Li-Ion Battery SoC Estimation Using a Bayesian Tracker

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter’s squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence. INTRODUCTION Recently, hybrid and electric vehicles have received substantial attention due to their high fuel efficiency, low cost of operation and reduced greenhouse gas emission. At the heart of these vehicles lies rechargeable (secondary) batteries as a source of energy. Specifically, Li-ion batteries have become a more popular choice than NiMH batteries in newer generation hybrid and electric vehicles due to their high energy density, slow selfdischarge, and zero memory effect. A battery management system (BMS) is required to keep the Li-ion cells within their specified operating range by sensing voltage, current, temperature and internal pressure signals. This ensures the availability of reliable electrical power, improves overall energy efficiency, protects cells from damage and prolongs battery lifespan. The battery SoC is one of the most important parameters that BMS estimates in real time to accomplish its goals. The SoC needs to be estimated accurately in a broad range of environmental and operating conditions, including environmental temperature (from freezing cold to scorching hot) and battery age (from new to old). To this end, the following two key elements must be readily available: • An accurate battery model and • A robust SoC estimation strategy. A Brief Review of Battery Modelling Modeling refers to the process of analysis and synthesis to determine a suitable mathematical description that characterizes the relevant dynamics of a component under test. To be useful, the model must be scalable and easy to be simulated. A battery model is required to capture battery physics accurately. The two broad approaches to battery modelling are • Equivalent electrical-circuit modeling and • Electrochemical modeling Equivalent electrical-circuit models use RC (ResistorCapacitor) circuits to model the charge and discharge behavior of Li-ion batteries [15, 24, 10]. They may consist of the first-order, second-order or the third-order RC models coupled with a hysteresis effect [24, 10]. Equivalent circuit models are conceptually simple to understand and use a few parameters to be identified. However, they provide little insight into underlying physical battery limitations. On the other hand, electrochemical modeling uses the first principles– they use partial differential equations to capture the diffusion dynamics of Li-ions in the solid phase composite electrodes and the electrolyte. Although electrochemical modeling provides a more accurate SoC estimate, it comes with a price; it requires many unknown parameters to be identified. However, once an electrochemical model is developed, it can be easily manipulated to meet different battery specifications. For these reasons, electrochemical modeling is often preferred to equivalent electrical-circuit modeling [17, 7]. A Brief Review of SoC Estimation methods Estimating the SoC in an electric vehicle is analogous to gauging fuel in a conventional vehicle. In general, SoC estimation techniques can be broadly categorized into two types: