State of charge (SOC) and state of health (SOH) of batteries are the indispensable control decision variables for online energy management system (EMS) in modern internal combustion engine vehicles. The real-time and accurate determination of SOC and SOH is essential to the reliability and safety of EMS operation. Obtaining good accuracy for the SOC estimation is difficult without considering SOH because of their coupling relationship. Although several works on the joint estimation of SOC and SOH of lithium–ion batteries are available, these studies cannot be applied to lead–acid batteries because of the differences in physical structure and characteristics. This study handles the problem of modeling the relationship between SOC and SOH of lead–acid battery and their online collaborative estimation. First, the structure and control strategy of a bus-based EMS is discussed, and the improper energy control actions of EMS due to the inaccurate SOC estimation are analyzed. Second, an instantaneous correlation factor β for SOC and SOH is defined as a new state estimating variable, and the simplified linear relationship model between β and open circuit voltage is established through the battery experiments. Third, a discretized augmented system equation of β is deduced according to the relationship model and the Randles circuit model. The least square circuit parameter identification (LSCPI) algorithm is presented to identify the time-varying circuit model parameters, while the adaptive Kalman filter for augmented system (AKFAS) algorithm is employed to estimate β online. A collaborative estimation algorithm is proposed on the basis of the LSCPI and AKFAS to determine SOC and SOH of lead–acid battery in real time, and a demo intelligent battery sensor is developed for its implementation. The results of battery charging and discharging experiments indicate that the proposed method has high accuracy. The estimation accuracy of SOC of this method reaches 3.13%, which is 7% higher than that of the existing method.

电池的充电状态（SOC）和健康状态（SOH）是现代内燃机车辆中在线能量管理系统（EMS）必不可少的控制决策变量。SOC和SOH的实时，准确确定对于EMS操作的可靠性和安全性至关重要。由于它们之间的耦合关系，如果不考虑SOH，就很难获得良好的SOC估计精度。尽管可以进行联合估计锂离子电池SOC和SOH的工作，但是由于物理结构和特性的差异，这些研究无法应用于铅酸电池。这项研究解决了铅酸电池的SOC和SOH之间的关系及其在线协作估计建模的问题。第一，讨论了基于总线的EMS的结构和控制策略，并分析了由于SOC估算不正确而导致的EMS能量控制不当行为。二，瞬时相关因子将SOC和SOH的β定义为新的状态估计变量，并通过电池实验建立了β与开路电压之间的简化线性关系模型。第三，根据关系模型和Randles电路模型，推导了离散的β的增广系统方程。提出了最小二乘电路参数识别（LSCPI）算法来识别时变电路模型参数，而自适应增强系统自适应卡尔曼滤波器（AKFAS）算法用于估计β线上。提出了一种基于LSCPI和AKFAS的协同估计算法，用于实时确定铅酸电池的SOC和SOH，并为其开发了演示智能电池传感器。电池充放电实验结果表明，该方法具有较高的准确性。该方法的SOC估计精度达到3.13％，比现有方法高7％。