Smarter Charging Could Add Years to Your Battery’s Life

2026-06-17 · Source: IEEE Spectrum · Field: Transportation & Mobility — Electric & Alternative Fuel Vehicles, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Researchers at Chalmers University of Technology in Gothenburg, Sweden, have developed an EV charging algorithm that, in simulation, extended the useful life of a lithium-ion battery cell by nearly 23 percent compared with current industry methods. This algorithm, detailed in "Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-ion Batteries," adjusts charging behavior based on the battery's real-time state of health, easing current as the cell ages. Applicable to Level 1 and Level 2 AC charging, the method could add two to three years of useful battery life with a minimal charging-time penalty of less than three seconds per session. While currently validated only on a single simulated cell at 25 degrees Celsius, the approach could be deployed via over-the-air software updates to existing battery management systems, offering significant economic benefits for EV fleet operators.

Key takeaway

For EV fleet operators or automotive engineers evaluating battery management strategies, this research suggests that adaptive charging algorithms could significantly extend battery lifespan. Implementing health-aware charging, particularly for Level 1 and Level 2 AC charging, offers a potential 23% life extension with minimal time penalty. You should monitor the progress of this early-stage research, especially validation on real-world multi-cell packs, to inform future software update considerations for existing vehicles.

Key insights

An adaptive EV charging algorithm extends battery life by adjusting current based on real-time state of health.

Principles

• Battery aging requires adaptive charging.
• Fixed voltage ceilings accelerate decline.
• Real-time health optimizes charge.

Method

A machine-learning technique discovers optimal charging strategies through simulated cycles, taking voltage and state of health as inputs to produce a current level.

In practice

• Apply to Level 1/2 AC charging.
• Target EV fleet operators for economics.
• Implement via OTA software updates.

Topics

• EV Charging Algorithms
• Lithium-ion Batteries
• Battery Management Systems
• Reinforcement Learning
• Battery Degradation
• Over-the-Air Updates

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by IEEE Spectrum.