Performance analysis results of a battery fuel gauge algorithm at multiple temperatures

Evaluating a battery fuel gauge (BFG) algorithm is a challenging problem due to the fact that there are no reliable mathematical models to represent the complex features of a Li-ion battery, such as hysteresis and relaxation effects, temperature effects on parameters, aging, power fade (PF), and capacity fade (CF) with respect to the chemical composition of the battery. The existing literature is largely focused on developing different BFG strategies and BFG validation has received little attention. In this paper, using hardware in the loop (HIL) data collected form three Li-ion batteries at nine different temperatures ranging from -20 °C to 40 °C, we demonstrate detailed validation results of a battery fuel gauge (BFG) algorithm. The BFG validation is based on three different BFG validation metrics; we provide implementation details of these three BFG evaluation metrics by proposing three different BFG validation load profiles that satisfy varying levels of user requirements

Experimental set-up and procedures to test and validate battery fuel gauge algorithms

A battery fuel gauge (BFG) helps to extend battery life by tracking the state of charge (SOC) and many other diagnostic features. In this paper, we present an approach to validate the SOC and time-to-shutdown (TTS) estimates of a BFG. Hardware-in-the-loop (HIL) testing under realistic usage scenarios provides a means for BFG algorithm evaluation and provides insights into practical implementation and testing of BFG algorithms in battery management systems. We report the details of a HIL system that was designed to validate the SOC and TTS estimation capability of BFG algorithms; different current load profiles were synthesized to replicate typical battery usage in portable electronic applications; the HIL system is automated with the help of programmable current profiles and is designed to operate at various controlled temperatures; three performance validation metrics are formulated for an objective assessment of SOC and TTS tracking algorithms. The HIL setup and the performance validation metrics are used to evaluate a BFG developed by the authors using three different batteries at temperatures ranging from -20 °C to 40 °C

A battery chemistry-adaptive fuel gauge using probabilistic data association

This paper considers the problem of state of charge (SOC) tracking in Li-ion batteries when the battery chemistry is unknown. It is desirable for a battery fuel gauge (BFG) to be able to perform without any offline characterization or calibration on sample batteries. All the existing approaches for battery fuel gauging require at least one set of parameters, a set of open circuit voltage (OCV) parameters, that need to be estimated offline. Further, a BFG with parameters from offline characterization will be accurate only for a known battery chemistry. A more desirable BFG is one that is accurate for any battery chemistry. In this paper, we show that by storing finite sets of OCV parameters of possible batteries, we can derive a generalized BFG using the probabilistic data association (PDA) algorithm. The PDA algorithm starts by assigning prior model probabilities (typically equal) for all the possible models in the library and recursively updates those probabilities based on the voltage and current measurements. In the event of an unknown battery to be gauged, the PDA algorithm selects the most similar OCV model to the battery from the library. We also demonstrate a strategy to select the minimum sets of OCV parameters representing a large number of Li-ion batteries. The proposed approaches are demonstrated using data from portable Li-ion batteries