摘  要：新能源汽車大功率動(dòng)力電池荷電狀態(tài)（state-of-charge，SOC）的快速精確估計(jì)是動(dòng)力電池能耗管理系統(tǒng)的核心技術(shù)，針對大功率動(dòng)力電池這一非線性、強(qiáng)耦合系統(tǒng)，提出基于支持向量機(jī)（support-vector-machine，SVM）靜態(tài)預(yù)測和基于卡爾曼濾波（Kalman）動(dòng)態(tài)預(yù)測的動(dòng)力電池SOC預(yù)估方法。仿真實(shí)驗(yàn)結(jié)果表明，采用基于SVM和Kalman濾波結(jié)合的預(yù)估方法可以快算完成動(dòng)力電池SOC的估計(jì)，并且動(dòng)力電池模型參數(shù)的變動(dòng)幾乎不影響算法的準(zhǔn)確性，表明算法具有一定的魯棒性。
關(guān)鍵詞：荷電狀態(tài)；卡爾曼濾波；支持向量機(jī)；預(yù)測模型
中圖分類號(hào)：U473.4；U461.2；TP391.9；TP301.6       文獻(xiàn)標(biāo)志碼：A      文章編號(hào)：1674-5124（2013）05-0092-04
Estimation method research of high volume battery based on SVM and Kalman filter
LI Zheng-guo1， MENG Fan-kun2
（1. Shenzhen Polytechnic，Shenzhen 518055，China；
 2. College of Electrical Engineering，South China University，Hengyang 421001，China）
Abstract: Fast and accurate state-of-charge（SOC） estimation about the high volume battery of new energy vehicles is a key technique for energy efficiency management. For the non-linear and inherent dynamic property of a high volume battery， this paper put forward an algorithm for battery SOC static estimation based on support-vector-machine（SVM） and dynamic estimation based on Kalman filter. The simulation result shows that the algorithm can estimate SOC quickly and accurately， and the disturbance of battery model parameters does not influence the accuracy of this method， which shows the robustness of this method.
Key words: state-of-charge； kalman filter； support-vector-machine； predication model