As advanced batteries continue to grow in the sectors of electrifications of transportation and energy storage systems, risk management by the stakeholders has also become an emerging topic of concerns. To date, the practical approach in addressing such concerns has not been fully understood and implemented. One of the impediments is the lack of full appreciation of the complexity involved in handling the issues in the research community and need of technical competency in addressing them. The industry does not really have the luxury to learn from the hiccups and disasters of fire and explosion incidents to calm the anxiety from the consumers in the expectation of reliable and safe products in the marketplace, while we continue to learn from the mistakes and manage the risks in the “green energy transformation.”

Batteries have some unique aspects that traditionally mechanical and electrical devices need not to pay attention to: performance degradation by aging and its path dependency. This unique aspect makes the battery management of performance, including risks, much more difficult than usual. Not only the initial properties of the battery systems need to be compatible for manufacturing processes without trading off performance, but also the consequences of aging and degradation during operations need to be managed to endure reliability and safety challenges. Worse than that, those system properties are often affected by the environment they operate. Therefore, matured battery designers need to familiarize not only battery systems’ capability but also the nature of the environments and the operating conditions to cater in their designs. To understand such causality in battery design is inherently challenging. This is something needs to be overcome to address battery reliability and safety issues. This is why battery system diagnostics and prognostics are pivotal to addressing the battery reliability and safety issues.

Battery diagnostics and prognostics are a vital part of the solutions to deal with this situation. Through proper diagnostic and prognostic analyses, one can extract useful information from the failure analyses (FA), including failure mode and effect analyses (FMEA). However, for these analyses to be feasible in battery research, it requires additional efforts to ensure accurate and precise information being extracted from the analyses in a quantitative manner, which is not commonly practiced in the industrial and the research communities. Here, the issues with quantitative diagnostic and prognostic analyses are discussed and explained; using practical examples in some of the abuse studies to illustrate the critical aspects of such analyses with mechanistic understanding.