The stakes could hardly be higher. Lithium-ion batteries have become the backbone of modern electrification, powering everything from smartphones and wearables to frequency-regulating grid farms that stabilize intermittent solar and wind output. Global production is racing toward multi-terawatt-hour capacity as nations pursue carbon neutrality. But every cell carries an internal clock. Capacity fade, growth of the solid electrolyte interphase that consumes active lithium, metallic lithium plating during fast charging or cold operation, micro-cracking of electrodes under mechanical stress, rising internal resistance, thermal stress, and cell-to-cell inconsistency all conspire to shorten a battery’s useful life. In the worst case, localized overheating can trigger thermal runaway—a fire or explosion risk that no manufacturer can afford to ignore.

Traditional monitoring methods, the review explains, struggle to keep pace with this complexity. Capacity-based estimation requires full charge-discharge cycles that vehicles rarely complete in the real world. Electrochemical impedance spectroscopy detects aging sensitively but is costly and largely an offline laboratory technique. Equivalent circuit models run fast but sacrifice physical fidelity, while full electrochemical models are accurate yet computationally punishing. Kalman filter observers track states online but falter when the underlying model drifts from reality. Under the non-linear, multifactorial, and time-varying conditions typical of contemporary lithium-ion packs, these classical approaches often miss hidden degradation dynamics entirely—precisely the dynamics that matter most for safety and longevity.

Enter deep learning. Machine learning frameworks can learn intricate relationships between voltage, current, temperature, impedance, and cycle behavior without requiring explicit physical simplifications. For SOC estimation—the measure of a battery’s remaining charge relative to its rated capacity—the review highlights gated recurrent unit (GRU) encoder-decoder networks equipped with dual-stage attention, which map sequences of voltage, current, and temperature readings onto charge states while taming the vanishing-gradient problems that plague conventional recurrent networks. Time-delayed recurrent neural networks add another twist, using delayed hidden-state feedback to capture electrochemical hysteresis and battery memory effects that simpler models gloss over.

The most compelling SOC results come from optimization-augmented hybrids. A genetic algorithm tunes the architecture of a GRU network—searching across one to three layers and 32 to 256 neurons—using mean squared error as its fitness function, yielding faster training and greater robustness under dynamic operating conditions. In comparative tests across standard drive cycles including US06, FUDS, BJDST, HWFET, and UDDS, the standout performers were CNN-BWGRU and Bi-LSTM models, which posted root mean square errors as low as 0.00659 and mean absolute errors down to 0.0017, with coefficients of determination exceeding 0.999. Those numbers translate to charge tracking accurate to fractions of a percent—precision that directly extends driving range confidence and pack safety.

State of health estimation, which tracks how far a battery’s maximum usable capacity has fallen from its factory-fresh value toward the roughly 80 percent end-of-life threshold, has spawned an equally creative arsenal. Physics-informed neural networks embed electrochemical constraints—such as the monotonic relationship between incremental capacity features and SOH—directly into the training loss, forcing the network to learn degradation physics alongside data patterns. Graph convolutional networks take a radically different approach: each charge-discharge cycle becomes a node in a graph, with discharge voltage profile similarities defining the edges, allowing the model to trace how degradation propagates from cycle to cycle.

Transformer architectures, borrowed from the language-modeling revolution, have proven especially potent here. A regression vision transformer slices one-dimensional battery cycling features into patches and processes them in parallel, capturing long-range temporal dependencies that recurrent models miss. The review’s comparative analysis found that attention-enhanced hybrids dominate the SOH leaderboard: an artificial bee colony-optimized bidirectional GRU achieved an R² of 0.9973, while a CNN-Transformer combination posted the lowest mean absolute error at 0.0027 and the lowest mean absolute percentage error at 0.0037. The IPSO-CNN Bi-LSTM model proved the most reliable tracker of non-linear aging, holding its cycle-wise error band to roughly 0 to 0.02 even through sharp post-cycle-25 declines in capacity.

Remaining useful life prediction—the forecast of how many cycles remain before a battery hits end-of-life—is where the review’s findings become most dramatic. Denoising transformer-based neural networks combine a one-dimensional convolutional front-end that strips sensor noise with stacked multi-head self-attention layers that learn charging and discharging trends across extended horizons. But the single best performer was a decomposition-assisted hybrid: ICEEMDAN signal decomposition feeding an optimal deep belief network tuned by a Bayesian algorithm. Trained on NASA and CALCE benchmark cells, it achieved a mean absolute error of 9.7261 × 10⁻⁵ for SOH and an absolute error of zero cycles for RUL—essentially perfect failure-cycle prediction on the test data. A chaotic particle swarm-optimized particle filter separately recorded the lowest MAPE of 0.0056 for end-of-life forecasting.

Perhaps the most forward-looking section of the review concerns joint estimation: single integrated models that predict SOC, SOH, and RUL simultaneously. Because all three metrics derive from the same underlying electrochemical state, shared feature extraction improves consistency and slashes computational overhead—a critical advantage for embedded battery management systems. One FPGA-accelerated LSTM implementation achieved SOC mean absolute error of just 0.016 percent while simultaneously estimating SOH, demonstrating that real-time multi-state monitoring is no longer theoretical. The review also catalogs a joint AR-ECM and hybrid kernel extreme learning machine approach fusing Kalman filtering with particle swarm optimization, which kept SOH error below 1 percent and SOC error below 2 percent on automotive cells.

Challenges remain, and the authors are candid about them. Model explainability is limited, a serious problem in safety-critical battery management where engineers must understand why a network flags a cell as failing. Laboratory-to-field transferability suffers from domain shift, noise in voltage and current measurements degrades robustness, and uncertainty quantification is still immature. The review’s prescription is a research agenda built on explainable AI, hybrid physics-informed learning that marries electrochemical models with data-driven intelligence, transfer learning across chemistries and operating conditions, and edge-deployment optimization for embedded hardware. If that agenda succeeds, the batteries in tomorrow’s vehicles and grids will not merely store energy—they will understand themselves, predicting their own decline with enough precision to schedule maintenance, prevent failures, and squeeze every last cycle from the multi-terawatt-hour fleet the energy transition demands.

Subject of Research: AI and deep learning techniques for estimating lithium-ion battery state of charge, state of health, and remaining useful life

Article Title: AI-driven lithium-ion battery health prediction: a review of SOC, SOH and RUL estimation techniques

Article References: AI-driven lithium-ion battery health prediction: a review of SOC, SOH and RUL estimation techniques. (n.d.). https://doi.org/10.3389/frai.2026.1858302

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1858302

Keywords: lithium-ion batteries, artificial intelligence, deep learning, battery management systems, state of charge, state of health, remaining useful life, transformers, electric vehicles, energy storage, physics-informed neural networks, predictive maintenance