By Akhil Aryan
The global electric vehicle (EV) market may reach a staggering $567,299.8 million by 2025, growing at a CAGR of 22.3% from 2018 to 2025. The EV industry is observing a deliberate effort being directed towards sustainable existence as governments are championing clean mobility. At present, EVs, homes, large solar/wind micro-grids run on lithium-ion batteries. These batteries enjoy the highest energy densities amongst all contemporary battery technologies, have a comparatively lower self- discharge, and demand low maintenance. But they have a finite shelf life that could be conditioned by usage, charging patterns and the environment in which they function.
The monetary feasibility of EVs depends invariably on battery life and performance particularly with the onset of shared mobility. A Li-ion battery is one of the most critical components in an EV that makes up for 40% of the total modern EV cost. Irregularities surrounding the shelf life and performance (i.e. charging time, safety, etc) need more than just local safety and protection. On an average, the life of a Li-ion battery is up to three years, or 500-700 charge cycles, post which they require replacement. The expected life is largely undetermined and loosely based on sporadic reports and estimates made by most companies in silos. A homogeneous understanding is still lacking. Since batteries are the costliest component in an EV, it is essential to optimise battery life. The key to enhancing battery life lies in battery data. As with every system, the three Es that impact battery life are:
Entity: It includes every configuration intrinsic to the battery pack, namely capacity planning, battery chemistry comparisons, cell selection, mechanical design, thermal management, etc.
Environment: It includes factors that are external to the battery and remain beyond the control of the end-user. Factors such as weather, traffic and terrain impact battery life significantly, so it’s pertinent to take them into consideration while attempting to assess the battery’s state of health and foretelling the trajectory.
Experience: It involves everything that relies on the end-user’s profile. Variable factors such as acceleration profile, charging patterns (slow/fast), braking patterns, etc, can impact battery life by up to 30%. These days, advanced battery intelligence platforms by measuring usage patterns and providing feedback seek to customise the performance and life for each deployed asset.
State-of-the-art advanced battery intelligence platform for EVs and energy storage systems (ESS) can improve the life of li-ion batteries by up to 40%. These include:
1. By leveraging battery data with advanced electronics and data science along with machine learning and simulation technologies such as the digital twin to outline patterns, anticipate life degradation, deliver prognostic alerts and send over-the-air updates to prolong battery life.
2. The digital twin imitates a battery’s behaviour and performance for accurate data extraction. It helps in visually depicting complex health and performance insights of a battery.
3. AI and data analytics can help comprehend battery data to derive valuable insights, predict, diagnose and improve the battery’s performance in real-time, ensure zero downtime, and reduce the overall ownership cost, helping OEMs and battery pack makers maximise RoI.
4. AI and data analytics can also help companies choose the right chemistry and plan capacity, resulting in reduced costs and a greater RoI through all the stages of the battery lifecycle.
5. Automakers and battery pack manufacturers can deliver better user experience with remote service, support and extended warranties, and build smarter batteries for the all-electric future.
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