Optimization of Energy Management Strategy and Sizing in Hybrid Storage System for Tram

Wang, Yu; Yang, Zhongping; Li, Feng

doi:10.3390/en11040752

Cited by 16 publications

(12 citation statements)

References 20 publications

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“…In developing optimized LIB-SC HESS for EV applications, researchers propose several algorithms in the T A B L E 6 Major components required for optimization algorithm framework [110][111][112][113]115,116…”

Section: Different Suitable Control Algorithms For Lib-sc Hessmentioning

confidence: 99%

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Ahsan

Mekhilef

Soon

et al. 2022

Intl J of Energy Research

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Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium-ion battery (LIB) and a supercapacitor (SC)-based HESS (LIB-SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal-sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system-level optimizations, include component sizing (CS) and power-energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB-SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB-SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB-SC HESS are provided to inspire researchers for further studies. Highlights• Lithium-ion battery (LIB) and supercapacitor (SC)-based hybrid energy storage system (LIB-SC HESS) suitable for EV applications is analyzed comprehensively.

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Section: Different Suitable Control Algorithms For Lib-sc Hessmentioning

confidence: 99%

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Ahsan

Mekhilef

Soon

et al. 2022

Intl J of Energy Research

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show abstract

“…Multi-objective optimization of lifespan, cost, and size was carried out [8][9][10]. Rather than ignoring the interaction between the parameter and the control strategy as in the above literature, literature [11] put forward a collaboration optimization method to make the HESS operate better. In the existing research of parameter matching, the optimal targets mainly focus on the operating or manufacturing cost of HESS.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the TCO of HESS is the crucial economic index for HESS, and in the present research on TCO, the replacement cost of the battery, the electricity fee, and the DC/DC converter cost are not included at the same time, which are important indexes of HESS cost. Also, the impact of control strategy on parameters should be considered [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles

Jiang

Jia

et al. 2018

Energies

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In order to complete the reasonable parameter matching of the pure electric vehicle (PEV) with a hybrid energy storage system (HESS) consisting of a battery pack and an ultra-capacitor pack, the impact of the selection of the economic index and the control strategy on the parameters matching cannot be ignored. This paper applies a more comprehensive total cost of ownership (TCO) of HESS as the optimal target and proposes an optimal methodology integrating parameters and control strategy for the PEV with HESS. Through the integrated optimal methodology, the application value of HESS is analyzed under various types of driving cycles and the results indicate that the HESS can significantly improve the economic performance of PEVs under both urban and suburban driving cycles. Due to the poor adaptability of traditional control strategies to different driving cycles, a novel extreme learning machine (ELM) based controller is established. Firstly, a dynamic programming (DP) based controller is applied for the offline optimization of the HESS power allocation under several typical driving cycles. Then, an analytical method combining correlation analysis and mean impact value (MIV) is employed to deal with offline sample data from DP and obtain the characteristic variables of the ELM model. Ultimately, the instantaneous power allocation strategy of HESS is acquired by utilizing ELM to learn offline data of HESS. Comparative simulations between the ELM-based controller and the rule-based controller are conducted, and the simulation results show that compared to the rule-based controller (RBC), the ELM-based controller reduces the electricity consumption by 3.78% and battery life loss by 6.51%.

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“…Although a generic ESS [36][37][38] may include several elements, such as batteries, supercapacitors [39,40], fuel cells [41], etc., in this paper, only an ESS based on batteries is considered due to its specific energy, energy density and discharge rate.…”

Section: Energy Storage Systemmentioning

confidence: 99%

Advanced Methodology for the Optimal Sizing of the Energy Storage System in a Hybrid Electric Refuse Collector Vehicle Using Real Routes

Cortez¹,

Moreno-Eguilaz²,

Soriano³

2018

Energies

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This paper presents a new methodology for optimal sizing of the energy storage system ( E S S ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( R C V ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( L i – P o ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.

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Optimization of Energy Management Strategy and Sizing in Hybrid Storage System for Tram

Cited by 16 publications

References 20 publications

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Lithium‐ionbattery and supercapacitor‐based hybrid energy storage system for electric vehicle applications: A review

Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles

Advanced Methodology for the Optimal Sizing of the Energy Storage System in a Hybrid Electric Refuse Collector Vehicle Using Real Routes

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