Health-Aware Control for Planning Battery Lifespan

In controlling the State of Health (SOH) of batteries, understanding the individual and collective impact of physical factors such as current draw, temperature, and age is crucial. Traditional lab tests offer deep insights into battery degradation but cannot isolate the effects of specific variables, as they typically analyze a series of actions and conditions in combination. Although this is useful for estimating the SOH, it limits the ability to develop strategies for optimal control of the battery lifetime. To address this, as a first step, this article proposes a method for designing a function estimator based on the State–Action Cost (SAC) that considers current, voltage, temperature, and age to predict the micro-effects of each state– action combination on the SOH. This estimator will provide a more granular understanding of how individual actions and conditions contribute to battery health. In the second step, it will be demonstrated how this approach could significantly enhance the precision of SOH control and facilitate better management strategies for extending battery lifespan.