The article discusses the application of a systematic approach to the development and optimization of lithium-ion batteries (LIBs). Traditional methods that focus on improving individual components (anode, cathode, and electrolyte) often do not lead to a proportional increase in the overall performance of the battery system. The systematic approach views LIBs as a complex, interconnected system where the properties of each component directly influence the behavior of others and the overall performance, including energy and power density, life cycle, safety, and cost. The work analyzes the key aspects of the approach: the interdependence between the main components of a lithium-ion battery, as well as the features of selecting materials for each component. It is proven that only a multidisciplinary approach that combines chemistry, materials science, and engineering can achieve a synergistic effect and create highly efficient, safe, and reliable battery systems for modern applications.

Keywords: lithium-ion battery, system approach, electrode materials, degradation, optimization, cathode, LTO, NMC

The rapid electrification of transport and energy systems imposes extreme and often conflicting requirements on the performance of lithium-ion batteries. The classical paradigm of step-by-step optimization of individual components (materials and designs) has reached its limits, facing the challenge of negative synergistic effects. Despite the availability of advanced methods, ranging from detailed physical and chemical models to machine learning algorithms, the field of energy storage system design remains fragmented. This article provides a critical analysis of three isolated domains: the empirical-synthetic approach, physical and mathematical modeling, and software methods. Systemic shortcomings have been identified, including the lack of end-to-end methodologies, the "black box" problem of ML solutions, extreme requirements for data and computational resources, and limited portability of solutions. The concept of a hybrid predictive platform is proposed, which purposefully integrates fast regression models for deterministic parameters and specialized neural networks for predicting complex nonlinear degradation processes. This integration allows for the consideration of a battery cell as a single entity, optimizing the trade-offs between key characteristics (capacity, power, lifespan, and safety) during the virtual design phase, resulting in reduced time and cost.

Keywords: energy storage systems, system approach, electrode materials, optimization, system design, machine learning, hybrid models, degradation prediction, and performance optimization