一种改进的电动汽车锂电池RC滞后模型及其应用
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摘要
为提高电动汽车电池SOC估计精度、收敛速度和鲁棒性,提出了一种改进的锂电池RC滞后模型及自适应粒子滤波的SOC估计方法。在传统RC模型基础上加入滞后模块,使用粒子群算法搜索的方法求解模型参数,综合考虑计算量和模型精度,确定了一阶RC滞后模型作为锂电池等效模型。在传统粒子滤波基础上,提出了观测噪声方差自适应估计方法。仿真结果表明,SOC初值误差较大时,自适应粒子滤波收敛速度和鲁棒性、SOC估计精度和稳定性明显优于传统算法。
To improve SOC estimation accuracy, rate of convergence and robustness of battery on electric vehicle, an improved Li-battery RC lagging model and adaptive particle filter SOC estimation method are proposed. Lagging model is introduced to traditional RC model, and Particle Swarm algorithm is used to search optimal model parameters. Considering calculation and model accuracy, first-order lagging model is chosen as the equivalent model of Li-battery. On the basis of traditional particle filter, observation noise variance adaptive adjustment method is proposed. Simulation results show that,rate of convergence and robustness of adaptive particle filter, SOC estimation accuracy and stability are superior to traditional algorithm when SOC original error is big.
To improve SOC estimation accuracy, rate of convergence and robustness of battery on electric vehicle, an improved Li-battery RC lagging model and adaptive particle filter SOC estimation method are proposed. Lagging model is introduced to traditional RC model, and Particle Swarm algorithm is used to search optimal model parameters. Considering calculation and model accuracy, first-order lagging model is chosen as the equivalent model of Li-battery. On the basis of traditional particle filter, observation noise variance adaptive adjustment method is proposed. Simulation results show that,rate of convergence and robustness of adaptive particle filter, SOC estimation accuracy and stability are superior to traditional algorithm when SOC original error is big.
引文
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[13]Ramadan H S,Bendary A F,Nagy S.Particle Swarm Optimization Algorithm for Capacitor Allocation Problem in Distribution Systems with Wind Turbine Generators[J].International Journal of Electrical Power&Energy Systems,2017,84:143-152.
[14]Jwo D J,Chung F C,Yu K L.GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters[J].Journal of Applied Research&Technology,2013,11(4):496-509.
[15]李天成,范红旗,孙树栋.粒子滤波理论、方法及其在多目标跟踪中的应用[J].自动化学报,2015,41(12):1981-2002.
[2]付浪,杜明星,刘斌,等.基于开路电压法与卡尔曼滤波法相结合的锂离子电池SOC估算[J].天津理工大学学报2015,31(6):9-13.
[3]张健.基于DUFK的动力电池内阻与SoC估算[J].通信电源技术,2017,34(1):104-106.
[4]鲍慧,于洋.基于安时积分法的电池SOC估算误差校正[J].计算机仿真,2013,30(11):148-151.
[5]胡春花,何仁,王润才,等.基于PID神经网络的车用锂电池SOC估算[J].汽车技术,2012(10):36-38.
[6]胡丽平.基于支持向量机的动力锂离子电池SOC估算算法研究[D].武汉:湖北工业大学,2014.
[7]张頔,马彦,柏庆文.基于自适应卡尔曼滤波的锂离子电池SOC估计[J].汽车技术,2011(8):42-45.
[8]李涛,梅成林,刘波峰,等.基于粒子群的模糊神经网络铅酸蓄电池SOC估计[J].电源技术,2017,41(1):64-67.
[9]Li T,Sun S,Sattar T.Adapting Sample Size in Particlefilters Through KLD-Resampling[J].Electronics Letters,2013,49(12):740-742.
[10]Zuo J,Jia Y,Zhang Y,Lian W.Adaptive Iterated Particle Filter[J].Electronics Letters,2013,49(12):742-744.
[11]Straka O,Simandl M.Particle Filter with Adaptive Sample Size[J].Kybernetika,2011,47(3):385-400.
[12]Plett G L.Extended Kalman Filtering for Battery Management Systems of LiPB-based HEV Battery Packs:Part 3.State and Parameter Estimation[J].Journal of Power Sources,2004,134(2):277-292.
[13]Ramadan H S,Bendary A F,Nagy S.Particle Swarm Optimization Algorithm for Capacitor Allocation Problem in Distribution Systems with Wind Turbine Generators[J].International Journal of Electrical Power&Energy Systems,2017,84:143-152.
[14]Jwo D J,Chung F C,Yu K L.GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters[J].Journal of Applied Research&Technology,2013,11(4):496-509.
[15]李天成,范红旗,孙树栋.粒子滤波理论、方法及其在多目标跟踪中的应用[J].自动化学报,2015,41(12):1981-2002.