The accelerating growth of electric mobility and stationary storage has intensified demand for lithium-ion bat-
teries, where raw materials represent 50–70% of total cost. Volatility in Ni, Co and Li markets therefore poses
major risks for affordability and investment decisions. This study systematically evaluates existing battery-cost
models with a specific focus on how raw-material price inputs are represented, standardized and propagated
into final $/kWh outputs.
A comparative review of sixteen representative models, including BatPaC, GREET®, EverBatt and CellEST,
investigates methodological assumptions, cost-element structure, and transparency of data sources. This work
provides the first review that jointly analyses deterministic cost models alongside probabilistic validation using
real-world commodity volatility. Robustness is assessed through a 1,000-run Monte Carlo simulation based on
historical price distributions of key metals.
Results indicate clear differentiation among chemistries. NMC-111 exhibits the widest 5–95% uncertainty
range due to cobalt sensitivity, whereas NMC-811 and NMC-9525 show narrower bands reflecting reduced cobalt
exposure. Bottom-up process-based models offer stronger cost traceability and better uncertainty representation
than top-down macro forecasts, which often lack explicit linkages to $/kWh outputs. Findings demonstrate that
raw-material price treatment strongly influences forecast accuracy, and that scenario-free point estimates sys-
tematically under-represent market risk.
To address these gaps, the study proposes a structured multi-source data acquisition strategy integrating public
databases, commodity-market feeds and industry publications. The review enhances transparency, establishes
reproducible comparison criteria, and quantifies price-volatility effects, offering actionable guidance for re-
searchers, analysts and policymakers aiming to improve reliability and reduce uncertainty in LIB cost assessment.