EV Day: How Can AI/ML Help Drive Green Mobility In India?

India's electric vehicle market is experiencing rapid growth, with projections indicating a rise from $8.03 billion in 2023 to $117.78 billion by 2032. This represents a compound annual growth rate of 22.4%. The market is driven by an increasing demand for EVs across various segments, including two-wheelers, three-wheelers and commercial vehicles.

However, there are some roadblocks ahead. The success of the EV industry in India hinges on overcoming challenges such as limited charging infrastructure, high upfront costs, consumer anxiety over vehicle range and quality, and supply chain vulnerability. While investments in public and home charging infrastructure are critical, enhancing consumer confidence through improved technology and reliability, and reducing costs are equally important for more to get on board an EV.

This is where artificial intelligence and machine learning can come into play. From intelligent energy management systems and smart charging infrastructure to predictive maintenance and optimised routing, there are numerous use cases of AI and ML in the EV ecosystem that have the potential to propel green mobility in the country.

The nascent charging infrastructure in India poses the most significant barrier to EV adoption, and this is where technology can be of critical help. AI-powered smart charging stations can provide valuable insights on charging patterns, grid demand and user behaviour. This information can be used to optimise the deployment and location of charging stations, ensuring maximum utilisation by and accessibility for EV owners.

AI/ML algorithms can power smart charging stations that can—quite literally—communicate with EVs, allowing for dynamic charging protocols based on factors such as battery health, charging rates and grid conditions. These algorithms can dynamically adjust the charging current and voltage based on the battery's state of charge, ensuring efficient and safe charging, and can also help reduce the waiting time for EV owners and improve the overall experience.

Additionally, by predicting the demand for charging based on historical data and real-time information, these technologies can optimise the distribution of power among stations, preventing overloads and ensuring a stable grid. This is particularly important in India, where the power grid is often under strain and needs to be managed efficiently.

Range anxiety is one of the main concerns for EV owners, especially in a country like India with limited charging infrastructure. By continuously analysing data on driving conditions, usage patterns and battery health, AI/ML technologies can predict the range of an EV with high accuracy. This can enable owners to plan their journeys accordingly. By factoring in variables such as traffic conditions, charging station availability and battery range, these technologies can further suggest the most efficient routes for EV owners.

AI/ML algorithms also have the potential to optimise energy consumption of EVs by adjusting factors such as speed, acceleration and regenerative braking. By finding the optimal balance between energy efficiency and driving performance, these technologies can help maximise EV range.

By tracking a range of parameters and sensor-derived inputs, AI can play a role in monitoring and maintaining battery health. AI/ML technologies can also optimise charging and discharging cycles, prolonging the battery's lifespan and improving its efficiency. These algorithms can predict battery performance, enabling EV owners to have a better understanding of their battery's health and increasing confidence in their vehicle.

By leveraging these technologies, EV makers and original equipment manufacturers are also developing advanced battery management systems that optimise the performance, longevity and reliability of EV batteries. This includes not just increasing the primary life of batteries, but also putting their valuable minerals to maximum use before being transferred to secondary life or recycling.

EV companies and OEMs are increasingly turning to AI/ML to improve the performance, reliability and safety of their vehicles. These technologies are being utilised in quality control during the manufacturing process.

By deploying computer vision systems equipped with AI algorithms, companies and OEMs can detect defects or anomalies in real-time, ensuring that only high-quality components are used in the production of EVs.

AI/ML can play a crucial role in predictive maintenance and fault detection for EVs. Analyses of real-time data from EVs can also help detect anomalies and patterns that indicate potential mechanical issues and predict faults or failures before they occur.

Predictive maintenance enables proactive repairs and maintenance, minimising downtime and reducing repair costs. Detecting and resolving issues at an early stage can increase the reliability and longevity of EVs, instilling confidence in consumers and promoting the adoption of EVs.

The high upfront costs of EVs are a significant barrier to its adoption. AI/ML technologies are being applied at various stages of the manufacturing process, from design and development to production and quality control, to optimise processes, improve efficiency and minimise wastage. This can lead to cost savings for manufacturers, which can be passed to the end consumer.

Manufacturers can, for example, identify the most optimal design configurations and materials for EV components. They can also optimise their production lines to ensure efficient utilisation of resources, minimise idle time and reduce production bottlenecks. EV manufacturers and service providers can further optimise their maintenance schedules, reduce costs and enhance customer satisfaction through the use of these technologies.

By analysing historical data, these technologies can predict demand patterns and help manufacturers optimise inventory levels, reducing the risk of stockouts or excess inventory. Additionally, AI algorithms can identify potential disruptions in the supply chain and suggest alternative sourcing options.

This is particularly crucial in the Indian EV ecosystem. Despite growth in local manufacturing, the country's EV sector still faces resource constraints. This results in continued reliance on imports for crucial components like batteries and motors, making the supply chain vulnerable to uncertainties and rising costs.

While AI and ML offer immense potential, there are hurdles that need to be overcome. These technologies rely on large amounts of data to train and improve their performance. However, in the context of the EV ecosystem in India, obtaining high-quality data can be challenging. Another obstacle is the integration of these technologies into the nascent EV infrastructure. Retrofitting existing EVs and charging stations with AI and ML capabilities can be a complex and costly process.

Despite these challenges, the future prospects of AI and ML in the EV ecosystem in India are promising. As technology advances and costs decrease, its integration into EVs and charging infrastructure is likely to become more widespread. AI and ML will allow for continuous improvement and innovation in EV technology, making green mobility a more attractive and viable option for consumers going forward.