Abstract
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.
Highlights
Energy saving and environmental protection are currently becoming the inevitable trend in the development of internal combustion engine (ICE) vehicles due to the pressures of environment degradation and environmental regulations.1 To achieve this goal, an increasing number of energy management systems (EMS) are applied to the vehicles and become an indispensable vehicular control system.2,3 For these systems, fast and accurate acquisition of battery state is the basis of realizing complex energy management functions, such as energy recovery and battery protection.4 The battery state of charge (SOC) and the battery state of health (SOH) are the two most important battery state parameters of the EMS in vehicles
Accurate and rapid determination of the SOC and SOH of lead–acid batteries is a necessary condition for the complex energy management of ICE vehicles
The results demonstrate that using electrochemical impedance spectroscopy (EIS) to measure battery SOH is a feasible method
Summary
Energy saving and environmental protection are currently becoming the inevitable trend in the development of internal combustion engine (ICE) vehicles due to the pressures of environment degradation and environmental regulations. To achieve this goal, an increasing number of energy management systems (EMS) are applied to the vehicles and become an indispensable vehicular control system. For these systems, fast and accurate acquisition of battery state is the basis of realizing complex energy management functions, such as energy recovery and battery protection. The battery state of charge (SOC) and the battery state of health (SOH) are the two most important battery state parameters of the EMS in vehicles. An online collaborative estimation algorithm is proposed on the basis of this augmented system model to determine SOC and SOH of lead–acid battery in real time. When the EMC obtains the SOC, SOH and other information, it dynamically adjusts the output of lead–acid battery and intelligent alternator according to these control strategies to manage electrical energy in vehicles, such as battery maintenance and braking energy recovery. Considering the computing complexity and estimation accuracy of battery states, the Randles equivalent circuit model in Figure 5 is used for the lead–acid battery in this study.. If the timevarying factors are ignored and fixed model parameters are used, the estimation accuracy of the SOC and SOH will be affected.29 These circuit model parameters need to be identified online to obtain better determination. The parameters Ri, Input: Uk-1, Uk-2, Ik-1, Ik-2, θk-1, Pls, k-1
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