Battery PC

The Intelligent Revolution of Drone Batteries

With the rapid development of drone technology, batteries have evolved from simple energy supply units to “intelligent power centers” integrated with AI algorithms. The flagship models represented by DJI Mavic 3 Pro use the DJI BP03 intelligent battery to achieve a comprehensive upgrade in battery life prediction, health management and safety protection through artificial intelligence technology. This article will deeply analyze how AI empowers drone batteries and explore future technology trends.

1. AI-driven battery management system (BMS)

1. Dynamic battery life prediction algorithm

Traditional batteries only display a rough percentage of power, while the AI ​​system of Mavic 3 Pro analyzes in real time: flight attitude data (wind speed, climbing resistance), environmental parameters (temperature, altitude)

Load status (gimbal power consumption, image transmission strength)

Combined with historical data models, it can accurately predict the remaining flight time (error <3 minutes). For example: when it detects flying against the wind, the system will automatically lower the battery life display and recommend returning home.

2. Adaptive charging and discharging strategy

Intelligent charging sorting: Charging manager (Battery Hub 03) uses AI to prioritize charging the battery with the lowest power, increasing efficiency by 20%

Cycle life optimization:  AI will learn user habits and automatically maintain the battery at 50% power (the best storage state for lithium batteries) during the non-shooting season

3. Fault prediction system

By monitoring:

Battery cell voltage difference (warning when >0.1V), internal resistance change trend, abnormal charging rate

Prompt battery attenuation risk 3-5 cycles in advance to avoid power outage accidents during flight

2. Deep integration of AI and battery safety

1. 3D temperature field modeling

The battery has 5 built-in temperature sensors, and  AIconstructs a 3D thermal map:

Normal working conditions: uniform heat dissipation design

Extreme environment: automatic output power limit (such as 10% power reduction at 40°C high temperature)

3. AI-enabled user interaction experience

1. Voice assistant linkage

Through the “DJI Assistant” APP, you can achieve:

“Check battery health” → Display cycle number/maximum capacity

“Plan multi-battery charging” → Automatically calculate the optimal charging sequence

3. Smart maintenance reminder

Automatically push based on frequency of use:

“You have a battery that has been idle for more than 30 days, it is recommended to charge to 60%”

“The current cycle number has reached 150 times, it is recommended to enable the backup battery” AI fast charging optimization

Through neural network learning battery chemical characteristics, complete 0-80% charging in 15 minutes (currently takes 40 minutes).

Wireless charging collaboration

UAVs and charging piles automatically adjust coil positions (error <1mm) through AI negotiation to dynamically match transmission power (up to 500W)

Hydrogen-electric hybrid system

AI manages the coordinated energy supply of fuel cells and lithium batteries:

Cruise phase: hydrogen fuel cells are the main

Maneuvering phase: lithium batteries instantly replenish power

Conclusion: Redefine energy management

DJI has not only solved the “range anxiety” and “safety concerns” of traditional drones by deeply embedding AI into the battery system, but also created a new paradigm for intelligent energy management. With the evolution of machine learning algorithms, drone batteries in the future may have self-diagnosis and self-repair capabilities, further unleashing creative potential. For professional users, understanding these  AI features will directly improve flight safety and shooting efficiency.


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If we were to describe CATL’s development pace over the past decade as a dance, it would undoubtedly be the “Battery Dance”—focusing on power batteries, energy storage, and electrochemical systems, pushing scale, cost, and safety to the extreme.

However, with the accelerated competition in AI in China and globally by the end of 2024, CATL has also begun its AI dance, joining the faster-paced, more variable “AI Dance” that the global industry is participating in.

Humanoid robots are the most concrete and easily perceived manifestation of this shift in dance steps.

Why is CATL moving from batteries to humanoid robots? Is this simply chasing a trend, or is there a deeper industrial logic?

From our perspective, the answer is not complicated. Humanoid robots represent an application scenario that is highly aligned with CATL’s capabilities.

Batteries are the core power source for robots, and robots naturally embody the three directions that CATL is pursuing: high energy density, high safety, and system capabilities deeply coupled with AI 

Compared to new energy vehicles, which have entered a new stage of scale competition and price wars, humanoid robots offer a new space that is “not yet finalized and where standards can be jointly defined.”

The humanoid robot “Xiao Mo”

What truly made the outside world realize that CATL wasn’t just “telling stories,” but was seriously engaging in this AI dance, was the deployment of the humanoid robot “Xiao Mo” at its Zhongzhou base.

Unlike many robot applications that remain in the demonstration or conceptual stage, “Xiao Mo” directly entered CATL’s production environment, specifically the EOL and DCR processes of the power battery PACK production line.

This is a process that has long relied on manual labor, requiring operation under conditions of multiple models, small batches, and high flexibility, and also involving hundreds of volts of high-voltage connections, inherently posing safety risks and consistency issues.

 “Xiao Mo” began its trials in this process, using an end-to-end vision-language-action model, enabling it to adapt to material deviations and point changes, and autonomously adjust its posture;

 It dynamically controls the force when plugging and unplugging flexible wiring harnesses to avoid damage;

 In terms of cycle time and success rate, it has already achieved results close to or even exceeding those of skilled workers, with a connection success rate consistently above 99%.

With so many subsequent PACK production lines at CATL, the application of CATL’s humanoid robots in replacing human workers in real, high-risk, and high-consistency industrial scenarios has proven its value.

This step is crucial. CATL first integrated it into its own manufacturing system. By refining it through the production line, it created a closed loop between robot capabilities, AI models, and battery systems internally, which has more long-term value than simply investing externally or releasing concepts.

This path may not be fast, but it is sufficiently stable. Humanoid robots are already having an impact on three levels:

Firstly, they are driving the evolution of intelligent manufacturing towards greater flexibility and intelligence;

Secondly, they are providing real-world application scenarios for cutting-edge technologies such as solid-state batteries;

Thirdly, they are allowing CATL to occupy a more proactive position in the long-term narrative of the integration of AI  and energy.


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