SVM和Kalman濾波大功率動力電池SOC預估方法的研究
摘 要：新能源汽車大功率動力電池荷電狀態(tài)（state-of-charge，SOC）的快速精確估計是動力電池能耗管理系統的核心技術，針對大功率動力電池這一非線性、強耦合系統，提出基于支持向量機（support-vector-machine，SVM）靜態(tài)預測和基于卡爾曼濾波（Kalman）動態(tài)預測的動力電池SOC預估方法。仿真實驗結果表明，采用基于SVM和Kalman濾波結合的預估方法可以快算完成動力電池SOC的估計，并且動力電池模型參數的變動幾乎不影響算法的準確性，表明算法具有一定的魯棒性。
關鍵詞：荷電狀態(tài)；卡爾曼濾波；支持向量機；預測模型
中圖分類號：U473.4；U461.2；TP391.9；TP301.6 文獻標志碼：A 文章編號：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
