In the fast-moving electric vehicle (EV) manufacturing industry, every decision carries weight. One of the most crucial elements in producing high-quality electric vehicles is ensuring the durability and reliability of vehicle batteries.
This is where Weibull analysis steps in as a powerful tool. We will demonstrate four key uses for Weibull analysis in EV battery manufacturing and how this tool can pay dividends in terms of time and cost savings.
#1: Optimize Warranties
It is important to set appropriate and realistic warranty policies to balance customer expectations and financial sustainability. Weibull analysis can help manufacturers estimate the distribution of battery life lengths, which allows them to create generous and fiscally responsible warranties.
The sample here contains data from over 10,000 batteries. As you can see, over 95% of batteries last at least 5 years, with a mean length of life of just under ten years. This information can help EV automotive companies make smarter and better-informed decisions.
#2: improve product quality
Battery manufacturers must continually strive to improve the quality and reliability of their products to meet demanding EV market requirements. This includes identifying and addressing potential design flaws, material weaknesses or manufacturing process issues that could lead to premature battery decay or failure.
For example, perhaps you are analyzing field data and identify that a particular model of battery has a higher failure rate than expected due to overheating during fast charging. Weibull analysis can be used to pinpoint the failure mode and work on improving the battery’s thermal management system, enhancing product quality and reliability.
Check out our webinar, "Data-Driven Decision Making: Harnessing Reliability Analysis for Optimal Results" to learn more.
#3: Reduce Manufacturing Cost
Weibull helps to reduce waste and guide production processes which can lower costs. It can also help identify where your resources can be used more efficiently.
Weibull analysis may uncover that a specific production step, such as the electrolyte filling process, contributes to variations in battery life. The analysis provides insights into the distribution of battery lifetimes, revealing that inconsistencies in the electrolyte filling process can lead to a higher likelihood of early failures.
#4: Allocate resources
Weibull can help prioritize which batteries or product batches require closer monitoring or further investigation, which allows for greater resource allocation.
For example, in a manufacturing plant, Weibull analysis could help to identify that a particular production line consistently produces batteries with shorter life spans. The manufacturer can allocate more quality control personnel and resources to this line to address the issues as soon as possible and reduce possible defects.
