HYBRID THERMAL MANAGEMENT OF LITHIUM-ION BATTERIES USING PHASE CHANGE MATERIALS AND INTELLIGENT CONTROL

Abstract

Lithium-ion batteries are widely employed in electric vehicles, renewable energy storage systems, portable electronics, and highperformance energy applications because of their high energy density, long cycle life, low selfdischarge, and favorable power characteristics. However, battery performance, safety, degradation, and service life are strongly influenced by temperature. Excessive heat generation during high-rate charging, rapid discharging, aggressive driving, elevated ambient temperature, and repeated cycling can produce non-uniform temperature distribution, accelerated aging, capacity loss, internal resistance growth, and increased thermal runaway risk. Conventional air cooling and liquid cooling systems can remove battery heat, but they may introduce parasitic energy consumption, mechanical complexity, pumping requirements, additional weight, and control limitations. Passive phase change material systems can absorb transient heat through latent thermal storage, but their effectiveness can decrease when the material becomes fully melted or when thermal conductivity is insufficient. This paper proposes a hybrid thermal management framework for lithium-ion batteries using phase change materials and intelligent control. The proposed system integrates a phase change material layer, thermally conductive enhancement structures, distributed temperature sensing, battery current and voltage monitoring, ambient-condition assessment, active cooling components, real-time data acquisition, intelligent state classification, predictive temperature analysis, and adaptive cooling control. The framework uses machine learning models including Random Forest, Support Vector Machine, Gradient Boosting, Artificial Neural Network, and Long Short-Term Memory networks to identify thermal states, predict temperature growth, and determine appropriate cooling intensity. The phase change material absorbs transient heat passively, while the intelligent controller activates fans, pumps, or auxiliary cooling only when predicted thermal risk requires additional intervention. A representative prototype-oriented evaluation indicates that the hybrid strategy can reduce peak cell temperature, improve module temperature uniformity, decrease active cooling energy, and respond earlier to developing thermal stress compared with passive-only and fixed-control cooling approaches. The architecture also incorporates secure data interfaces, lifecycle monitoring, energy-aware computation, and scalable digital analytics. The study demonstrates that combining passive latent heat absorption with intelligent predictive control provides a promising pathway toward safer, more efficient, and adaptive lithium-ion battery thermal management.