高安全性、长循环寿命的磷酸铁锂电池在纯电动汽车上得到了广泛应用，磷酸铁锂电池性能研究是搭建动力电池管理系统（Battery management system, BMS）必需的基础工作之一，对提高纯电动汽车的安全性和经济性具有重要意义。本论文主要研究了磷酸铁锂电池的基本性能、多应力加速下的循环耐久性以及估算电池荷电状态（State-of-charge, SOC）的主要方法。电池主要基本性能参数如容量、内阻、开路电压（Open-circuit-voltage, OCV）等随环境与工况的变化呈现高度的非线性。在电池基本性能研究中，首先，通过稳态放电实验研究了电池容量与电流倍率等因素的关系；其次，利用阶跃法研究了电池不同SOC下的充放电欧姆内阻和极化内阻；再次，实验得到了电池的开路电压曲线，并探讨了获取稳定开路电压曲线的合理实验方法；最后，研究了不同环境温度对电池容量、内阻和开路电压等参数的影响。在电池耐久性研究中，建立了电池寿命预测模型。该模型全面考虑了电池寿命的多个影响因素，如环境温度、放电倍率、放电截止电压、充电倍率和充电截止电压等，通过对照实验，证实了因素间耦合性的存在，得到了耦合强度与应力水平的关系，分析了不同应力水平下的耦合性对寿命衰减的影响程度。进而，提出了基于耦合强度判断和多因素输入的寿命建模方法，并基于模型的因素敏感性分析了各因素对电池寿命影响的权重。在循环进入稳定衰减期后，这一考虑了因素间耦合性的耐久性模型对电池寿命的预测误差达到15%以内。在电池SOC估算方法的研究中，分别使用了模型算法和改进的安时积分方法。直接使用模型算法的估算结果波动过大，结合卡尔曼滤波等递归算法一并使用可取得稳定的SOC估算结果。将多因素标定的SOC-OCV曲线簇替代传统的单一曲线用于校准安时积分方法中的SOC初值，可显著提高其校准精度，扩大方法在SOC区间上的适用范围，并提高初值校正的频率，减小误差累积。采用这一SOC-OCV曲线簇方法进行SOC初值校准，在SOC小于40%或大于70%的区间上，其精度在5%以内，而在40%至70%的区间内，精度在15%以内。论文涉及的实验方法和主要结论对于混合动力汽车及其他类型的锂离子电池也具有一定的通用性。
LiFePO4 battery has been widely applied to electric vehicles due to its high safety and long cycle life. The research of the battery performance provides the main reference for the construction of battery management system (BMS), and has great significance to improve the security and energy economy of the electric vehicles.The research has been conducted on the basic characterization of the battery, the cycle durability under multi-stress and the approaches for the state-of-charge (SOC) estimation.The basic parameters such as battery capacity, internal resistance and open-circuit-voltage (OCV) demonstrate a high nonlinearity with the changes in environment and working conditions. First, the relationship between capacity and current ratio is studied by steady-state discharge experiments. Second, the charge/ discharge ohmic internal resistance and polarization resistance under different SOC have been analyzed through a step-input method. Then, the OCV curve is studied and the discussion on reasonable experimental methods for the stable OCV curve has been carried out. Finally, the impact of the different ambient temperature on battery capacity, internal resistance and OCV is considered. In the research of battery durability, a battery life prediction model has been established. The model takes several influence factors into account, such as ambient temperature, discharge rate, discharge cut-off voltage, charge rate and charge cut-off voltage. The existence of coupling between these factors has been verified, and the relationship between stress levels and the coupling intensity is obtained by experiments. Then, the influence of coupling on capacity fade is analyzed under different stress levels, and the life prediction model is developed based on the coupling intensity determination and multi-factor input. Additionally, a model-based sensitivity analysis is conducted to reveal the weight of these factors on battery life. After the battery has entered the stable decay phase, the life prediction model, which has taken into account the coupling between factors, has reached an error of 15% or less.
