Battery Intelligence
Lithium-ion batteries degrade unpredictably under real-world cycling conditions. Without reliable RUL estimates, operators are forced to over-engineer safety margins, replace batteries prematurely, or risk catastrophic failure. Our multi-modal Transformer fuses 23 electrical and thermal signals with battery identity and manufacturing metadata to produce calibrated quantile forecasts up to 24 cycles ahead.
Battery-Level Forecast Explorer
Inspect RUL forecasts across 6 reliable test batteries with diverse charge policies and degradation patterns. Select a battery from the dropdown to view its performance and forecast visualisation.
Model Validation Metrics
Summary metrics compiled from out-of-sample evaluations across the 6 reliable test batteries with quality forecast samples, spanning 4 manufacturing batches and 82 distinct charge policies.
Average RUL prediction error across reliable batteries
Model explains ~95% of RUL variance
~9 in 10 actuals fall within the 10–90% band
Average uncertainty band width for RUL
With quality forecast samples
Root mean squared error in cycles
Across 4 manufacturing batches
Across 19 test batteries
Horizon Degradation & Calibration
How RUL forecast accuracy changes across the 24-cycle prediction horizon, alongside quantile calibration analysis comparing expected versus empirical coverage.
Multimodal Seq2Seq Transformer
Our architecture fuses 23 continuous measurement signals, temporal cycle features, battery identity embeddings, and static categorical metadata through a Transformer encoder with learnable query decoder, producing quantile forecasts via pinball loss.
Multi-Source Feature Fusion
Projects 23 measurement signals (QDischarge, IR, temperatures, voltages, currents) into 128-dim latent space alongside time, battery ID, and policy embeddings.
Temporal CNN & Transformer Encoder
Two residual Conv1D blocks capture local degradation trends, followed by 4-layer multi-head self-attention with pre-norm and GELU activations.
Learnable Query Decoder
Cross-attends over encoder memory using learned query vectors to generate 24 future RUL steps in parallel, avoiding autoregressive compounding errors.
Quantile Prediction Head
Produces 3 quantiles (P10, P50, P90) for RUL trained with pinball loss, prioritising interval calibration over point estimate accuracy.
183 Batteries, 82 Policies
Trained on 146 batteries (80%) with 18 held out for validation and 19 for testing. The model uses AdamW optimiser with cosine decay, gradient clipping, and early stopping.
Dataset Composition
183 lithium-ion batteries cycled under 82 unique charge policies across 4 manufacturing batches. 23 measurement signals per cycle plus temporal features.
Training Configuration
AdamW optimiser (lr=3e-4), linear warmup (5%) + cosine decay to 5% min, gradient clipping at 1.0, mixed precision, early stopping with patience of 15 epochs.
Context & Prediction Windows
Sliding window sampling with 72 cycles of context to predict 24 cycles ahead. 13,635 test windows totalling 327,240 test cycles evaluated.
Performance Variation
Batteries with smoother degradation achieve RUL R² > 0.95 and PICP > 90%. Fast-charging policies show higher uncertainty, with MAE ranging from 32 to 574 cycles.
Incorporate predictions
API access for pilot partners is planned as the platform matures.
curl -X POST https://api.purnabodha.ai/v1/predict/battery \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"battery_id": 102,
"context_length": 72,
"quantiles": [0.10, 0.50, 0.90]
}'Where We're Taking This Next
Every deployed system raises new questions. These research programs explore the next generation of capabilities for battery intelligence.
Physics-Informed Degradation Models
Embedding electrochemical constraints into learned representations for reliable extrapolation beyond training regimes and cycle configurations.
Fast Adaptation to New Chemistries
Few-shot transfer learning across battery formulations, form factors, and manufacturing batches with minimal retraining.
Cell-to-Pack Representation Learning
Scaling cell-level predictions to module and pack-level remaining useful life accounting for thermal and electrical coupling effects.
RUL Under Unseen Operating Conditions
Generalizing across temperature, C-rate, and depth-of-discharge profiles not encountered during training.
Interested in piloting this on your battery data?
We're seeking battery manufacturers, EV fleet operators, and energy storage labs interested in evaluating this model on production battery data.