Adaptive energy management for hybrid power system considering fuel economy and battery longevity

Li, Shuangqi; Gu, Chenghong; Zhao, Pengfei; Cheng, Shuang

doi:10.1016/j.enconman.2021.114004

Cited by 51 publications

(16 citation statements)

References 49 publications

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“…A plug-in hybrid electric bus [54] weights 18000 kg with a 147 kW fuel engine, a 65 kW ISG motor, a 168 kW main driving motor, and a 34.5 kWh battery pack is studied in this part to verify the performance of the developed battery antiaging energy storage system management method. The Chinese Typical Urban Drive Cycles (CTUDC) is used in this study to simulate vehicle daily operation, the velocity and acceleration profiles are shown in Fig.…”

Section: A Hybrid Electric Vehicle Energy Managementmentioning

confidence: 99%

Aging Mitigation for Battery Energy Storage System in Electric Vehicles

Zhao

et al. 2023

IEEE Trans. Smart Grid

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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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Section: A Hybrid Electric Vehicle Energy Managementmentioning

confidence: 99%

Aging Mitigation for Battery Energy Storage System in Electric Vehicles

Zhao

et al. 2023

IEEE Trans. Smart Grid

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“…Operation of BESS, ISG, and driving motor are scheduled based on vehicle power requirements by energy management strategies. The detailed parameters of the studied hybrid electric vehicle are provided in [35]. The Chinese typical urban drive cycle (CTUDC) with a driving range of 5.897 km is used in this study to verify the developed PHEV power distribution model.…”

Section: B Hybrid Electric Vehicle Energy Managementmentioning

confidence: 99%

Linearizing Battery Degradation for Health-Aware Vehicle Energy Management

Zhao

et al. 2023

IEEE Trans. Power Syst.

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The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energytransportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-theparameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.

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“…Where: ed P and ec P are the rated discharging and charging power of the battery. Based on the derived V2G power state, the battery SOC of i EV can be calculated by the following equation [43]:…”

Section: Centralized V2g State Coordinatormentioning

confidence: 99%