Self-discharge is a critical parameter in evaluating battery performance, particularly for repurposed batteries in energy storage applications. When batteries reach the end of their primary life in electric vehicles or consumer electronics, they often retain sufficient capacity for secondary use in grid storage or backup power systems. However, their prior usage history significantly influences self-discharge behavior, which in turn affects their reliability and economic viability in second-life applications.

The self-discharge rate of a battery refers to the gradual loss of charge when the battery is not in use. For lithium-ion batteries, this occurs due to internal chemical reactions, such as electrolyte decomposition, slow redox reactions between electrodes and electrolytes, and micro-short circuits caused by dendrite formation. In repurposed batteries, these mechanisms are often exacerbated by prior cycling and aging. Batteries with extensive charge-discharge histories exhibit higher self-discharge rates due to cumulative degradation of electrode materials, increased internal resistance, and electrolyte breakdown. For example, a lithium-ion battery cycled over 1,000 times at high temperatures may demonstrate a self-discharge rate twice as high as a new battery due to SEI layer growth and lithium inventory loss.

Testing protocols for second-life batteries must account for these variations. Standard self-discharge assessments involve storing batteries at a known state of charge and measuring voltage or capacity loss over time. For repurposed batteries, extended testing periods are necessary to capture nonlinear self-discharge behavior. A common approach involves storing batteries at 25°C and 50% state of charge for 28 days, with periodic capacity checks. Accelerated aging tests at elevated temperatures can also predict long-term self-discharge trends, though correlations must be validated for each battery chemistry and prior usage condition.

Prior usage history introduces several challenges in predicting remaining useful life. Batteries from electric vehicles, for instance, may have experienced different cycling patterns, depth-of-discharge profiles, and thermal environments. A battery frequently fast-charged in a hot climate will degrade differently from one used in moderate conditions with slow charging. These factors influence not only the absolute self-discharge rate but also its progression over time. In some cases, self-discharge rates increase exponentially after repurposing, making it difficult to estimate how long the battery will remain viable for grid storage.

Economic implications of accelerated self-discharge are significant for grid-scale applications. In stationary storage, batteries are often required to hold charge for extended periods to provide energy arbitrage or frequency regulation. A high self-discharge rate reduces the effective energy available for discharge, lowering the system's round-trip efficiency. For example, a battery system with a 5% monthly self-discharge rate loses approximately 46% of its stored energy over a year without any useful output. This directly impacts revenue streams in applications where energy retention is critical.

Mitigation strategies include rigorous sorting and grading of repurposed batteries based on their self-discharge characteristics. Batteries with similar histories and degradation patterns can be grouped to ensure uniform performance in storage systems. Advanced battery management systems can also compensate for self-discharge by adjusting state-of-charge calculations and implementing periodic top-up charging. However, these solutions add complexity and cost to second-life battery deployments.

The relationship between aging and self-discharge is not always linear. Some batteries exhibit stable self-discharge rates for extended periods before a sharp increase, while others degrade gradually. This unpredictability complicates lifecycle assessments and financial modeling for grid storage projects. Accurate forecasting requires large datasets of aged battery performance under various usage scenarios, which are still emerging as second-life applications gain traction.

In summary, repurposed batteries present unique challenges in self-discharge management due to their prior usage histories. Testing protocols must be adapted to capture nonlinear degradation patterns, and economic models must account for the impact of self-discharge on system performance. As the market for second-life batteries grows, standardized evaluation methods and predictive tools will be essential to ensure reliability and cost-effectiveness in energy storage applications.