## 5 Conclusion  
This work applied the DCCAC method to evaluate the SOH of lead-acid batteries using a time series of voltage measurements during vehicle start-up. A comparison of a battery with SOH deterioration and one without signs of deterioration, within the same engine displacement, did not show a statistically significant cross-correlation, while comparisons between batteries with an adequate SOH state showed a statistically significant cross-correlation across the size boxes of time. This difference in behavior allowed for the segregation of batteries with adequate SOH from those with deteriorated SOH, nearing the end of their useful life, even though the battery is still capable of starting the combustion engine. This approach may offer advantages over other SOH estimation methods in the literature. It does not require complex modeling, extensive datasets, or high computational resources, making it an efficient, cost-effective solution for real-time battery monitoring in automotive systems. The DCCAC method can be integrated into vehicles to identify deteriorating SOH before the battery reaches critical failure levels, offering drivers a preemptive alert and allowing better maintenance planning. An essential step in comparing a battery with a good SOH to one with a deteriorated SOH is the calculation of the statistically non-significant zone. This step is crucial as it enables a meaningful comparison of results and helps define the critical region for the cross-correlation index. By establishing this zone, it becomes possible to accurately identify the thresholds where the cross-correlation index indicates potential concern regarding battery health. When compared to lithium-ion batteries, 12 V lead-acid batteries continue to hold a significant share in the global market. However, their challenges in operating efficiently under high-rate partial state of charge conditions make them less suitable for use in EVs. And in HEVs, the DCCAC needs to be tested and analyzed before it can be considered able to monitor battery degradation under partial state-of-charge conditions, a critical factor for ensuring optimal performance and longevity in energy storage applications. An option may be the usage of the method in E2 Ws, which rely heavily on efficient and affordable lead-acid battery systems, where DCCAC offers a low-cost, computationally efficient approach to identify battery deterioration before the critical failure occurs. Despite being used for the last few decades as an energy storage technology, lead-acid batteries will continue to play a crucial role in the global rechargeable battery market, offering significant advantages in terms of cost-effectiveness and recyclability. Future research should focus on expanding the application of DCCAC to different types of batteries, such as lead-carbon batteries, as well as integrating this method with other diagnostic tools to enhance predictive accuracy. It may also work with lithium-ion batteries, but new studies require to be conducted to confirm it. Real-world testing across various environmental conditions and usage patterns will help refine the method, potentially leading to improved models for predicting battery SOH across broader contexts. This work presents a new direction for improving battery health monitoring in the automotive industry. **Author Contributions:** Conceptualization, T.B.M., R.C.d.C., and M.A.M. ; methodology, T.B.M. and M.A.M. ; software, R.C.d.C. ; validation, R.C.d.C. and T.B.M. ; formal analysis, T.B.M. ; investigation, R.C.d.C. ; resources, M.A.M. ; data curation, R.C.d.C. ; writing--original draft preparation, T.B.M., R.C.d.C., R.L.S.M., H.B.d.B.P., and M.A.M. ; writing--review and editing, T.B.M., R.C.d.C., R.L.S.M., H.B.d.B.P., and M.A.M. ; visualization, R.C.d.C., R.L.S.M., H.B.d.B.P., and M.A.M. ; supervision, T.B.M. ; project administration, T.B.M. ; funding acquisition, M.A.M. All authors have read and agreed to the published version of the manuscript.