV stored energy level and its battery capacity is defined as the state of charge (SoC), which the initial battery level of the i-th EV (SoC i,int ) at the beginning of charging which can be calculated using Equation (21). The high accuracy model of EV battery's SoC level estimation can be found in [23]. However, this model must be evaluated by using many parameters of the battery.…”

Section: Electric Vehicle (Ev) Charging Constraintsmentioning

confidence: 99%

“…Therefore, to reduce the complexity of this constraint, the SoC of EV battery energy formula as shown in Equation 22is used in this work. This equation can be compared with the equation in [23] and is successfully used in reference [15].…”

Section: Electric Vehicle (Ev) Charging Constraintsmentioning

confidence: 99%

Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost

et al. 2020

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Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.

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“…Ragone's plots can be used to find a suitable time constant for both energy storage systems [31,32]. Several adaptive filter-based strategies such as fuzzy logic approaches [33,34], filter folding frequency [35], or the frequency-separation method by polynomial control design [36] have been studied to adapt to system states changing as a function of the driving conditions.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles

et al. 2021

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Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.

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A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

Cited by 46 publications

References 59 publications

A review on hybrid source energy management strategies for electric vehicle

A review on hybrid source energy management strategies for electric vehicle

Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost

A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles

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