Artificial Intelligence

How AI and Data Analytics Will Help in Predicting Battery Life and its Extension

The EV uptake suggests a significant transition in battery manufacturing volumes and increased investment in battery technology
How AI and Data Analytics Will Help in Predicting Battery Life and its Extension
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Co-founder & CEO of ION Energy
4 min read
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In its next big breakthrough, Artificial Intelligence (AI) is set to disrupt the battery technology space, by combining the power of predictive intelligence and data analytics to achieve high battery efficiency and operational reliability. OEMs, battery pack manufacturers, electric fleet managers, and Electric Vehicle (EV) makers will leverage AI, data science and machine learning to remarkably improve the battery’s performance & acquire better ROI through all stages of the battery life cycle.

With major development and conscious efforts being directed towards sustainable living and governments pushing for clean mobility, the global EV market was valued is projected to reach $567,299.8 million by 2025, growing at a CAGR of 22.3% from 2018 to 2025.  

The EV uptake suggests a significant transition in battery manufacturing volumes and increased investment in battery technology, which is imperative as EVs are expensive and the cost of the battery amounts to 40% of the total vehicle cost. Lithium-ion batteries, which power high-energy solutions like EVs, homes & large solar/wind micro-grids, have one of the highest energy densities of any battery technology, a relatively low self-discharge, & requires low maintenance. On the other hand, they have a limited life that could be affected by usage, charging patterns and the environment in which they operate, etc. In the long run, it is imperative to consider, assess and critically analyze all the factors that affect battery life: 

  • Excessive Charging or Discharging - To extend the battery life, it is important to operate in mid-State of Charge (SoC) of 30–80% and prevent ultra-fast charging and full cycles by applying some charge, after a full discharge

  • High Temperatures - Avoid high temperatures and limit deep cycling, lower voltage limit preferred

  • Unused Batteries - Batteries must not be left unused for an extended period of time, in EV or in storage. Keep tab of the battery’s charge status 

  • Replace battery - Under two conditions the battery should be replaced -
    1. When the run time drops below 80% of the original run time
    2. When the battery charge time increases significantly

The market size of Lithium-ion-based battery type is anticipated to reach $12.23 billion by 2025 and is projected to witness a high CAGR of 24.2%. On average, the life of a lithium-ion battery is up to 3 years or 500-700 charge cycles, after which, they need to be replaced. Today, the expected life of the battery is largely unknown and based on assumptions made by most companies on the on-road battery life and performance, which is a critical concern area. 

The key to unlocking the mystery of battery life lies in the data. The underlying, core potential of battery data, when leveraged with ML, data analytics and digital twin capabilities, can help accurately determine, predict and exceptionally improve battery life. It will help ensure cost optimizations and zero downtime and essentially accelerate the transition of businesses to an all-electric future. 

By applying battery domain knowledge to these technologies, businesses can: 

  • Optimize their fleet of batteries using data - When sufficient data is logged, collected and analyzed, it becomes possible to predict battery life, deploy faster, improve uptime and improve the life of the batteries, making a tremendous impact on the business.

  • Access real-time visualization - The digital twin of the battery takes in data from the application and environment to accurately estimate the residual life at scale and speed for each battery in the fleet, monitor real-time performance, identify issues, allowing a business to know the current state and take appropriate actions with confidence

  • Get Recommendations - Based on the battery’s current usage,  data science, and ML can accurately predict the trajectory, suggest corrective measures and recommendations, help set predictive alerts, and send over-the-air updates, thereby preventing abnormal degradation. This enables businesses to reduce replacement costs, reuse batteries and process warranties with accuracy

  • Reduce Ownership Cost - The battery data simulations help improve the deployment speed, uptime, and battery life, thereby reducing the overall ownership cost.

Businesses that leverage descriptive, diagnostic, predictive and prescriptive analytics will be able to significantly and continuously improve battery life. They will gain an edge against competition and make the most of the emerging opportunities in this space.

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