Browsing by Author "Cheng, Shuang"

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    Ageing mitigation for battery energy storage system in electric vehicles
    (IEEE, 2022-09-27) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Li, Jianwei; Huo, Da; Cheng, Shuang
    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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    Linearizing battery degradation for health-aware vehicle energy management
    (IEEE, 2023-09) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Li, Jianwei; Cheng, Shuang
    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 energy-transportation 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-the-parameter 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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    Online battery-protective vehicle to grid behavior management
    (Elsevier, 2022-01-03) Li, Shuangqi; Zhao, Pengfei; Gu, Chenghong; Huo, Da; Zeng, Xianwu; Pei, Xiaoze; Cheng, Shuang; Li, Jianwei
    With the popularization of electric vehicles, vehicle-to-grid (V2G) has become an indispensable technology to improve grid economy and reliability. However, battery aging should be mitigated while providing V2G services so as to protect customer benefits and mobilize their positivity. Conventional battery anti-aging V2G scheduling methods mainly offline operates and can hardly be deployed online in hardware equipment. This paper proposes a novel online battery anti-aging V2G scheduling method based on a novel two-stage parameter calibration framework. In the first stage, the V2G scheduling is modeled as an optimization problem, where the objective is to reduce grid peak-valley difference and mitigate battery aging. The online deployment of the developed optimization-based V2G scheduling is realized by a rule-based V2G coordinator in the second stage, and a novel parameter calibration method is developed to adjust controller hyper-parameters. With the parameter calibration process, the global optimality and real-time performance of V2G strategies can be simultaneously realized. The effectiveness of the proposed methodologies is verified on a practical UK distribution network. Simulation results indicate that it can effectively mitigate battery aging in providing V2G services while guaranteeing algorithm real-time performance.