Improved intelligent decision model for electric vehicle battery using q-Rung orthopair fuzzy Schweizer–Sklar prioritized Z-information

Why choosing the right EV battery is so tricky

As electric vehicles move from niche to mainstream, the battery hidden under the floor is doing a lot of heavy lifting. It determines how far a car can drive, how fast it recharges, how long it lasts before needing replacement, and how safe and environmentally friendly it is. Yet engineers and policymakers must pick among competing battery technologies using information that is often incomplete, uncertain, or based on expert opinion rather than hard numbers. This paper presents a new mathematical decision tool designed to make those choices more reliable when the facts are fuzzy.

Balancing many needs at once

Selecting an electric vehicle battery is a classic balancing act. Options such as nickel–cadmium, lead–acid, sodium–sulfur, lithium‑ion, and flow batteries each have their own mix of strengths and weaknesses. Some charge quickly but wear out sooner; others last for many years but are bulky or require high operating temperatures. Decision makers must weigh technical performance, cost, safety, and environmental impact all at once. Traditional decision methods assume that every criterion can be described with precise numbers, but real‑world data about, say, cycle life in harsh climates or failure rates in crashes are rarely that crisp.

From fuzzy opinions to structured judgments

To cope with this ambiguity, researchers have long used “fuzzy” sets, which allow things to be partly true rather than simply yes or no. Later refinements, such as intuitionistic and Pythagorean fuzzy sets, added ways to express hesitation or conflicting information. The method proposed in this paper builds on a still more flexible framework called q‑rung orthopair fuzzy sets, which can capture very high degrees of uncertainty, and combines it with Z‑numbers, a scheme that separately records both a value and the confidence experts have in that value. In plain terms, the model not only records that a battery is judged “high” on safety but also how trustworthy that judgment is.

A tunable way to combine conflicting criteria

The second pillar of the work is a family of mathematical rules, known as Schweizer–Sklar aggregation operators, used to blend many uncertain inputs into an overall score. Unlike simple averaging, these operators have a built‑in tuning knob that lets decision makers lean toward cautious or bold strategies without redesigning the whole model. When set to be more conservative, the method behaves as if it were saying, “all key criteria must be reasonably good”; when set to be more optimistic, it allows an outstanding strength in one area to compensate for weaknesses elsewhere. The authors extend these rules so they work smoothly with the combined fuzzy and Z‑number framework and allow priorities and weights to be assigned to different criteria.

Testing the method on real battery choices

To see how well the model works, the authors apply it to a realistic case study comparing five battery technologies for use in electric vehicles. Experts rate each option on three crucial aspects: charging time, cycle life, and safety. These verbal assessments (“low,” “medium,” “high,” and so on) are translated into the new fuzzy format, including both the rating itself and the experts’ confidence in it. The proposed operators then aggregate this information in several slightly different ways, and each battery receives a final score. Across all versions of the method, lithium‑ion batteries consistently emerge as the top choice, closely followed by sodium–sulfur and flow batteries, while nickel–cadmium and lead–acid trail behind because of shorter life, slower charging, or greater environmental concerns. A separate analysis using a well‑known technique called WASPAS, adapted to the same fuzzy framework, produces the same best choice, lending credibility to the new approach.

Robust results, even when priorities shift

The study also explores what happens when the tuning parameters in the Schweizer–Sklar operators are changed, mimicking more risk‑averse or more risk‑tolerant attitudes. Although the exact numerical scores shift, the ranking of batteries remains remarkably stable: lithium‑ion stays in first place, the middle group stays in the middle, and the weakest options stay at the bottom. This stability is important in practice, because it suggests that the method is not overly sensitive to subjective choices about how to blend criteria, and that its conclusions are not easily overturned by small modeling changes.

What this means for electric vehicles and beyond

In everyday terms, the paper offers a more nuanced calculator for hard choices where evidence is incomplete and expert opinions differ. For electric vehicles, it supports the current industry focus on lithium‑ion batteries while making room to compare emerging technologies in a fair and transparent way. More broadly, the same framework could help with other complex sustainability decisions, such as placing charging stations, selecting energy storage for the grid, or evaluating green technologies. By explicitly representing both uncertainty and confidence, and by giving decision makers a tunable way to trade off competing goals, the proposed model turns messy, real‑world judgments into clearer guidance without pretending that the world is simpler than it is.

Citation: Ali, A., Shaikh, H.U., Ashraf, S. et al. Improved intelligent decision model for electric vehicle battery using q-Rung orthopair fuzzy Schweizer–Sklar prioritized Z-information. Sci Rep 16, 13890 (2026). https://doi.org/10.1038/s41598-025-27621-6

Keywords: electric vehicle batteries, multi-criteria decision making, fuzzy logic, uncertainty modeling, sustainable technology