State of charge tells you how full a battery is right now. State of health tells you how much the battery has permanently aged — how much of its original capacity is gone for good. The second is harder, because aging does not announce itself in any single voltage or current reading. It is the slow, cumulative result of every cycle, temperature excursion and charge habit the cell has ever experienced.That path-dependence is why machine learning has become the tool of choice. The 2025 grants are thick with it. Eatron Technologies holds US12270860B2 on state-of-health assessment in rechargeable batteries and US12299549B1 on an AI decision engine to extend battery lifespan. Element Energy's US12228613B1 claims state-of-health prediction modeling. Sungkyunkwan University's US12241942B2 claims a method for estimating a battery's aging state. Four assignees, one approach: learn the aging pattern from data.The mechanism is to train a model on how cells degrade under different use, then have it infer a given cell's health from the subtle fingerprints aging leaves — how the voltage curve flattens, how the internal resistance creeps up, how the cell responds to a known load. No single measurement reveals health; the pattern across many does, and that pattern-recognition is what ML is good at.The economic payoff is large because health drives the most expensive decisions in a battery's life. When to retire a pack, whether a used EV battery is worth a second life in grid storage, how to warranty a fleet — all hinge on knowing true state of health. A good estimator can extend a pack's useful life by managing it gently as it ages, or unlock resale value by certifying remaining capacity. Both move real money.The caution batteryfolio always raises with machine learning: a model is only as good as the data it learned from, and a cell aging in a way the training data never saw can fool it. The honest products treat ML health estimates as informed estimates with error bars, not oracle truth. The CPC codes here — G01R 31/392 for aging-state determination alongside G06N machine-learning classes — mark exactly this fusion of electrochemistry and data science.For readers, the signal is whether a battery product's health estimation is grounded in real cycling data and validated, or whether 'AI' is a marketing veneer over a crude model. The 2025 IP shows the serious players building genuine ML pipelines for aging — the unglamorous infrastructure that decides when a multi-thousand-dollar pack is done.
“In one aspect, based on at least a received first state of health of a battery pack and an initial state of charge of the battery pack, a method may include determining, by a state of charge estimator of a digital twin battery model, states of charges for the battery pack.”— U.S. Patent No. 12,270,860 source