Experimental and Numerical Explorations of Fire Performance of Intumescent Thermoplastic Composites for Electric Vehicle Battery Enclosures

Abstract

The global shift toward renewable energy has accelerated the adoption of electric vehicles (EVs) powered by lithium-ion (Li-ion) batteries. While these batteries offer high energy density and efficiency, their inherent fire risk remains a critical safety concern. To mitigate this hazard, plastic-intensive, flame-retardant enclosures have been engineered as promising alternatives to traditional metal-intensive systems for EV battery pack protection. This dissertation presents a first-of-its-kind experimental and numerical investigation into the fire performance of three newly developed injection-molded, flame-retarded polypropylene (FR-PP) thermoplastic composites, each reinforced with 30% discontinuous glass fibers with differing fiber length and FR content. These systems are candidates for next-generation plastic EV battery enclosures. Understanding the fire performance of Li-ion battery systems under fire conditions has traditionally relied on full-scale pool-fire testing, as prescribed by standards such as GB/T 38031-2020 and ECE R100. While indispensable for certification and regulatory compliance, full-scale fire tests are costly, logistically challenging, and carry significant safety risks, particularly due to potential battery explosions. To navigate these challenges, this study adopts a more agile, modular approach. Instead of testing the entire system, a representative smaller-scale experimental approach was implemented to replicate pool-fire thermomechanical loading on horizontally oriented plate-scale composite specimens in a safer and more controlled manner. One-sided fire exposure generated using a sand burner reproduced pool-fire-like conditions, while steel cylinders were configured to represent mechanical loads of Li-ion battery weight and compartments. This scaled-down framework removes hazards while enabling precise, repeatable experiments and allows examination of the influences of material formulation (fiber length and level of flame-retardant content), geometric parameters (plate thickness), fire intensity (fire source size), and mechanical loading (flexural load configuration) on the fire performance of composite specimens. Through these experiments, the study identified configurations that achieved the greatest fire endurance, with the top-performing candidate selected as a benchmark for computational validation. The key characterizations of the intumescent, decomposing thermoplastic systems are thickness expansion and viscoelasticity. The evolution of thickness expansion and softening behavior across the full thermal spectrum and material states, from intact nondecomposed regions at room temperature, through condensed phases at decomposition onset, to porous layers infused with gases during decomposition, and finally to fully degraded charred systems, was captured through comprehensive property characterizations and material measurements. Advanced constitutive models derived from these characterizations were incorporated into the computational framework. The primary contribution is the development of a coupled thermomechanical, physicsbased, and experimentally driven finite element framework that simulates the composite plates under replicated pool-fire loading and the weight of the battery and its compartments. This framework is validated via fire experimental testing while integrating the key characterizations of the intumescent, decomposing thermoplastic composite plate, as well as all composite plate–steel cylinder and system-environment interactions. The integrated experimental-computational platform reliably predicts temperature distributions and out-of-plane deflections under combined thermal and flexural stresses, providing a scalable foundation for plate-to-module and full-scale simulations. Collectively, these results establish a robust foundation for the safe and effective design of composite battery enclosures under extreme fire scenarios, with computational predictions validated against observed experimental behavior.