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What is the Electric Vehicle (EV) industry about?

The EV industry is focused on the production, sales, and maintenance of electric vehicles (EVs). These are vehicles that are powered by one or more electric motors, using energy stored in rechargeable batteries or other energy storage devices. EVs are seen as a more environmentally friendly alternative to traditional internal combustion engine vehicles, as they produce fewer emissions and can be powered by renewable energy sources. The EV industry includes various players such as EV manufacturers, battery manufacturers, charging station operators, and service providers. The industry is rapidly growing, driven by factors such as government incentives, environmental regulations, and advancements in technology.

Why are EV taking over?

EVs are gaining popularity due to several factors, including:

Environmental concerns

The most significant reason for the rise of EVs is growing concerns over climate change and air pollution. EVs produce fewer emissions than traditional gasoline-powered vehicles, making them a more environmentally-friendly transportation option.

Technological advancements

The advancements in battery technology have led to longer range and faster charging times for EVs. This has made them a more practical alternative to gasoline vehicles.

Government incentives

Governments around the world are offering incentives to promote the adoption of EVs. This includes tax credits, subsidies, and rebates, which can make EVs more affordable for consumers.

Lower operating costs

EVs have lower operating costs than gasoline vehicles. They require less maintenance, have fewer parts to replace, and electricity is cheaper than gasoline.

Public awareness

As more people become aware of the benefits of EVs, the demand for these vehicles continues to rise. Many people are choosing EVs because they want to be part of a cleaner and more sustainable future.

The Evolution of EV

The past, present and future technological developments that have shaped and will continue to shape the electric vehicle (EV) industry:

History of EVs

The history of EVs dates back to the mid-19th century, when inventors were experimenting with battery-powered electric vehicles. However, it wasn’t until the 20th century that EVs gained popularity. In the 1920s and 1930s, electric vehicles were a common sight on American roads, especially for delivery trucks and taxis. However, the invention of the gasoline-powered internal combustion engine (ICE) soon made EVs less practical and cost-effective, and they gradually fell out of favor.

The 1970s energy crisis and increasing environmental concerns renewed interest in EVs, leading to the development of modern electric vehicles in the 1990s. Early electric cars, such as the General Motors EV1 and the Toyota RAV4 EV, were limited in range and performance, but they paved the way for future developments.

Recent Developments

In recent years, the EV industry has experienced significant growth and technological advancements, driven by the need to reduce greenhouse gas emissions and combat climate change. Some of the key recent developments in the EV industry include:

Battery technology improvements

Lithium-ion batteries have become the dominant battery technology used in EVs due to their high energy density, long cycle life, and low self-discharge rates. In addition, new battery chemistries, such as solid-state batteries and lithium-sulfur batteries, are being developed to further improve the range, performance, and safety of EVs.

Charging infrastructure expansion

Governments and private companies are investing in charging infrastructure to support the growth of the EV market. Fast-charging stations are becoming more common, and wireless charging technology is being developed to make charging more convenient.

Range improvements

The range of EVs has increased significantly in recent years, with some models able to travel over 400 miles on a single charge. This is due to improvements in battery technology and aerodynamics, as well as the development of more efficient electric motors.

EV models and variety

Today, there is a wide range of EV models available to consumers, from compact cars to SUVs to luxury vehicles. EV manufacturers are also exploring the use of electric drivetrains in other modes of transportation, such as buses, trucks, and even airplanes.

Autonomous driving

Autonomous driving technology is being developed for EVs, which will enable self-driving cars to navigate roads and traffic without human intervention.

Future Developments

Looking ahead, there are several emerging technologies and trends that are likely to shape the future of the EV industry. Some of these include:

Solid-state batteries

Solid-state batteries are an emerging battery technology that offers higher energy density and faster charging times than lithium-ion batteries. Companies such as Toyota, Volkswagen, and QuantumScape are investing in the development of solid-state batteries for use in EVs.

Vehicle-to-grid (V2G) technology

V2G technology enables EVs to be used as a source of energy for the grid, allowing owners to sell excess energy back to the grid during peak demand periods. This has the potential to reduce the strain on the electrical grid and promote the use of renewable energy sources.

AI and machine learning

AI and machine learning are being used to improve the performance, safety, and efficiency of EVs. This includes the use of AI for predictive maintenance, battery optimization, and autonomous driving.

3D printing

3D printing technology is being used to produce lightweight and complex parts for EVs, which can improve performance and reduce manufacturing costs.

How is the EV industry using AI?

The electric vehicle (EV) industry is using artificial intelligence (AI) in a number of ways, including:

Autonomous driving

AI is being used to develop self-driving cars, which could revolutionize the transportation industry. Autonomous vehicles rely on AI algorithms to process data from sensors, cameras, and other sources to make real-time decisions about steering, acceleration, and braking.

Predictive maintenance

EV manufacturers are using AI to monitor the health of their vehicles and predict when maintenance will be needed. This allows them to perform preventative maintenance, reducing downtime and improving overall performance.

Battery optimization

AI is being used to optimize the performance of EV batteries by analyzing data on factors such as temperature, charge levels, and usage patterns. This can help extend the life of batteries and improve their efficiency.

Supply chain management

EV manufacturers are using AI to manage their supply chains more efficiently, predicting demand, optimizing inventory levels, and identifying potential disruptions.

Customer service

AI-powered chatbots are being used by EV manufacturers to provide customer service and support. These chatbots can handle routine inquiries and provide quick responses to customer questions and concerns.

AI is playing an increasingly important role in the EV industry, helping manufacturers improve the performance and reliability of their vehicles, while also improving the customer experience.

How is AI changing autonomous driving?

Artificial intelligence (AI) is a key technology that is driving advancements in autonomous driving. Here are some ways AI is changing autonomous driving:

Object Detection and Recognition

AI algorithms can detect and recognize objects in the environment, including other vehicles, pedestrians, and road signs. This enables autonomous vehicles to make decisions and adjust their behavior based on their surroundings.

Decision Making

AI can help autonomous vehicles make decisions based on their perception of the environment, traffic conditions, and the vehicle’s own capabilities. For example, AI algorithms can decide when to accelerate, decelerate, or change lanes to ensure safe and efficient driving.

Path Planning and Navigation

AI algorithms can plan the most efficient path for an autonomous vehicle to reach its destination, taking into account traffic conditions, road hazards, and other factors. This helps ensure that the vehicle reaches its destination safely and on time.

Predictive Maintenance

AI algorithms can monitor the health of a vehicle’s systems and predict when maintenance is needed. This can help prevent breakdowns and reduce maintenance costs.

Data Analysis

Autonomous vehicles generate vast amounts of data, including sensor data, GPS data, and other telemetry. AI algorithms can analyze this data to improve the performance of autonomous driving systems and enable continuous improvement over time.

AI is transforming the autonomous driving industry by enabling vehicles to make intelligent decisions based on their surroundings and their own capabilities, and by enabling continuous improvement through data analysis.

What is the timeline for autonomous driving?

The timeline for fully autonomous driving is still uncertain, as it involves the development of complex technology and the resolution of numerous legal and regulatory issues. However, most experts predict that we will see increasingly advanced autonomous driving systems in the coming years, with widespread adoption of fully autonomous vehicles perhaps still several years away. Some manufacturers and technology companies have announced plans to launch self-driving cars in the near future, but these are often limited to specific areas or use cases. Ultimately, the timeline for autonomous driving will depend on a variety of factors, including technological progress, regulatory developments, and consumer acceptance.

What needs to change so your EV can drive you to the Airport?

There are several technological, regulatory, and societal changes that need to occur to accelerate the possibility of autonomous driving.

Improved Sensors and Software

Autonomous driving requires high precision sensors and software that can accurately perceive and interpret the surrounding environment, including other vehicles, pedestrians, and traffic signs. Continued advancements in lidar, radar, and computer vision technologies are necessary to improve the accuracy and reliability of these systems.

Regulatory Frameworks

As autonomous driving technology continues to evolve, regulatory frameworks need to keep pace to ensure safety on the roads. Governments around the world are developing guidelines and regulations that address the testing and deployment of autonomous vehicles, but there is still a need for standardized regulations that apply across different regions and countries.


Autonomous vehicles require a robust infrastructure that supports high-speed data transfer, including 5G networks, to enable real-time communication between vehicles and infrastructure components. This includes everything from smart traffic lights to improved road design and maintenance.

Public Acceptance

Widespread adoption of autonomous driving technology is dependent on public acceptance. As people become more familiar with the technology and experience its benefits, such as improved safety and reduced congestion, public acceptance is likely to increase.

Collaboration and Innovation

Continued collaboration and innovation among automakers, technology companies, and other stakeholders will be key to driving progress in autonomous driving technology. Open standards and shared knowledge can accelerate progress and enable new innovations that can benefit the industry as a whole.

How is AI used in Predictive Maintainance?

AI is being increasingly used in the predictive maintenance of EVs to identify and address potential issues before they occur. Predictive maintenance involves using data from sensors and other sources to monitor the performance and condition of a vehicle’s components and systems in real-time. AI algorithms can then analyze this data to detect patterns and anomalies, predict when a component is likely to fail or require maintenance, and generate alerts for maintenance personnel.

Some examples of how AI is used in predictive maintenance of EVs include:

Predicting battery degradation

AI can analyze battery usage patterns and environmental factors to predict how a battery is likely to degrade over time. This information can be used to optimize battery usage and maintenance.

Predicting mechanical failures

AI algorithms can analyze data from various vehicle sensors to detect patterns that may indicate a mechanical failure is imminent. This information can be used to schedule preventive maintenance before a major failure occurs.

Optimizing maintenance schedules

By analyzing data from a large number of vehicles, AI algorithms can identify patterns that help optimize maintenance schedules for specific components or systems.

Overall, the use of AI in predictive maintenance can help improve the reliability and safety of EVs while reducing maintenance costs and downtime.

How is AI used in Battery Optimization?

AI is used extensively in battery optimization. With the help of AI, it is possible to predict the remaining useful life of a battery accurately. This is done by analyzing the data obtained from various sources such as the battery’s internal sensors, its operating conditions, and the environment in which it is used. By analyzing this data, AI algorithms can predict when a battery is likely to fail and optimize its usage accordingly.

AI is also used to optimize battery charging and discharging. For example, AI algorithms can analyze the energy usage patterns of a particular vehicle or device and optimize the battery usage to ensure maximum efficiency. This can help extend the life of the battery and reduce the overall energy consumption.

In addition to this, AI is also used to optimize the design of batteries. By analyzing the performance of different battery chemistries and designs, AI algorithms can identify the most efficient and cost-effective options. This can help manufacturers create better batteries that offer improved performance and longer life spans.

Overall, AI is playing a critical role in battery optimization, enabling manufacturers to create more efficient and sustainable batteries while improving their performance and lifespan.

How is AI used in the supply chain management process of EV industry?

AI is increasingly being used in the supply chain management process of the EV industry to improve efficiency and reduce costs. Here are some examples:

Demand forecasting

AI can help predict demand for EVs and related components, such as batteries, and help manufacturers adjust production accordingly. By analyzing data on sales, customer preferences, and economic trends, AI algorithms can forecast demand more accurately than traditional methods.

Inventory management

AI can help manage inventory by predicting which parts and components are likely to be in demand and when. This can help reduce waste and prevent stockouts or overstocking.

Supplier selection

AI can help select the best suppliers based on factors such as quality, price, and delivery time. By analyzing data on supplier performance and comparing it to industry benchmarks, AI algorithms can identify the most reliable and cost-effective suppliers.

Logistics optimization

AI can help optimize logistics by analyzing data on shipping routes, traffic patterns, and other factors that affect delivery times and costs. By identifying the most efficient routes and modes of transportation, AI algorithms can reduce delivery times and costs.

Risk management

AI can help manage risks in the supply chain by identifying potential disruptions, such as natural disasters or supplier bankruptcies, and developing contingency plans. By analyzing data on past disruptions and their causes, AI algorithms can predict future risks and help companies prepare accordingly.

How is AI used in customer service of EV companies?

AI is being used in various ways to improve customer service in the EV industry. Here are some examples:


Many EV companies are using AI-powered chatbots to provide quick and efficient customer support. These chatbots are able to understand customer inquiries and respond with helpful answers, often without the need for human intervention.


AI can be used to analyze customer data and provide personalized recommendations to customers. For example, an EV company could use AI to analyze a customer’s driving patterns and suggest the most suitable EV model for their needs.

Voice assistants

Some EV companies are using voice assistants like Amazon’s Alexa or Google Assistant to provide hands-free access to customer support. Customers can ask questions or report issues while driving, without needing to take their hands off the wheel.

Sentiment analysis

AI can be used to analyze customer feedback and sentiment on social media and other online platforms. This information can be used to identify areas where customer service can be improved and to develop targeted solutions.

Overall, AI is helping EV companies to provide faster, more efficient, and more personalized customer service.

Which fields are most important for EV companies?

EV companies rely on a wide range of fields to design, manufacture, and market their products. Some of the most important fields include:

Electrical engineering

This is the foundation of the EV industry, and encompasses the design and development of the electric motors, battery systems, and power electronics that are critical to the operation of electric vehicles.

Mechanical engineering

This field is crucial to the design and manufacture of the physical components of electric vehicles, such as the chassis, suspension, and braking systems.

Materials science

Advances in materials science are critical to improving the efficiency and performance of electric vehicles. This includes the development of new materials for batteries, as well as lightweight, durable materials for vehicle components.

Software engineering

As electric vehicles become more complex and sophisticated, software engineering is becoming increasingly important. This includes the development of software for the vehicle control systems, as well as for the various sensors and communication systems that are integral to the operation of modern electric vehicles.

Data science

The EV industry generates vast amounts of data, which can be used to optimize vehicle performance, improve safety, and enhance the customer experience. Data science is critical to making sense of this data and extracting insights that can inform decision-making.

Marketing and sales

Like any industry, the EV industry relies on effective marketing and sales strategies to promote its products and grow its customer base. This includes everything from advertising and brand management to dealer networks and customer support.

How AI electrifies Electrical Engineering

AI is helping in electrical engineering in various ways. Here are some examples:

Design optimization

AI algorithms can analyze vast amounts of data and identify patterns that can help optimize the design of electrical systems. For example, AI can help optimize the placement of components in a circuit board to minimize interference and improve performance.

Fault detection and diagnosis

AI algorithms can be used to monitor the performance of electrical systems and detect any faults or anomalies. This can help identify issues before they become serious problems and improve system reliability.

Predictive maintenance

By analyzing sensor data from electrical systems, AI algorithms can predict when components are likely to fail and schedule maintenance before a failure occurs. This can help reduce downtime and improve system availability.

Energy management

AI can help optimize energy usage in electrical systems by analyzing data from sensors and adjusting system settings in real-time. This can help reduce energy waste and improve system efficiency.


AI can be used to automate repetitive or complex tasks in electrical engineering, such as circuit design or testing. This can help improve productivity and reduce errors.

Overall, AI is enabling electrical engineers to work more efficiently and effectively by providing new tools and techniques for design, monitoring, and maintenance of electrical systems.

How AI affects mechanical engineering?

AI is affecting mechanical engineering in several ways. Here are some examples:

Design optimization

AI can help mechanical engineers optimize the design of parts and systems, by analyzing large amounts of data and identifying the best design parameters for a given set of requirements. This can help engineers create more efficient, lightweight, and cost-effective designs.

Predictive maintenance

AI can help mechanical engineers monitor and maintain equipment by analyzing sensor data and predicting when maintenance will be required. This can help reduce downtime, improve reliability, and extend the life of equipment.

Robotics and automation

AI is increasingly being used in robotics and automation systems, which are essential to modern manufacturing and production processes. Mechanical engineers are involved in designing, building, and programming these systems, and AI can help optimize their performance and improve their accuracy and efficiency.

Quality control

AI can help mechanical engineers improve quality control by analyzing data from sensors and cameras to detect defects and anomalies in parts and systems. This can help identify problems early on and prevent costly recalls or repairs.

AI is helping mechanical engineers work more efficiently, make better decisions, and create more innovative and effective designs.

How is AI affecting the materials science of EV companies?

AI is having a significant impact on the materials science of EV companies. Materials science is the study of the properties of materials and how they can be modified or optimized for specific applications. In the EV industry, materials science plays a critical role in the development of high-performance batteries, lightweight and durable vehicle components, and efficient charging infrastructure.

AI is being used in materials science in several ways:

Predictive modeling

AI algorithms can analyze large amounts of data to identify patterns and predict how materials will behave in different conditions. This can help researchers design materials that are stronger, more durable, and more efficient.

Materials discovery

AI can help identify new materials with specific properties that are needed for EVs. By analyzing vast amounts of data, AI can help identify materials that are both efficient and cost-effective.

Manufacturing optimization

AI can optimize the manufacturing process for EV components by predicting how different materials will behave during the manufacturing process. This can help reduce waste, improve quality, and increase efficiency.

Supply chain optimization

AI can help optimize the supply chain for materials used in EV manufacturing. By analyzing data on the availability, cost, and quality of materials, AI can help companies make informed decisions about sourcing and procurement.

AI is helping EV companies accelerate the development of new materials and improve the efficiency and sustainability of their manufacturing processes.

How is AI affecting Software Engineering?

AI is having a significant impact on software engineering in several ways. Here are a few examples:


AI-powered tools are being used to automate testing processes, making them faster and more accurate. These tools use machine learning algorithms to identify patterns and detect anomalies in code, helping developers find and fix bugs more quickly.

Code generation

AI can also be used to generate code automatically, based on high-level specifications. This can save developers a significant amount of time and effort, as well as reduce the likelihood of errors.


AI can be used to optimize software performance, by analyzing data from sensors and other sources to identify areas where performance can be improved. For example, AI algorithms can be used to optimize the energy consumption of software running on an EV’s battery.


AI can also be used to improve the maintenance of software systems, by predicting when maintenance will be needed and alerting developers to potential issues before they become critical.

AI is changing the way software is developed and maintained, making it faster, more efficient, and more reliable.

How is AI used in data science for EV companies?

AI is used in data science for EV companies to extract insights from large and complex datasets that are generated during the manufacturing, testing, and operation of EVs. Some examples of how AI is used in data science for EV companies include:

Predictive maintenance

AI is used to analyze sensor data from EV components and systems to identify potential issues before they occur. This helps prevent costly breakdowns and reduces maintenance costs.

Quality control

AI is used to analyze production data to identify patterns and anomalies that may indicate a defect or quality issue. This helps improve the overall quality of EVs and reduces the risk of recalls.

Performance optimization

AI is used to analyze data from EV sensors and components to optimize performance, such as battery life, energy efficiency, and driving range. This helps EV companies to design and build more efficient and effective vehicles.

Autonomous driving

AI is used to process sensor data from autonomous driving systems to detect and respond to changing road and traffic conditions. This helps improve the safety and reliability of autonomous vehicles.

Customer insights

AI is used to analyze data from customer feedback, surveys, and social media to understand customer preferences and needs. This helps EV companies to develop more customer-centric products and services.

How is AI used in marketing and sales of EV companies?

AI is being used in various ways to enhance the marketing and sales efforts of EV companies. Here are some examples:

Personalized Marketing

AI algorithms can analyze customer data to understand their preferences and create personalized marketing campaigns. This can help EV companies target the right customers with the right message, improving the chances of a successful sale.

Sales Forecasting

By analyzing data on customer behavior, market trends, and other factors, AI can help EV companies predict sales volumes and adjust their marketing strategies accordingly.


AI-powered chatbots can provide customer support around the clock, answering frequently asked questions and resolving simple issues. This can improve customer satisfaction and reduce the workload for human customer service representatives.

Predictive Maintenance

By analyzing data from sensors and other sources, AI can predict when a vehicle or its components are likely to fail. This information can be used to proactively schedule maintenance appointments and prevent breakdowns, improving the overall customer experience.

Pricing Optimization

AI can help EV companies optimize their pricing strategies based on factors such as market demand, competitor prices, and customer behavior. This can help companies maximize their revenue while still remaining competitive in the market.

AI can help EV companies improve their marketing and sales efforts by providing insights into customer behaviour and market trends, as well as automating certain tasks to improve efficiency and reduce costs.

How can AI optimize the energy management of an EV?

AI can optimize the energy management of an EV by analyzing data from various sources, such as the vehicle’s battery, sensors, and the environment, to make real-time decisions about how to allocate energy resources. Here are a few ways AI can optimize energy management in EVs:

Predictive Energy Management

AI algorithms can analyze data from sensors and vehicle systems to predict energy usage and plan for optimal use of the battery, maximizing range and efficiency.

Adaptive Energy Management

AI can adjust energy usage in real-time based on changing road and weather conditions, driving style, and other factors to optimize range and efficiency.

Smart Charging

AI can manage the charging process to avoid peak electricity rates and ensure that the battery is charged when energy is cheapest and most available.

Energy Harvesting

AI can identify opportunities for energy harvesting, such as regenerative braking or solar panels, to capture and store energy that would otherwise be lost.

Personalized Energy Management

AI can analyze driver behavior and preferences to optimize energy management for each individual driver, improving range and efficiency while maintaining a comfortable driving experience.

AI can help EVs operate more efficiently, reduce range anxiety, and improve the driving experience for consumers.

Which different approaches for autonomous driving are being developed or tested?

Several approaches for autonomous driving are being developed or tested, including:

Vision-based approach

This approach relies on cameras and sensors to detect and recognize objects, traffic signs, road markings, and other vehicles on the road. It uses computer vision algorithms to process the images and make decisions based on the perceived environment.

Sensor-based approach

This approach uses a combination of sensors, including radar, lidar, and ultrasonic sensors, to detect objects around the vehicle. The data collected by these sensors is processed by AI algorithms to make decisions about the vehicle’s movements.

Map-based approach

This approach uses high-definition maps to provide the vehicle with a detailed understanding of the road network. The vehicle’s sensors are used to identify its location on the map and make decisions based on the road layout and other features.

V2X communication approach

This approach relies on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to exchange information about the road environment. This information can be used to make decisions about the vehicle’s movements and improve safety.

Rule-based approach

This approach uses a set of predefined rules and heuristics to control the vehicle’s movements. For example, the vehicle may be programmed to stop at a red light or yield to pedestrians.

Hybrid approach

This approach combines multiple techniques to provide a more robust and reliable autonomous driving system. For example, a vehicle may use a sensor-based approach for detecting obstacles and a map-based approach for route planning.

Which EV company has the best AI?

It’s difficult to say which EV company has the “best” AI, as each company likely has its own unique approach and areas of focus when it comes to AI. However, some EV companies that are known for their AI capabilities include Tesla, which has been developing its Autopilot system for several years and has a strong focus on AI and machine learning; and Waymo, which is a subsidiary of Alphabet (Google’s parent company) that is developing self-driving technology using AI and has been testing its autonomous vehicles on public roads for several years. Other EV companies such as Rivian, Lucid Motors, and Nio are also investing in AI and autonomous driving technology, although it remains to be seen how their systems will compare to those of Tesla and Waymo.

The 30 biggest EV companies

The biggest electric car companies in the world based on their market capitalization as of March 2023:

  1. Tesla
  2. BYD
  3. NIO
  4. Xpeng
  5. Li Auto
  6. Rivian
  7. Lucid Motors
  8. Fisker Inc.
  9. Canoo
  10. Faraday Future
  11. Proterra
  12. Arrival
  13. Workhorse Group
  14. Lordstown Motors
  15. Nikola Corporation
  16. Bollinger Motors
  17. FAW Group
  18. SAIC Motor
  19. Geely Automobile
  20. General Motors
  21. Ford Motor Company
  22. Volkswagen Group
  23. Daimler AG
  24. BMW Group
  25. Hyundai Motor Group
  26. Kia Motors
  27. Toyota Motor Corporation
  28. Honda Motor Company
  29. Mitsubishi Motors Corporation
  30. Nissan Motor Corporation

Please note that the ranking can change depending on market fluctuations and company performance.


In conclusion, the current state of the EV industry is one of rapid growth and innovation. With increasing concerns about climate change and the push for renewable energy sources, electric vehicles have become a popular alternative to traditional gasoline-powered cars. The advancements in technology, particularly in the fields of battery storage and AI, have greatly improved the efficiency and reliability of EVs.

The future of the EV industry looks promising, with more and more companies investing in the development and production of electric vehicles. With the continued advancements in battery technology and the growing capabilities of AI, we can expect to see more efficient, longer-lasting, and affordable EVs in the near future. Additionally, the development of autonomous driving technology and the integration of renewable energy sources into EV charging stations will further enhance the sustainability and convenience of electric vehicles.

However, there are still challenges that need to be addressed, such as the lack of charging infrastructure in certain areas and the high cost of production. Nonetheless, with the continued efforts of governments, corporations, and consumers towards a cleaner and more sustainable future, the future of the EV industry looks bright.


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