By developing high crystallinity limited sintering technology and precisely controlling particle size, we improve material dynamics in multiple dimensions. This leads to a significant enhancement of both high gravimetric capacity and high volumetric performance.

By using microstructure characterization technology to choose precursors with high specific capacity potential, along with particle surface defect repair and particle size grading technologies, we achieve the objective of balancing the capacity and compaction of the negative electrode material.

By conducting mechanism research and utilizing electrode sheet simulation technology, we can achieve optimal material combinations that align with project objectives. This enables us to design battery performance and implement processes in the most efficient way possible, ultimately enhancing the cell's maximum energy density.

By designing the link between precursor and cathode material sintering processes, optimizing particle secondary structure and coating effects, we improve the stability of particles and interfaces. Additionally, incorporating active lithium technology ensures a longer battery lifespan.

By utilizing powder coating and surface vapor deposition technology, we construct a controllable and multifunctional artificial solid electrolyte interface (SEI) in small quantities. We conduct qualitative research to examine the impact of the coating process, amount of coating, and choice of coating materials on cell performance. Ultimately, we quantify the cause-effect relationship between these factors and cell performance.

By utilizing high-throughput computation and AI-designed electrolyte formulation, the solvation structure of lithium ions can be selectively adjusted to optimize the dynamics of lithium ion transport. This leads to the creation of a stable and highly ion-conductive SEI film, enabling long-lasting cycling performance under different operating conditions.

By integrating high crystallinity limited sintering technology, composite coating technology, and fast ion channel interface design, we aim to enhance the low-temperature dynamics of materials.

By adopting surface modification and bulk phase control technology for the negative electrode, we can improve material dynamics and optimize high-temperature durability. This will comprehensively enhance performance in both high and low temperatures.

A high-precision battery thermal model has been developed, pioneering the industry's internal battery temperature estimation technology. This technology estimates the internal temperature of the battery using surface and terminal temperatures collected from it.

Independent electrolyte additives and formulation design aim to enhance the thermal reaction of battery cells, improve the heat resistance of the electrolyte itself, and achieve precise control of the solid-liquid interface for various negative electrode materials.

Based on data and models, a separator compatibility library is developed for various battery cell application scenarios, to ensure optimal thermal resistance and interface properties with electrode plates, thereby enhancing battery safety.

By utilizing AI technology and battery models, we can calculate the real-time internal short circuit current of each battery cell. This enables us to promptly identify the internal short circuit status of each cell and prevent thermal runaway.

Based on the experimental databases of critical temperatures (T1/T2/T3) for battery cell thermal runaway, this technology compares the real-time temperature curve of the battery pack with fitted data. If key features match, it generates warnings for thermal runaway events

After the generation of thermal runaway warning information, the battery pack initiates the injection of flame retardant medium to disperse residual air oxygen inside the battery pack, ensuring that high-temperature leakage from battery cells does not result in open flames.

Sampling the electrical architecture while it is not powered off, integrating the sampling module with battery cells, monitoring early-stage anomalies of thermal runaway in real-time, and enabling the main control module to be awakened by safety events when it is in sleep mode.

By utilizing innovative pressure relief technology, the battery safely discharges high-temperature and high-pressure leakage components through the pressure relief valve without generating open flames, effectively eliminating any associated risks.

Circuit breakers protect against short-term excessive currents, fuses protect against system short-circuit failures, and relays ensure normal power off when the system is de-energized.

For different usage scenarios, dynamic adaptive algorithms are used to estimate the open circuit voltage (OCV) of the battery in real-time, ensuring that the SOC error throughout the battery's lifecycle is controlled within ±3%.

By utilizing adaptive algorithms that consider various factors including capacity, impedance, consistency, and self-discharge rate, the battery's health status (SOH) is estimated to promptly identify retired batteries. This ensures safety and enhances performance.

The industry's first dynamic adaptive power estimation technology is used to estimate the maximum charge and discharge power of the battery in real-time, fully utilizing the battery's capabilities, and enhancing battery safety and lifespan.

By leveraging AI, big data, and cloud computing technologies, we can overcome the limitations of storage and computing resources in terminal BMS. This allows for accurate lifespan prediction and timely safety warnings.