On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system
On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system
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DOI：
10.1016/j.energy.2017.04.116
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发表时间：
2017-08
期刊：
影响因子：
9
通讯作者：
K. Uddin;T. Jackson;W. D. Widanage;G. Chouchelamane;P. Jennings;J. Marco
中科院分区：
文献类型：
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作者：
K. Uddin;T. Jackson;W. D. Widanage;G. Chouchelamane;P. Jennings;J. Marco
可再生能源是电力行业脱碳的关键支柱。然而，由于它们增加的变异性和不确定性，对能源储存的需求增加了。这增加了额外的基础设施成本，达到了不可行的程度：在2030年前存储达到15 GW的最佳情况下，存储成本约为：GB 1000/kW。解决这一问题的一个有希望的解决方案是使用配备双向充电系统的电动汽车(EVS)中包含的电池，通过车辆到电网(V2G)技术来促进辅助服务，如频率调节和负载平衡。然而，一些作者对V2G不屑一顾，认为它在经济上是不可行的，声称电池退化的成本比套利更大。为了彻底解决V2G技术的可行性，在这项工作中，我们基于50多个商业C6/LiNiCoAlO2电池的长期老化实验收集的长期老化数据，开发了一个全面的电池退化模型。综合模型考虑了所有已建立的退化模式，包括日历年龄、容量吞吐量、温度、充电状态、放电深度和电流速率。该模型使用六个不同的实际使用周期进行了验证，在一年多的循环中，容量损失估计的平均最大瞬时误差为4.6%，阻力上升估计的平均最大瞬时误差为5.1%。这一经过验证的全面电池老化模型已被集成到智能电网算法中，该算法旨在最大限度地减少电池退化。我们表明，连接到该智能电网系统的电动汽车可以满足电网对清洁可再生能源份额增加的需求，但更深刻的是，智能电网能够将电动汽车电池的寿命延长到不存在V2G的情况下。大量的模拟结果表明，如果每天的驾驶周期消耗21%到38%的荷电状态，那么将40%到8%的电池荷电状态释放到电网可以在三个月的时间内减少大约6%的容量衰减和3%的功率衰减。智能电网优化被用来调查一个具有代表性的大学办公楼的电力需求的案例研究。结果表明，智能电网方案能够将电动汽车的电池组容量衰减高达9.1%，功率衰减高达12.1%。
Renewable energies are a key pillar of power sector decarbonisation. Due to the variability and uncertainty they add however, there is an increased need for energy storage. This adds additional infrastructure costs to a degree that is unviable: for an optimal case of 15 GW of storage by 2030, the cost of storage is circa: £1000/kW. A promising solution to this problem is to use the batteries contained within electric vehicles (EVs) equipped with bi-directional charging systems to facilitate ancillary services such as frequency regulation and load balancing through vehicle to grid (V2G) technologies. Some authors have however dismissed V2G as economically unviable claiming the cost of battery degradation is larger than arbitrage. To thoroughly address the viability of V2G technologies, in this work we develop a comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial C6/LiNiCoAlO2batteries. The comprehensive model accounts for all established modes of degradation including calendar age, capacity throughput, temperature, state of charge, depth of discharge and current rate. The model is validated using six operationally diverse real-world usage cycles and shows an average maximum transient error of 4.6% in capacity loss estimates and 5.1% in resistance rise estimates for over a year of cycling. This validated, comprehensive battery ageing model has been integrated into a smart grid algorithm that is designed to minimise battery degradation. We show that an EV connected to this smart-grid system can accommodate the demand of the power network with an increased share of clean renewable energy, but more profoundly that the smart grid is able to extend the life of the EV battery beyond the case in which there is no V2G. Extensive simulation results indicate that if a daily drive cycle consumes between 21% and 38% state of charge, then discharging 40%–8% of the batteries state of charge to the grid can reduce capacity fade by approximately 6% and power fade by 3% over a three month period. The smart-grid optimisation was used to investigate a case study of the electricity demand for a representative University office building. Results suggest that the smart-grid formulation is able to reduce the EVs' battery pack capacity fade by up to 9.1% and power fade by up to 12.1%.