【正文】 of battery management, the state of charge (SOC) estimation is one of the most difficult problems due to the nonlinear effects and realtime requirement. On the other hand, the remaining useful life (RUL) is another critical performance indicator of battery management, which guarantees the timely replacement of batteries. However, the research on RUL prediction has not been started until recent years, therefore there is little existing work can be found. Based on the above considerations, this thesis conducts prehensive study on the SOC and RUL problems and battery monitoring system design, major contributions are summarized as follows: 1. Lithiumion batteries SOC estimation. Considering the external and internal nonlinear factors as well as the observation that the voltage remains mostly constant as SOC changes during the charge/discharge process of LiFePO4 battery, the back propagation (BP) neural work model is chosen as the training algorithm due to its adaptive learning ability. Through the constant current discharge experiment, we explore the relationship between the battery capacity and the charge/discharge rate. Based on the correlation between the battery parameters and the SOC, we choose current and voltage as the inputs for the BP work to carry out the work training and SOC estimation. The correctness and accuracy of our algorithm are verified by the experiment results. 2. Lithiumion batteries RUL prediction. First, three algorithms . particle filtering (PF), support vector machine (SVM), autoregressive moving average (ARMA) are applied for RUL prediction. Based on the advantages and disadvantages of the above algorithms, a novel approach using improved autoregressive (AR) model with particle swarm optimization (PSO) is proposed. Then, the root mean square error (RMSE) is used as the fitness function for AR model order determination. In addition, ABSTRACT III the information contained in the data is updated through metabolism at the prediction stage which makes the AR model order change adaptively. Finally, the experimental data are used to validate the proposed prognostic approach, and the results show accurate RUL prediction trends and small errors. 3. Lithiumion batteries monitoring system development. Based on the algorithms developed above, a lithiumion battery monitoring system is designed to meet the requirements of the lithiumion battery monitoring. The system consists of the following ponents: signal acquisition for battery parameters, data transmission, puter monitoring, analysis and processing. Therefore, the SOC estimation and the RUL prediction are achieved in a real time fashion. Keywords: lithiumion battery, state of charge estimation, back propagation neural work, remaining useful life prediction, autoregressive model, monitoring system 目錄 IV 目 錄 第一章 緒 論 ....................................................................................................................1 研究背景及意義 .....................................................................................................1 國內(nèi)外研究現(xiàn)狀 .....................................................................................................2 鋰離子電池及其管理系統(tǒng) .............................................................................2 鋰離子電池荷電狀態(tài)估計 .............................................................................4 鋰離子電池剩余壽命預(yù)測 .............................................................................4 本文的主要內(nèi)容及組織結(jié)構(gòu) .................................................................................5 第二章 鋰離子電池特性研究 ..........................................................................................8 鋰離子電池結(jié)構(gòu) .....................................................................................................8 鋰離子電池原理 .....................................................................................................9 鋰離子電池的性能指標(biāo) .......................................................................................10 鋰離子電池的特點 ...............................................................................................12 本章小結(jié) ...............................................................................................................12 第三章 鋰離子電池荷電狀態(tài)估計 ................................................................................13 鋰離子電池荷電狀態(tài) ...........................................................................................13 荷電狀態(tài)定義及影 響因素 ...........................................................................13 常用荷電狀態(tài)估計方法 ...............................................................................14 基于人工神經(jīng)網(wǎng)絡(luò)的荷電狀態(tài)估計 ...................................................................16 人工神經(jīng)網(wǎng)絡(luò) ...............................................................................................16 誤差反向傳播網(wǎng)絡(luò) .......................................................................................18 獲得樣本數(shù)據(jù) ...............................................................................................21 建立神經(jīng)網(wǎng)絡(luò) ...............................................................................................24 神經(jīng)網(wǎng)絡(luò)測試及估計結(jié)果 ...........................................................................26 本章小結(jié) ...............................................................................................................29 第四章 鋰離子電池壽命預(yù)測 ........................................................................................30 電池壽命預(yù)測基本原理 .......................................................................................30 電池壽命試驗 .......................................................................................................31 壽命預(yù)測方法比較 ...............................................................................................32 目錄 V 粒子濾波 ................................................................