摘要
基于一阶RC等效电路模型、热网络法以及老化模型建立了锂离子电池的电-热-老化耦合的多状态联合估计模型,采用粒子群优化算法建立阶数自适应的多段恒流充电策略.通过构建充电时间-电池寿命的帕累托边界曲线,得到了电池最短充电时间(β=1)、最小老化(β=0)及平衡(β=0. 02)充电三种充电策略,并与CC-CV充电策略进行了对比.结果表明,最短时间充电与2C CC-CV具有很高的一致性.最小老化充电与0. 1C CC-CV充电的老化损失都很小,但前者缩短了61. 7%的充电时间.平衡充电策略相比于最小老化策略,仅牺牲0. 06%SOH,缩短71. 19%的充电时间.相比于0. 5C CC-CV充电策略,平衡充电策略的充电时间减少了44. 9%.
An electro-thermal-aging coupling multi-state joint-estimation model of lithium-ion batteries was built based on the first-order RC equivalent circuit model, the thermal network method and the aging model.Particle swarm optimization(PSO) algorithm was adopted to establish a self-adaptive multistage constant current(SMCC)charging strategy whose stages were adaptive to the objective function.By constructing the Pareto frontier of charging time and battery life, three charging strategies, including the minimum-time charge, the minimum-aging charge and the balanced charge, were obtained.Then they were compared with the CC-CV charges. The results show that the minimum-time charge is highly consistent with the 2 C CC-CV.The aging losses of the minimum-aging charge and the 0.1 C CC-CV are very small, but the former reduces charging time by 61.7 %. Compared with the minimum-aging charge, the charging time of the balanced charge is reduced by 71.19 % at the expense of 0.06 % SOH.Compared with the 0.5 C CC-CV charge, the charging time of the balanced charge is reduced by 44.9 %.
作者
李夔宁
张宏济
谢翌
傅春耘
LI Kui-ning;ZHANG Hong-ji;XIE Yi;FU Chun-yun(Key Laboratory of Low-Grade Energy Utilization Technologies and Systems,Ministry of Education,Chongqing University,Chongqing 400044,China;School of Energy and Power Engineering,Chongqing University,Chongqing 400044,China;School of Automotive Engineering,Chongqing University,Chongqing 400044,China)
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2019年第9期1323-1329,共7页
Journal of Northeastern University(Natural Science)
基金
国家重点研发计划项目(2018YFB0106102,2018YFB0106104)
重庆市重点产业共性关键技术创新专项(No.cstc2017zdcy-zdzx0021,No.cstc2017zdcy-zdyf0139)
关键词
锂离子电池
电-热-老化耦合模型
自适应多段恒流
粒子群优化算法
帕累托边界
lithium-ion batteries
electro-thermal-aging coupling model
SMCC(self-adaptive multistage constantcurrent)
particle swarm optimization(PSO) algorithm
Pareto frontier