主动抑制电池老化V2G最优调频策略开发

doi:10.11985/2022.04.014

摘要/Abstract

摘要：

可再生能源对电网渗透规模的不断扩大给电力系统的频率稳定带来了巨大挑战。电动汽车作为移动电源参与V2G(Vehicle-to-grid)调频可有效解决这一难题。但是执行V2G调频会加剧电池老化，这在很大程度上打击了电动汽车车主参与V2G服务的积极性。因此，为抑制V2G调频对电动汽车电池老化的消极影响，通过引入基于机理的电池老化模型，建立了以抑制电池老化为目标的优化模型，进而基于模型预测控制理论，开发了一种全新的优化控制器，实现了对电动汽车充放电功率实时的高效控制。研究了不同前瞻时间长度对优化控制器性能的影响，并在此基础上讨论了所开发控制器对不同健康状态条件下电池的老化影响程度。仿真结果表明，电池性能的衰退会加剧V2G调频过程中电池的老化。另外，在保证对调频功率信号有良好跟踪效果的同时，与基准优化控制策略相比，所提出的V2G调频策略，可将电池老化降低高达22.34%。

关键词: 电动汽车, V2G, 模型预测控制, 调频, 电池老化

Abstract:

The continuous expansion of the penetration scale of renewable energy into the power grid has brought great challenges to the frequency stability of the power system. The participation of electric vehicles in V2G(vehicle-to-grid) frequency regulation as a mobile power source can effectively solve this problem. However, the implementation of V2G frequency regulation will aggravate battery aging, which greatly discourages EVs(electric vehicles) owners from participating in V2G services. Therefore, in order to suppress the negative impact of V2G frequency regulation on the battery aging of EVs, an optimization model aiming at suppressing battery aging by introducing a mechanism-based battery aging model is established, and then a new model is developed based on the model predictive control theory. The optimized controller realizes the real-time and efficient control of charging and discharging power of EVs. The effect of different look-ahead time lengths on the performance of the optimized controller is studied, and on this basis, the degree of influence of the developed controller on the aging of the battery under different state of health conditions is discussed. The simulation results show that deterioration of battery performance will exacerbate battery degradation during V2G frequency regulation. In addition, while ensuring a good tracking effect on the frequency regulation power signal, compared with the reference optimization control strategy, the proposed V2G frequency regulation strategy can reduce battery aging by up to 22.34%.

Key words: Electric vehicles, V2G, model predictive control, frequency regulation, battery degradation

中图分类号:

TG156

罗国庆, 张永志, 贾元威. 主动抑制电池老化V2G最优调频策略开发[J]. 电气工程学报, 2022, 17(4): 133-144.

LUO Guoqing, ZHANG Yongzhi, JIA Yuanwei. Development of V2G Optimal Frequency Regulation Strategy for Actively Suppressing Battery Aging[J]. Journal of Electrical Engineering, 2022, 17(4): 133-144.

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车型	初始SOC(%)	额定容量/(A·h)	额定电压/V	SOC变化范围(%)	充放电功率范围/kW
NIO 1	27.9	214.29	350	20~90	-20~20
NIO 2	34.2	214.29	350
NIO 3	43.6	214.29	350
BYD 1	53.3	135	569.6
BYD 2	54.7	135	569.6
BYD 3	63.2	135	569.6
BYD 4	68.5	135	569.6
Tesla 1	76.3	211.4	402
Tesla 2	83.8	211.4	402
Tesla 3	86.0	211.4	402

车型	截止SOC(%)			SOC变化范围(%)
车型	基准	M=20	M=30	基准	M=20	M=30
NIO 1	27.66	27.54	27.41	0.24	0.36	0.49
NIO 2	33.98	33.93	33.99	0.22	0.27	0.21
NIO 3	43.39	43.20	43.37	0.21	0..40	0.23
BYD 1	53.10	52.81	52.89	0.20	0.49	0.41
BYD 2	54.51	54.19	54.00	0.19	0.51	0.70
BYD 3	63.01	62.96	62.66	0.19	0.24	0.54
BYD 4	68.31	68.52	68.38	0.19	-0.02	0.12
Tesla 1	76.14	76.34	76.68	0.16	-0.07	-0.38
Tesla 2	83.64	83.83	84.12	0.16	-0.03	-0.32
Tesla 3	85.64	86.03	86.04	0.16	-0.03	-0.04