The lithium-ion battery is a viable power source for hybrid electric vehicles (HEVs) and, more recently, electric vehicles (EVs). Its performance, especially in terms of state of charge (SOC), plays a significant role in the energy management of these vehicles. The extended Kalman filter (EKF) is widely used to estimate online SOC as an efficient estimation algorithm. However, conventional EKF algorithms cannot accurately estimate the difference between individual batteries, which should not be ignored. However, the internal resistance of a battery can represent this difference. Therefore, this work proposes using an EKF with internal resistance measurement based on the conventional algorithm. Lithium-ion battery real-time resistances can help the Kalman filter overcome defects from simplistic battery models. In addition, experimental results show that it is useful to introduce online internal resistance to the estimation of SOC.

State of charge (SOC) and state of health (SOH) are two significant state parameters for the lithium ion batteries (LiBs). In obtaining these states, the capacity of the battery is an indispensable parameter that is hard to detect directly online. However, there is a strong correlation relationship between this parameter and battery internal resistance. This article first shows a simple and effective online internal resistance detection method. Secondly, the relationship between the measured internal resistance and the LiBs capacity is established by linear fitting. Finally, the capacity through internal resistance conversion is applied in SOC estimation. The estimation results show that this method can effectively enhance the SOC estimation accuracy regardless of temperature change and battery degradation.

The health and safety of lithium-ion batteries are closely related to internal parameters. The rapid development of electric vehicles has boosted the demand for online battery diagnosis. As the most potential automotive battery diagnostic technology, AC impedance spectroscopy needs to face the problems of complex test environment and high system cost. Here, we propose a DC impedance spectroscopy (DCIS) method to achieve low-cost and high-precision diagnosis of automotive power batteries. According to the resistance–capacitance structure time constant, this method can detect the battery electrolyte resistance, the solid electrolyte interphase resistance and the charge transfer resistance by controlling the pulse time of the DC resistance measurement. Unlike AC impedance spectroscopy, DCIS does not rely on frequency domain impedance to obtain battery parameters. It is a time-domain impedance spectroscopy method that measures internal resistance through a time function. Through theoretical analysis and experimental data, the effectiveness of the DCIS method in battery diagnosis is verified. According to the characteristics of DCIS, we further propose a fast diagnostic method for power batteries. The working condition test results show that this method can be used to diagnose online battery life and safety.