锂电池管理系统的研究与设计
文献类型：学位论文
| 作者 | 王笑天 |
| 学位类别 | 硕士 |
| 答辩日期 | 2014-05-28 |
| 授予单位 | 中国科学院沈阳自动化研究所 |
| 导师 | 杨志家 |
| 关键词 | 锂电池管理系统 SOC估算 卡尔曼滤波 电池均衡 STM32 |
| 其他题名 | Research and Application of lithium battery management system |
| 学位专业 | 模式识别与智能系统 |
| 中文摘要 | 近年来，环境问题、能源问题日益严重，已经影响到了人们的生活。人们一方面探索新能源，另一方面开发先进的能源管理方式。磷酸铁锂电池安全、无污染、可大电流放电等众多优点被人们广泛关注，在后备电源、汽车等领域得到了广泛应用。锂电池管理系统BMS（Battery Management System）也应运而生，成为很多科研就够的研究热点。简单来说，BMS管理系统就是动态监测电池运行过程中的各种参数，并根据采集到的数据信息进行容量估算，评估电池状态；再根据预设的电池管理策略，做出进一步的处理。如需要时进行备用电源放电、电量不足时进行充电、出现异常时进行报警等。 锂电池管理系统的关键技术是对电池的荷电状态估算，准确的荷电状态(SOC)的估计对提高电池使用寿命和提高安全性能都具有重要意义。当今，对电池的荷电状态估算有很多方法，本文以Thevenin电池模型和卡尔曼滤波算法为基础，对电池模型建立了状态空间表达式，分别采用最小二乘法和双卡尔曼滤波算法对该模型参数进行辨识。该算法一方面提高了该模型的精度，使电池模型能够较好的反映电池内部的真实状态，另一方面提高了SOC估算的精度。实验结果表明在不同的工况环境下，该算法在线估计SOC具有较高的精度和对环境的适应度，最大误差小于4.5%。此外，还验证了双卡尔曼滤波算法具有较好的收敛性和鲁棒性，可以有效解决初值估算不准和累积误差的问题。 锂电池管理系统的另一项关键技术是均衡管理，由于锂电池串联充电存在不一致现象，因此需要均衡管理。根据工况的现场环境特点，本文采用了传统的电阻式均衡方式，虽然产生了一定能耗，但是体积小、可靠性高、实用性强的特点决定了该方法适用于备用电源领域。为了尽量减少能耗产生的热量，设计了改进的均衡控制策略，做到了分散式均衡、电池异常检测、改进的充放电策略等，大大改善了热管理的问题。 此外，论文还完成了以下工作：基本完成了锂电池管理系统的系统设计，设计了硬件平台和部分软件模块。硬件平台以STM32为核心，主要包括核心控制板卡、主控制板卡、数据采集板卡，具体包括STM32最小系统，数据采集模块（如温度，电压等），均衡管理模块，通信系统（如串口，CAN通信，SPI通信等）。软件部分编写这些模块的驱动程序，并进行了调试。设计的BMS系统，无论从结构上还是功能上，能够达到系统的要求。 |
| 索取号 | TM911.41/W37/2014 |
| 英文摘要 | In recent years, the energy crisis and the environmental problems have become more prominent, which has affected people's lives. On the one hand, people explore new energy sources, on the other hand, to develop the advanced energy management. LiFePO4 batteries to its own security, no pollution, high current discharge and many other performance characteristics are gradually to be concerned, and have a wide range of applications in the backup power and car supply etc. Lithium battery management system BMS (Battery Management System) has emerged and became a very hot research point in many research institutions. In simple terms, the lithium battery management system is mainly dynamic monitoring the state parameters of lithium batteries, according to a variety of information collected，to estimate the remaining capacity of the battery, and to assess the state of the battery; then by the default battery management strategies to further processing. The key technology of the lithium battery management system is to estimate the battery state of charge, the accurate state-of-charge (SOC) estimates to improve battery life and safety performance is very important. Today, there are many ways to estimate the battery state of charge, this paper establish the state-space representation of the battery model which is based on Thevenin battery model and Kalman Filtering algorithm. In order to improve the previous model’s accuracy, parameters of the battery model are identified by applying respectively the least squares method and the dual extended Kalman filtering (DEKF) algorithm. On one hand, the algorithm improves the precision of the model, and that facilitates the battery model to well reflect the actual internal state of the battery; On the other hand, improves the accuracy of SOC estimation. Results of these experiments demonstrate that under various operating conditions, the algorithm, when applied to evaluate SOC on line, is of relatively high accuracy and of good adaptability, the maximum error is less than 4.5%. Finally, the algorithm of DEKF proves to have better convergence and robustness, and thus can solve problems efficiently of the inaccuracy of initial-value estimation and error accumulation. Another key technology of the lithium battery management system is equilibrium management, because the lithium battery charging is not consistent, so need to equilibrium management. According to the environmental characteristics of the site conditions, we use the traditional resistive equilibrium way, although have certain energy consumption, but has the advantages of small size, high reliability, strong practicability, which is decided that the method is applicable to the standby power supply. In order to minimize the energy consumption, this paper design the improved equilibrium control strategy, to achieve a decentralized equilibrium, battery anomaly detection, improved battery charging and discharging strategy, greatly improved thermal management. In addition, the paper also completed the following work: basically completed the system design of the lithium battery management system, including the hardware platform and the software modules. The hardware platform with STM32 as the core controller, mainly includes the core control board, main control board, data acquisition board, specifically including the STM32 minimum system, the data acquisition module (such as temperature, voltage, etc.), equilibrium management module, communication system (such as serial port, CAN communication, SPI communication). In terms of software, written these drivers modules, and debugging. The designed BMS system, both from the structure or function, can meet the system requirements. |
| 语种 | 中文 |
| 产权排序 | 1 |
| 页码 | 63页 |
| 分类号 | TM911.41 |
| 源URL | [http://ir.sia.ac.cn/handle/173321/14823]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 推荐引用方式 GB/T 7714 | 王笑天. 锂电池管理系统的研究与设计[D]. 中国科学院沈阳自动化研究所. 2014. |
入库方式： OAI收割
来源：沈阳自动化研究所
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