Category: news

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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Is your phone battery draining too quickly? Is your tablet suddenly unable to turn on? Are you worried about how much your old phone is worth when you trade it in? These questions trouble many people. Now, with Lenovo’s AI-powered Helper, you can get intelligent responses and precise solutions throughout the entire process.

Is your phone battery always draining too quickly?AI  Smart Check can help.

Many users find that their newly purchased phones have batteries that drain quickly, worrying if there’s a problem. But they don’t know how to check it themselves or how to read “battery health.”

Open the AI ​​Smart Check function in Lenovo’s AI-powered Helper. With just one click, it automatically checks the device’s status, including battery health, whether the power strategy is reasonable, and whether there are any abnormal power consumption issues. It’s not just a “health check,” but it also provides targeted optimization suggestions, such as closing high-power background programs and adjusting performance scheduling modes.

【How to check your phone’s battery health?】

→ Use Lenovo’s AI-powered Helper’s AI Smart Check to perform a “battery health check.”

Operation Steps:

1. Open the Lenovo ThinkHelp app and click on AI Smart Check in the scene;

2. Enter “Check Battery Health,” and Lenovo ThinkHelp will perform a comprehensive device check;

3. After completion, Lenovo ThinkHelp will display the test results. Click “View Full Report” to see more detailed data and usage suggestions.

How to Trade in Your Phone? AI  Smart Trade-in Tells You the Answer

Is it worth continuing to use your old phone, or is it more cost-effective to trade it in now? This is a dilemma for many users.

With the AI ​​Smart Trade-in function, Lenovo ThinkHelp’s AI service intelligence can intelligently assess the value of your old phone based on multi-dimensional data such as device configuration, status, and usage time, and recommend suitable new models and trade-in plans. It not only lets you know “how much it’s worth,” but also provides cost-effective trade-in solutions.

【How to Trade in Your Phone?】

→ Open the AI ​​Smart Trade-in function of Lenovo ThinkHelp’s AI service intelligence to experience one-stop trade-in service.

Operation Steps:

1. Open the Lenovo ThinkHelp app and click “Trade-in” at the bottom;

2. Click “Estimate Now.” Lenovo ThinkHelp will automatically identify the device information and provide an intelligent estimate based on the device’s condition, helping users get the best value for their trade-in.

Why can’t my tablet or phone turn on? AI  Repair can help!

Sometimes users encounter the problem of their devices suddenly turning off and unable to turn on. Buttons are unresponsive, the charging light is off, and they can’t even access the system interface.

The Lenovo ThinkHelp AI Service’s AI Repair function can easily solve these “can’t turn on” problems. Even if the main device is inoperable, users can still submit fault information and schedule repair services across devices using other Lenovo phones or tablets. The entire process is convenient and efficient.

【Where can I get my tablet repaired when it suddenly turns off?】

→ Use Lenovo ThinkHelp AI Service’s AI Repair function to report repairs across devices.

Operation Steps:

1. On another device, open the Lenovo Help App, log in to your account, switch to the device that won’t turn on under “Service,” and click “Service Appointment” at the bottom;

2. On the service appointment page, select the device malfunction description;

3. Fill in the pickup information and wait for pickup.

Frequently Asked Questions!

【How to Check Your Phone’s Battery Health】

→ Open the Lenovo Help AI Service Assistant and use the “AI  Smart Check” function to check the battery status and health with one click, and receive personalized optimization suggestions.

【How to Trade in Your Phone】

→ Use the “AI  Smart Trade-in” function to assess the value of your old device. Combine this with Lenovo’s trade-in program to easily get an accurate valuation and recommendations for new models.

【What to Do If Your Tablet Suddenly Goes Black and Won’t Turn On】

→ Even if the main device won’t start, you can quickly schedule repair service through the “AI Smart Repair” function on other Lenovo devices.

How to check your phone’s battery health? How to trade in your phone? What to do if your tablet suddenly goes black and won’t turn on…? These common device problems can all be solved quickly and intelligently through Lenovo’s Helpful AI Service Agent. The Helpful AI Service Agent is an intelligent agent in the 3C service field. Like a full-process intelligent butler, it uses AI technology to connect users with services, comprehensively improving the user experience. If you’re also troubled by phone battery health, phone trade-in programs, or your tablet won’t turn on, open the Lenovo Helpful AI Service Agent now and get a fantastic one-click solution.

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Google’s next-generation large-scale language model, Gemini 3.0, is about to be released, with frequent leaks from company employees and CEO Sundar Pichai explicitly stating that the model will launch before the end of this year.

IT Home notes that AI enthusiasts on social media platform X (formerly Twitter) and multiple Discord communities are reaching fever pitch; some “AI truth-seekers” even firmly believe that Gemini 3 has already been quietly launched. Given Google’s tradition of semi-secretly testing new models, such speculation is not entirely unfounded.

It’s not just ordinary users who are eagerly awaiting this; the entire AI  industry is holding its breath, waiting for Google to reveal its trump card. The industry generally expects Gemini 3 to achieve significant leaps in code generation and multimodal content creation; particularly noteworthy is the rumored integration of an upgraded version of “Nano Banana,” a new generation of Google’s previously wildly popular image generation tool.

Since the emergence of ChatGPT in late 2022, Google has been widely perceived as a slow-moving industry giant struggling to catch up. At the time, this assessment was not without merit: facing its most severe survival challenges in years, Google quickly restructured its team, focusing on integrating generative AI into its core product matrix.

Now, this once-dormant tech behemoth has awakened: Gemini’s user base is rapidly expanding; the AI revolution has so far not shaken its core profit pillar, advertising; and calls for Pichai’s resignation have largely subsided.

To achieve this catch-up, Google fully leverages its unique “full-stack” advantage, not only developing large-scale models in-house but also achieving efficient distribution through its vast product ecosystem and building a robust infrastructure using Google Cloud. This has allowed Google to remain outside the increasingly complex “mutual aid network” of the current AI  industry and avoid the widespread concerns about a growing bubble.

Meanwhile, a huge opportunity lies before Google: OpenAI’s highly anticipated ChatGPT 5 has received a lukewarm reception, falling far short of expectations as a “blockbuster.” Is this a sign that the AI ​​industry is entering a “plateau,” or does it mean OpenAI’s edge is waning?

According to multiple insiders speaking to Business Insider, the new model’s performance is “extremely stunning.” If Gemini 3 truly becomes a phenomenon, Google could potentially reclaim the industry leadership it has coveted since the start of the generative AI wave.

For OpenAI, this undoubtedly poses a serious challenge: it lacks Google’s full-stack integration capabilities, and its previous lead was primarily built on “first-mover advantage” and extensive industry alliances.

Google still needs to address a key challenge: currently, “ChatGPT” has become the default synonym for AI technology in the public eye; its cognitive position in the chatbot field is comparable to that of “Google” in web search.

The user base also differs significantly: Google claims Gemini has 650 million monthly active users, while OpenAI’s ChatGPT boasts approximately 800 million weekly active users. Although Gemini’s popularity among younger users continues to rise, bridging the gap remains a long and arduous task.

It’s worth noting that Google’s strategic investments over the years in cloud computing, self-developed chips, and top talent are now bearing fruit. If Gemini 3 truly makes a splash, all Google needs to do is capitalize on this opportunity.

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Artificial Intelligence and Experimental Validation Reveal the Atomic-Scale Basis for Improving the Performance of “Water-in-Salt” Batteries

Upton, New York—A team of scientists from Brookhaven National Laboratory and Stony Brook University have used artificial intelligence (AI) to gain insights into how zinc-ion batteries work and explore ways to improve their efficiency to meet future energy storage needs. Their findings, published in the journal *PRX Energy*, focus on the water-based electrolyte responsible for transporting charged zinc ions during charging and use. The AI ​​model analyzed how these charged ions interact with water in solutions of zinc chloride (ZnCl₂, a water-soluble salt) at different concentrations.

The AI ​​findings were experimentally validated at the National Synchrotron Radiation Facility II (NSLS-II) at Brookhaven National Laboratory, demonstrating why high salt concentrations produce optimal battery performance.

“Artificial intelligence is a crucial tool for advancing scientific progress,” said Esther Takeuchi, Director of the Division of Interdisciplinary Sciences at Brookhaven National Laboratory and William and Jane Knapp Professor of Energy and Environment at Stony Brook University. “This team’s findings demonstrate the profound insights that can be gained by combining experimentation and theory with artificial intelligence.”

Amy Marschilok, Professor of Chemistry at Stony Brook University (SBU) and Manager of the Energy Storage Division at ISD, added, “This research helps advance the development of robust zinc-ion batteries for large-scale energy storage. These batteries are particularly attractive for applications requiring highly reliable energy because their water-based electrolytes are inherently safe, and the materials used to manufacture them are abundant and inexpensive.”

Deryu Lu, a scientist in the Theoretical and Computational Group at the Center for Functional Nanomaterials (CFN) at Brookhaven National Laboratory who led the research, explained that zinc-ion batteries, like all batteries, convert the energy generated by chemical reactions into electrical energy.

“However, competing chemical reactions, such as the splitting of water molecules to produce hydrogen, can severely degrade battery performance,” he said. “If this energy is used for side reactions, then the energy that should have been used for work is lost.”

Lu and his collaborators knew that previous research had found that the water-breaking reaction was inhibited in a special zinc chloride electrolyte. This electrolyte had a very high salt concentration and was called a “water-in-salt” electrolyte, in contrast to the more common “salt-in-water” electrolyte. To investigate why the high-salt electrolyte was superior, they wanted to capture the atomic-scale details of how zinc and chloride ions move and interact with water at different salt concentrations, and how this interaction affects the electrolyte’s conductivity.

However, observing these atomic-scale details is extremely difficult. Therefore, the research team turned to a computer modeling method enhanced by artificial intelligence vision.

Developing AI Vision

Professor Lu stated, “These complex details cannot be observed using traditional computing techniques. Traditional simulation methods cannot handle the large number of atomic interactions with the required precision, thus failing to capture the timescale of evolution in such systems. Such calculations require enormous computing power and can easily take years.”

Therefore, instead of performing all the complex calculations required to simulate ion-water interactions, the research team used traditional simulation methods to generate a small amount of simulated data (called a “training set”) and fed it into the AI ​​program. They utilized computing resources at the Theory and Computation Facility (CFN) (a user facility of the U.S. Department of Energy’s Office of Science) and the Scientific Computing and Data Facility (CDS) within Brookhaven National Laboratory’s Division of Computation and Data Science (CDS).

“We need a small amount of data, collecting data by computing a small number of interactions to initiate the initial model training process,” said Cao Chuntian of CDS, the paper’s first author. “Then, we run the model to generate more data, continuously improving the model’s predictive capabilities.”

At each step, the scientists fed the results into a set of machine learning (ML) models to evaluate the accuracy of the predictions. Lu likened the process to calling several friends and asking them to help answer questions from the once-popular TV game show “Who Wants to Be a Millionaire?”. “If the friends/models all agree, then your prediction is likely accurate,” he noted.

But as Cao pointed out, “When we find that some predictions in the machine learning model ensemble are significantly biased, we re-perform traditional calculations to get the correct answers. Then, we add these new corrected data points back into the training data to further improve the machine learning model.”

This iterative “active learning” process minimizes the high computational demands required to train the machine learning model. After several rounds of training, the AI ​​model is able to predict interactions between a larger number of atoms over longer timescales.

“Springfield performed simulations for hundreds of nanoseconds on a massive system of thousands of atoms—a task that would have been impossible using traditional methods. AI/machine learning has truly transformed the landscape of complex materials research,” Lu said.

Stabilizing Water AI models developed by scientists at Brookhaven National Laboratory and Stony Brook University show that high concentrations of zinc chloride play a crucial role in stabilizing water molecules and preventing their fragmentation.

Professor Lu explained that in pure water, the oxygen atom in a water molecule (H₂O) forms two hydrogen bonds with the hydrogen atom in an adjacent water molecule. These hydrogen bonds connect water molecules into a continuous network, making them more reactive and easier to break down.

The research team found that as the concentration of zinc chloride increases, the number of hydrogen bonds decreases rapidly, disrupting the hydrogen bond network. In the water-in-salt system, only about 20% of the hydrogen bonds remain.

Cao said, “Stabilizing water molecules is the key factor behind the remarkable effect of high-concentration water-in-salt electrolytes.”Browse the great range of batteries & chargers at Batterypcs.co.uk in UK.Low prices, big inventory, expert advice. Find your battery here!”

The decade from 2025 to 2035 will be a crucial period for artificial intelligence to deeply empower battery technology and fundamentally transform its R&D paradigm and application scenarios. The intervention of AI will drive innovation across the entire battery technology chain, from material discovery and system design to management and maintenance.

The following is a summary of the core trends in AI-driven battery technology development over the next decade: AI is transforming battery R&D from an experience-based “trial and error” approach to a data-driven “automated design.”

From “Trial and Error” to “Design”: Traditional battery R&D cycles are lengthy and heavily reliant on experimental trial and error. The emergence of Battery Design Automation (BDA), similar to EDA software in the chip industry, integrates multi-scale physical models with artificial intelligence algorithms to build an automated R&D platform from atomic-level material design to system-level performance prediction. This can significantly shorten the R&D cycle of next-generation batteries from several years.

AI Accelerates Material Innovation: Generative AI can reverse engineer novel battery materials that meet specific performance targets (such as high conductivity and high stability) within a vast chemical space. For example, the Uni-Electrolyte platform can utilize generative AI to design novel electrolyte molecules and predict their synthetic pathways. The Fudan University team used AI high-throughput computing to improve material screening efficiency by a hundredfold.

Accurate Performance Simulation and Prediction: Through algorithms such as Physical Information Neural Networks (PINN), AI can accurately and efficiently solve complex multiphysics problems within batteries, achieving precise predictions of battery state of health (SOH). This provides a key tool for optimizing battery design and extending battery life.

Deep Integration Across the Industry Chain: AI’s empowerment of battery technology will permeate the entire industry chain, from laboratory innovation to large-scale applications.

Intelligent Manufacturing and Quality Control: AI-powered large-scale models can automatically call upon and optimize production process parameters. Through digital twin technology based on physical models and quantum computing, material interface reactions can be predicted in virtual space, and a defect detection system can be built, thereby significantly improving production yield and reducing experimental trial-and-error costs by more than 90%.

Intelligent Battery Management System (BMS): Equipping batteries with a “digital brain” is the most direct manifestation of AI at the application level. For example, the “Battery Digital Brain” developed by the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, can achieve day-level advanced fault warnings through AI algorithms, far exceeding the minute-level level of traditional systems, greatly improving the safety and operation and maintenance efficiency of energy storage power stations.

Empowering the Next Generation Battery System: All-solid-state batteries are hailed as the ultimate next-generation battery technology, but their industrialization faces many challenges, such as interface impedance. AI, through high-throughput computing and knowledge graphs, can quickly analyze decades of accumulated literature and patents, providing a new path to overcome key challenges such as solid-solid interfaces and sulfide electrolyte stability, accelerating its commercialization. It is expected that all-solid-state batteries will enter the vehicle testing phase in 2027.

Future Applications and Emerging Opportunities: The combination of AI and battery technology will spawn and drive the development of a series of cutting-edge technology industries.

Opening Up a New Market Worth Hundreds of Billions: Due to size limitations and the extreme pursuit of energy density, AI consumer terminals (such as eVTOL electric vertical take-off and landing aircraft and humanoid robots) will become the testing ground for the commercialization of solid-state batteries. eVTOL requires all-solid-state batteries with an energy density ≥400Wh/kg. The low-altitude economic battery market is projected to reach 150-200 billion yuan by 2030.

Building the Energy Foundation for the AI ​​Revolution: Data centers providing computing power for AI are huge energy hogs. Large-scale battery energy storage systems, especially long-term energy storage solutions using non-lithium technologies (such as zinc batteries), can serve as reliable power buffers for data centers, helping them “skip” the long wait for grid upgrades and enter operation years earlier. These companies are positioning themselves as key solvers of AI energy bottlenecks.

Driving the Intelligent Upgrade of Energy Storage Systems: Future energy storage systems will develop around core technologies such as large-capacity cells, liquid cooling thermal management, and AI-driven intelligent management. AI will optimize the operating strategies of energy storage systems and promote the widespread adoption of grid-forming control technologies, thereby enhancing grid stability and security.

Challenges and Key Insights: Looking ahead, opportunities and challenges coexist. Data quality, the accuracy of cross-scale model fusion, and the robustness of AI-specific algorithms remain scientific issues requiring continuous research and development. However, what is certain is that the deep integration of AIand battery technology is irreversible. It is driving the global battery industry to shift from relying on “manufacturing advantages” to relying on “R&D and innovation advantages,” which will reshape the future competitive landscape of energy technologies.

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DeepSeek’s AI has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. Chinese AI startup DeepSeek has released an update to its V3 model that promises better programming capabilities.
The V3-0324 update, which was initially announced on Hugging Face this week but has not yet been officially released, claims to address real-world challenges while setting benchmarks for accuracy and efficiency. V3 is actually an older DeepSeek platform, and DeepSeek claims to have significant improvements in benchmark performance across multiple metrics.
It also claims to have improved the style and content quality of Chinese writing features, improved multi-round interactive rewrites, optimized translation quality and letter writing, enhanced reporting analysis requests and output more detailed output, and improved the accuracy of function calls, fixing issues in previous V3 versions.
The startup’s AI service has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. It also highlights the company’s intention to stay ahead of its competitors, especially those from Silicon Valley, such as OpenAI and Google.
Previously, DeepSeek surpassed OpenAI’s ChatGPT to become the most popular free app in Apple’s US App Store.
DeepSeek’s achievements also include the performance of the initial R1 model, which seems to be on par with OpenAI’s best model, but at a fraction of the cost. The cost part was particularly shocking to the industry and triggered a sell-off in  AI and technology-related stocks in the US market. This is because the best companies in Silicon Valley have invested huge amounts of money in their artificial intelligence projects, but have only achieved similar results


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Battery energy storage is essential to improving the reliability of renewable energy. It can collect extra energy from solar and wind power to provide electricity when needed. However, artificial intelligence (AI) is taking battery management to the next level.

Experts say AI software is now key to managing large battery systems. Companies are applying AI not only to basic tasks but also to energy trading, safety monitoring and predictive maintenance.

Advanced AI technologies enhance battery energy storage

Battery systems use intelligent tools such as machine learning, deep learning, predictive analytics and reinforcement learning. They are becoming a key tool for managing large battery systems.

By combining these technologies,  AI ensures that:

Batteries store and release energy efficiently based on demand.

Optimize performance by processing real-time data to reduce waste and improve efficiency.

These upgrades provide a stable and reliable power source, making battery energy storage more viable and cost-effective.

S&P Global says the market demand for battery energy storage systems is growing. However, the integration of  AI  is just getting started. Lithium-ion battery energy storage developers are well positioned to meet this demand.

Henrique Ribeiro, lead analyst for batteries and energy storage at S&P Global Commodity Insights, said:

“As market competition intensifies and more capacity is deployed, maximizing returns may become increasingly difficult. Therefore, such tools may become an advantage.”

Improving battery energy storage safety

Scientists, researchers and experts agree that manufacturing high-quality batteries is technically complex and challenging. As lithium-ion battery production grows, especially in China and the United States,  AI analysis will become increasingly important.

A major challenge is the rapid pace of innovation. If manufacturing errors go unnoticed, they can cause serious problems. Thermal runaway is one of them, which can cause dangerous fires. However, AI can help detect problems early and avoid costly failures.

Like other energy storage systems, batteries also have safety risks. This is a cause for concern. However, this challenge also provides an opportunity for the industry to improve safety measures.

Organizations such as the Industrial Electrotechnical Committee (IEC) and UL Solutions are raising safety standards. Therefore, effectively managing these risks is essential to maintain industry momentum.

As energy storage technology develops,  AI will help optimize operations and ensure grid reliability and sustainability.

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Purpose-built for the enterprise, helping you confidently develop, deploy, and secure AI applications.

News Highlights:

Cisco’s end-to-end solution secures the development and use of AIapplications, enabling enterprises to confidently advance their AI initiatives.

AI Defense protects against AI misuse, data breaches, and increasingly sophisticated threats that existing security solutions can’t address.

This innovative solution leverages Cisco’s unmatched network visibility and control to help you meet the evolving challenges of AI security.

San Jose, California, January 15, 2025—Cisco (NASDAQ: CSCO), a leader in networking and security, today announced Cisco AI Defense, a breakthrough solution designed to support and protect enterprises’ AI transformation. As AI continues to advance, new security challenges and threats are emerging at an unprecedented rate, and existing security solutions are no longer able to address them. Cisco AI Defense is designed to help enterprises confidently develop, deploy, and secure AI applications.

Jeetu Patel, Executive Vice President and Chief Product Officer at Cisco, said, “As business and technology leaders embrace artificial intelligence, they cannot sacrifice security for speed. In a highly competitive and rapidly changing environment, speed determines success or failure.” Cisco AI Defense Integrated into the Network: “We have integrated unique capabilities into our framework to detect and block threats during the development and access ofAIapplications, preventing them from causing harm.”

With AI, the stakes are extremely high that things won’t go as planned. According to Cisco’s 2024 AI Readiness Index, only 29% of respondents believe they are fully capable of detecting and preventing unauthorized AI modifications. Due to the multi-model and multi-cloud nature of AI applications, security challenges are both novel and complex. Vulnerabilities can arise at the model or application level, with responsibility borne by different owners, including developers, end users, and vendors. As organizations move beyond public data and begin training models with proprietary data, the risks will only increase.

To unleash the potential of AI innovation and application, organizations need a universal security layer to protect every user and every application.AI Defense empowers enterprise AI transformation by addressing two pressing risks:

Developing and deploying secure AI applications: As AI becomes more widespread, enterprises will use and develop hundreds, even thousands, of AI applications. Developers need a comprehensive set of AI security measures for each application. AIDefense helps developers scale faster and create more value by protecting AI systems from attacks and ensuring cross-platform model behavior. AI Defense capabilities include:

Understanding AI: Security teams need to understand who builds applications and the training sources they use. AI Defense detects malicious and regulated AI applications across public and private clouds.

Model validation: Model adjustments can lead to adverse consequences. Automated testing verifies AI models for hundreds of potential security issues. This AI-powered algorithmic red team identifies potential vulnerabilities and recommends safeguards that security teams can implement to prevent AI attacks.

Runtime security: Continuous validation provides ongoing protection against potential security threats such as edge injection, denial of service, and sensitive data leakage. Securing access to AI applications: As end users adopt AI applications, such as summarization tools, to improve productivity, security teams must protect against data breaches and data poisoning. AI Defense provides security teams with the following capabilities:

Visibility: Gain comprehensive visibility into shadow AIapplications and approved AI applications used by employees.

Access Control: Enforce policies that limit employee access rights.


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An unexpected, potentially historic, collaboration may be brewing between two of the world’s largest tech companies. According to Bloomberg, Apple is in advanced talks to integrate Google’s Gemini artificial intelligence model to power a major upgrade of its long-running but recently criticized voice assistant, Siri. This rumored partnership isn’t the first between the two companies, but it may be a much-needed strategic move for Apple to catch up with the likes of Perplexity, OpenAI, and Google in the field of artificial intelligence.

For years, Siri has lagged behind competitors like Google Assistant and Amazon Alexa in its ability to handle complex, open-ended questions. Therefore, this partnership will enable Siri to leverage Gemini’s large language model to provide richer, more conversational, and contextually aware answers. While Apple currently uses its own Apple Foundation Models for personal and privacy-sensitive requests, this partnership will allow Siri to route more complex questions requiring answers from web information to a version of Google Gemini. Gemini will reportedly run on Apple’s private cloud computing servers to maintain a high level of privacy. Cupertino had previously held talks with other AI companies, including OpenAI and Anthropic, but partnering with Google made strategic sense given their existing partnership. In fact, Apple and Google’s partnership dates back to the early days of the iPhone, when the device defaulted to integrating key Google services, including Google Search, Google Maps, and even the YouTube app. For years, Google Search was the default search engine in Apple’s Safari browser, a lucrative partnership reportedly generating billions of dollars in revenue for Apple annually.

Although the two companies became fierce competitors after Google launched Android, their strategic partnership has remained. Even as Apple develops its own competing services, such as Apple Maps, it still relies on Google to maintain its search dominance, a partnership recently affirmed by a significant court ruling.

Of course, another winner from this partnership is the end user. The launch of “Apple Intelligence” (the company’s take on Gemini), originally scheduled for 2025, has been delayed until sometime in 2026, meaning consumers are already impatient for Apple to take action.

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