Incremental Degradation Estimation Method for Online Assessment of Battery Operation Cost

Sandelić, Monika; Sangwongwanich, Ariya; Blaabjerg, Frede

doi:10.1109/tpel.2022.3172499

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…The LIB cycles are categorized into two groups: the complete and incomplete cycles. A nonlinear method based on the RFC algorithm is presented in [32] to count the number of the complete and incomplete cycles of the LIB. As using nonlinear constraints in the optimization problem increases the computation time, a novel linear algorithm is proposed in this paper to calculate the LIB cycle aging according to the DOD at each complete/incomplete cycle.…”

Section: ) Battery Cycle Agingmentioning

confidence: 99%

“…Constraint (26) demonstrates the amount of power purchased from the realtime market. Other LIB operation constraints are modeled by ( 27)- (32).…”

Section: A Objective Function and Constraintsmentioning

confidence: 99%

“…The third term of equation ( 31) demonstrates the amount of the LIB capacity fading due to the calendar aging process and is calculated by equation ( 16). Finally, the LIB state of health is determined by equation (32).…”

Section: A Objective Function and Constraintsmentioning

confidence: 99%

“…In the next step, the LIB energy management problem is solved over the prediction horizon. The associated energy management problem is modeled by equations ( 1)-( 11), (16), and ( 21)- (32). Afterward, the new value of θ bat is calculated by equations ( 17)-( 20).…”

Section: B Predictive Energy Management Strategymentioning

confidence: 99%

“…On the other hand, after the discharge process completes in the incomplete cycles of the LIB, the SOC will be less than 100% during the charging process [28]. In [31], [32], [33], and [34], the rain-flow counting (RFC) algorithm is employed to count the complete and incomplete cycles of the LIB. As the RFC algorithm is nonlinearly modeled, it increases the computation time of the optimization problem and may get stuck in a locally optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

2023

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This paper presents a novel battery degradation cost (BDC) model for lithium-ion batteries (LIBs) based on accurately estimating the battery lifetime. For this purpose, a linear cycle counting algorithm is devised to estimate the battery cycle aging. In this algorithm, the local maximum and minimum values of the profile of the battery state of charge are identified by the proposed linear formulations. Then, the battery cycle aging due to the complete and incomplete cycles is determined. In this step, the battery cycle aging during an incomplete cycle is calculated by converting it to two complete cycles. After that, the calendar aging process of the LIB is linearly formulated based on the semi-empirical model to estimate the BDC accurately. After linearizing the LIB degradation process, a mechanism for computing the BDC during the scheduling horizon is designed by modeling the BDC as a series of equal payments over the LIB lifetime. In order to incorporate the BDC in the battery energy management problem, an iterative algorithm is presented for efficiently calculating the BDC associated with the adopted charging/discharging strategy. The numerical simulation results indicate that integrating the battery degradation process into the battery scheduling problem can reduce the amount of the battery capacity fading by 32.81%, as well as increase the profit of battery owners by 1.21%. Moreover, the conducted analyses highlight the importance of considering the LIB calendar aging process in determining the optimal LIB capacity.INDEX TERMS Battery degradation cost (BDC), cycle aging process, calendar aging process, battery scheduling problem, wind energy.

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Section: ) Battery Cycle Agingmentioning

confidence: 99%

“…Constraint (26) demonstrates the amount of power purchased from the realtime market. Other LIB operation constraints are modeled by ( 27)- (32).…”

Section: A Objective Function and Constraintsmentioning

confidence: 99%

Section: A Objective Function and Constraintsmentioning

confidence: 99%

Section: B Predictive Energy Management Strategymentioning

confidence: 99%