A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs

Peng, Jun; Wang, Rui; Liao, Hongtao; Zhou, Yanhui; Li, Heng; Wu, Yue; Huang, Zhiwu

doi:10.3390/en12030440

Cited by 22 publications

(7 citation statements)

References 27 publications

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“…The gearbox and wheels of the car are represented as a mono-physical conversion element, which contains the following model equations: (12) where k gear and k wheel are the coefficients of the gearbox and wheels, respectively; η g = 95% is the efficiency of the gearbox.…”

Section: Gearbox and Wheels' Modelingmentioning

confidence: 99%

“…Firstly, the rule-based strategies can be classified into deterministic and artificial intelligence (AI)-based methods. While the deterministic ones are formed by the user's experience with the system with explicit rules such as frequencybased methods [12], filtering algorithms [13], or feedback control [14,15], the others use AI techniques to achieve the human-like learning abilities of the system control, such as fuzzy logic [16,17], neural-network-based methods [18,19], and machine learning solutions [20]. The AI techniques after deep training in these studies gave promising results for the control and management of HESSs.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Gearbox and Wheels' Modelingmentioning

confidence: 99%

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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“…The DC/DC converter model DC/DC converter can convert the working voltage of direct current (DC) power supply and stabilize the voltage. 20 In this paper, the bidirectional DC/DC converter is used. In the HESS, the converter is connected in series with the ultracapacitor, which can adjust the output of the power supply and stabilize the bus voltage.…”

Section: The Ultracapacitor Modelmentioning

confidence: 99%

“…Combined with formula (19)(20)(21), a multi-objective optimization model is obtained, which takes the working current of battery and the energy consumption of the HESS as the optimization objective. It can be written as…”

Section: Control Strategy and Optimization Of Hessmentioning

confidence: 99%

A new adaptive PSO-PID control strategy of hybrid energy storage system for electric vehicles

2020

Advances in Mechanical Engineering

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Research on optimization of control strategy for hybrid energy storage system (HESS) of the electric vehicle (EV), a new adaptive control strategy based on particle swarm optimization (PSO) algorithm is proposed in this paper. The steady-state power of the filtered power is used as the ideal output power of the battery. For the steady-state current output of the battery, the output power of the ultracapacitor is dynamically adjusted by the proportional-integral-derivative (PID) controller to construct a power difference control structure. The parameters of PID controller are optimized by PSO algorithm, and the target test is compared and analyzed based on MATLAB/Advisor. The research results show that the proposed PSO-PID control strategy can quickly eliminate the power deviation and achieve the approximate global optimization of the EV energy management strategy. Compared with the pre-optimized PID control strategy, the output current and power of the battery pack are smoother and the total power consumption is reduced by 3.8360% and 0.5125%, respectively. Then, the energy consumption parameters of PSO-PID are compared with the theoretical minimum energy consumption calculated by dynamic programming (DP) algorithm, and the deviation is less than 1% under both conditions.

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“…In this manner, both the SC and battery's characteristics are exploited effectively. Obviously, the rule-based method developed by the experience or heuristics of experts is easy to be realized without considering prior knowledge of the road conditions [17].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information

Zhi³

et al. 2021

Energies

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To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.

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A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs

Cited by 22 publications

References 27 publications

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

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

A new adaptive PSO-PID control strategy of hybrid energy storage system for electric vehicles

Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information

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