Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 14, Issue 3, Pages 2152-2163Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2022.3210041
Keywords
Batteries; Aging; Energy management; Vehicle-to-grid; Costs; Mathematical models; US Department of Defense; Battery energy storage system; electric vehicle; battery aging assessment; battery aging mitigation; energy management; vehicle power distribution; vehicle to~grid
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This paper proposes an integrated battery life loss modeling and anti-aging energy management method for improving the total economy of battery energy storage systems in electric vehicles. A multifactorial battery life loss quantification model is established by capturing aging characteristics from cell acceleration aging tests. Two active anti-aging vehicle energy management models are designed, where the battery life loss quantification model is used to generate the aging cost feedback signals.
Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests. Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.
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