Researchers Have Introduced The TD3-Powered Charger
Edited by Adam Harrie — May 21, 2026 — Tech
This article was written with the assistance of AI.
References: digitaltrends
Researchers at Victoria University of Wellington and Chalmers University of Technology developed a TD3-Powered Charger using a deep reinforcement learning algorithm that adapts safe voltage levels during charging based on a battery's current degradation state.The system adapts charging behavior based on a battery’s degradation state, dynamically adjusting safe voltage levels during charging rather than relying on fixed routines.
In simulation tests using a real-world battery model, the AI-driven method extended battery cycle life by roughly 23%, reaching around 703 equivalent full cycles compared with 572 under conventional charging approaches, while still achieving an 80% charge in about 24 minutes. The researchers also noted that the system was trained using consumer-grade computing hardware instead of specialized high-performance infrastructure.
For EV drivers and fleet operators, adaptive charging systems could reduce battery wear while preserving fast-charging convenience, lowering replacement costs and improving long-term vehicle efficiency. The research also reflects a broader trend toward AI-managed energy systems that optimize performance in real time based on equipment condition and usage patterns.
Image Credit: Shutterstock/Adwo
In simulation tests using a real-world battery model, the AI-driven method extended battery cycle life by roughly 23%, reaching around 703 equivalent full cycles compared with 572 under conventional charging approaches, while still achieving an 80% charge in about 24 minutes. The researchers also noted that the system was trained using consumer-grade computing hardware instead of specialized high-performance infrastructure.
For EV drivers and fleet operators, adaptive charging systems could reduce battery wear while preserving fast-charging convenience, lowering replacement costs and improving long-term vehicle efficiency. The research also reflects a broader trend toward AI-managed energy systems that optimize performance in real time based on equipment condition and usage patterns.
Image Credit: Shutterstock/Adwo
Would you use battery-health-aware fast charging?
Helps decide what EV charging features to cover and what products/services readers might adopt or pay for.
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When was the last time you used a public fast charger?
2 / 3
If you fast-charged, how willing are you to let the charger adjust speed to reduce wear?
3 / 3
Which fast-charging option would you be most likely to choose?
Trend Themes
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Adaptive Battery Management — Demonstrates how charging protocols that adjust to real-time degradation states can materially extend cycle life while maintaining fast-charge times.
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Reinforcement-learning Charging — Points to the use of TD3 and similar deep RL methods to discover charging policies that balance longevity and charge speed beyond fixed-rule approaches.
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Consumer-grade AI Training — Shows that sophisticated battery-optimization models can be developed using commodity hardware, lowering barriers to AI-driven energy management.
Industry Implications
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Electric Vehicle Fleets — Lowered battery degradation for fleets suggests sizable reductions in total cost of ownership and vehicle downtime through smarter charging regimens.
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Charging Infrastructure Providers — Adaptive chargers that negotiate safe voltage profiles indicate opportunities for differentiated fast-charging services that protect customer batteries.
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Battery Manufacturing and Recycling — Extended usable cycle life shifts material demand dynamics and creates incentives for designs and recycling processes optimized around longer-lived cells.
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