Effects of variability and rate on battery charge storage and lifespan

The growing prevalence of hybrid and electric vehicles, intermittent renewable energy sources, and other complex power systems has triggered a rapid increase in demand for energy storage. Unlike portable electronic devices, whose batteries can be recharged according to a pre-determined protocol simply by plugging them into the wall, many of these applications are characterized by highly variable charge and demand profiles. The central objective of this work is to assess the impact of power distribution and frequency on battery behavior in order to improve overall system efficiency and lifespan in these variable power applications.;We first develop and experimentally verify a model to describe the trade-off between battery charging power and energy stored to assess how varying power input affects battery efficiency. This relationship is influenced both by efficiency losses at high powers and by premature voltage cutoffs, which contribute to incomplete battery charging and discharging. We experimentally study the impact of variable power on battery aging in lead-acid, nickel metal hydride, lithium-ion and lithium iron phosphate batteries. As a case study we focus on off-grid wind systems, and analyze the impact of both power distribution and frequency on charge acceptance and degradation in each of these chemistries. We suggest that lithium iron phosphate batteries may be more suitable for off-grid electrification projects than standard lead-acid batteries. We experimentally assess the impact of additional variable charging parameters on battery performance, including the interplay between efficiency, frequency of power oscillations, state-of-charge, incomplete charging and path dependence. We develop a frequency-domain model for hybrid energy storage systems that couples non-stationary frequency analysis of variable power signals to a frequency-based metric for energy storage device performance. The experimental and modeling work developed herein can be utilized to optimize energy storage system design and control algorithms for variable power applications.