锂离子电池精确的荷电状态（SOC）和功率状态（SOP）是电动汽车能否稳定运行的关键，也是电池管理系统的核心问题和技术难点。为充分发挥锂电池性能并使车用动力电池BMS系统安全可靠的工作，需采用荷电状态和持续峰值放电功率状态作为判断电池性能的重要指标。为了精准地获取锂离子电池模型参数和内部状态估计值，本文建立二阶RC等效电路模型，设计基于双容积卡尔曼滤波（D-CKF）的锂电池SOC估计与多参数约束的SOP估计算法。

在分析锂离子电池结构与工作原理的基础上，开展动态容量测试、开路电压测试、快速脉冲放电工况以及EPA城市动力工况（UDDS）测试，完成动态容量建模、SOC-OCV曲线标定。建立二阶RC等效电路模型，利用人工蜂群算法对模型参数进行初始最优解标定，为模型参数辨识初始化作参考。采用双容积卡尔曼滤波算法同时进行锂电池模型参数辨识和SOC估计。设计基于SOC的锂电池多参数约束SOP估计方法，能实现最优BMS配置，延长电池使用寿命；在基于SOC、端电压和单体峰值电流约束条件计算峰值电流，根据多参数约束下的峰值放电电流计算不同持续时间峰值放电功率。将半实物仿真系统与锂电池测试系统相结合，在快速脉冲放电工况和UDDS工况下，对SOC估计算法和多参数约束条件下SOP估计方法的精度实现快速控制原型（RCP）验证。

试验结果表明：无论是恒电流放电还是复杂交变电流放电，选用D-CKF算法进行锂电池模型参数辨识均能够提高模型端电压预测精度。在快速脉冲放电工况和UDDS工况下，基于D-CKF算法的模型参数辨识端电压平均误差分别为0.0067和0.0066。选用D-CKF算法进行锂电池SOC估计能够较快修正初始误差，具有较强的跟踪收敛能力。在快速脉冲放电工况和UDDS工况下，D-CKF算法的SOC估计平均误差保持在0.01左右，相较于传统CKF算法分别降低34.7%和41.9%。所设计的锂电池多参数约束下SOP估计能够在定义的持续放电时间内提高电池利用率及保障系统安全性。在快速脉冲放电工况和UDDS工况下，电池SOP估计平均误差均在4W左右，不同SOC区间的峰值电流约束条件不同，如果仅单约束条件进行峰值SOP估计，会造成某一区间内电流偏大，缩短电池使用寿命。

Accurate State of Charge (SOC) and State of Power(SOP) of lithium-ion batteries are the keys to electric vehicles stable operation, and also the core issues and technical difficulties of battery management system. In order to full play performance of lithium battery and make the vehicle power battery BMS system work safely and reliably, State of Charge and sustained peak discharge State of Power are used as important indicators to judge battery performance. For the good of accurately obtain the lithium-ion battery model parameters and internal state estimate. this thesis establishes the second-order RC equivalent circuit model, the lithium battery SOC estimation and multi-parameter constrained SOP estimation algorithm based on the double-cubature kalman filter (D-CKF) are designed.

On the basis of analyzing the structure and working principle of lithium-ion batteries, the dynamic capacity tests, open-circuit voltage tests, rapid pulse discharge conditions and EPA urban dynamic conditions (UDDS) tests were carried out, dynamic capacity modeling and SOC-OCV curve calibration are completed. The second-order RC equivalent circuit model is established, the artificial bee colony algorithm is used to model parameters initial optimal solution calibration, which provides reference for the initialization of model parameter identification; The double-cubature kalman filter algorithm is used to concurrence lithium battery model parameter identification and SOC estimation. Design a lithium batteries multi-parameter constrained SOP estimation method based on SOC, which can achieve optimal BMS configuration and prolong battery life; Calculate the peak discharge current based on SOC, terminal voltage and single maximum peak current constraints, according to multi-parameter constraint peak discharge current calculate peak discharge power of different duration. The hardware-in-the-loop simulation system and lithium battery test system are combined, under rapid pulse discharge conditions and UDDS conditions, the accuracy of SOC estimation algorithm and SOP estimation method under multi-parameter constraints is verified by rapid control prototype(RCP).

The test results show that whether is constant current discharge or complex alternating current discharge, the D-CKF algorithm is used to identify lithium battery model parameters, which can improve model terminal voltage prediction accuracy. Under rapid pulse discharge conditions and UDDS conditions, the model parameter identification based on D-CKF algorithm terminal voltage average errors separately for 0.0067 and 0.0066. The D-CKF algorithm for lithium battery SOC estimation can quickly correct initial error and has strong tracking convergence ability. Under rapid pulse discharge conditions and UDDS conditions, the average error of SOC estimation based on the D-CKF algorithm remains at about 0.01, compared with the traditional CKF algorithm separately for decreases 34.7% and 41.9%. The designed lithium battery multi-parameter constraint SOP estimation can improve the battery utilization rate and ensure system security within the defined continuous discharge time. Under rapid pulse discharge conditions and UDDS conditions, the average error of the battery SOP estimation is about 4W, the peak current constraints in different SOC intervals are different, if only single constraint is used to peak SOP estimation, peak current discharge will be too large at a certain interval, which battery service life will shorten.

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