Battery lifespan is one of the largest considerations when designing battery packs for electrified vehicles. Even during vehicle operation, it is essential to monitor the progression of a battery health as it degrades and predict battery life. This thesis presents a preliminary severity factor analysis based on available experimental data and details the development of an algorithm for predicting, while in operation, the remaining life of a battery based on the growth of internal resistance.
Nine lithium ion batteries were systematically aged through severe aging protocols spanning multiple C-rates (2C, 4C and 8C), low ranges of SOC (0-10, 0-20 and 0-30%), and elevated temperature (55 deg C). Their internal resistance was continuously calculated at each sharp current transition, and these values were filtered and processed. Severity factors were calculated for each battery by determining the average rate of resistance growth over a battery life and a preliminary analysis of these factors was carried out. A resistance growth dynamic model was developed to identify rates of resistance growth on a local basis as resistance values were collected. These local rates of resistance growth were then used to calculate predicted future rates of resistance growth, which were in turn used to predict remaining life.
The life prediction algorithm produced continuously updated predictions of remaining battery life that proved relatively accurate for cases of constant battery aging conditions. This computationally simple algorithm could be implemented onboard an electrified vehicle to provide estimates of remaining battery life based on resistance growth. This methodology can in principle be readily extended to track capacity degradation as well, provided that a feasible capacity estimator can be developed on the basis of vehicle measurements.