Do the lithium-ion cell designs found with a fast model still win under the full porous-electrode model?

research/energy-cell-dfn

Re-scoring 49 designs from energy-cell under PyBaMM's full DFN model instead of SPMe: rank correlation 0.86 at cycle 100, the SPMe winner is 3rd of 45 and still +38% over the reference cell (+31% under SPMe), DFN reports about 19 Wh/kg more on average, and SPMe fails to cycle 3 designs DFN handles (modelled, stack level)

4 tests

What it found

Takes the first next step of energy-cell: the 49 designs it logged (baseline, each search run's best, 36 spread over the SPMe ranking, 3 SPMe failures) are re-scored with the same parameters, protocol and mass model under the full Doyle-Fuller-Newman model. Over the 45 designs both models cycled, the cycle-100 Wh/kg rank correlation is 0.86 (0.93 at beginning of life); the SPMe winner (143.4 Wh/kg) is 3rd of 45 under DFN (160.9 vs 163.0 for the DFN best) and beats the reference cell by 38% under DFN against 31% under SPMe. DFN gives more energy for nearly every design (mean +18.6 Wh/kg), with gaps up to 127 Wh/kg, and the SPMe plateau is not flat under DFN. Limits: one cell, one parameter set, 49 designs chosen to spread over the SPMe ranking; both models share the same degradation parameters and neither is validated against measured data; stack-level mass only. Every input comes from energy-cell and PyBaMM's OKane2022 set.

A figure from its own output

Figure produced by research/energy-cell-dfn: scatter — energy-cell-dfn/experiments/scatter.svg, written by the project's own experiment code.

Paper, code and results

More in Energy supply and dense energy storage

How hard does a least-cost zero-emission German grid cycle its storage, and does the levelised-cost map survive that use?How hard does a least-cost zero-emission German grid cycle its storage? Batteries about 1,700–1,900 h a year (near the assumed 2,000), cavern hydrogen only 719 h, which more than doubles its LCOS to $864/MWh.
Which storage technology is cheapest at each duration once costs come from NREL ATB and PyPSA technology-data, not from round numbers?Which storage technology is cheapest at each duration once costs are sourced (NREL ATB 2024, PyPSA technology-data)? Li-ion to 4 h, cavern compressed air from 6 h; at 8 h pumped hydro 52%, air 27%, Li-ion 22% of scenarios; hydrogen's assumed 500 h win does not survive.
How much of the cost of a zero-emission German power system is the storage assumption, once costs are sourced?What does a zero-emission German power system cost once energy-mix's assumed costs are replaced by sourced ones? PyPSA on 2019 hourly demand with PyPSA technology-data costs (Danish Energy Agency et al.): EUR 222/MWh with a steel hydrogen tank, EUR 144 with caverns, EUR 75 unconstrained; the storage type moves the answer more than the cost year.
Which weather year sets the hydrogen store of a zero-emission German power system, and how much does the cost depend on it?Which weather year sets the hydrogen store of a zero-emission German power system? The same sourced-cost PyPSA model on 2015 to 2019: EUR 203 to 271/MWh with a steel tank, EUR 141 to 155 with caverns, and a cavern store of 24 to 36 TWh depending on the year.
Does residual load explain German day-ahead electricity prices on a year the model never saw, and how wrong was the flat merit-order price?Does residual load explain German day-ahead prices on a year the model never saw? Yes: correlation 0.87 and RMSE 9.6 EUR/MWh on 2019 against 23.0 for the flat merit-order price; the cheap-quarter mean is within EUR 4–9/MWh; the rest is the fuel-price level.
Does a weather-dependent heat-pump COP change the sand-store versus heat-pump verdict?Does a weather-dependent heat-pump COP change the sand-store verdict? The table's 3.6 equals a 35 C floor-heating sink; at 45 to 55 C the store's weekly flip multiplier rises 8 to 46%, and 2018 still favours the pump.

All 39 Energy supply and dense energy storage projects →

Other research topics

Proper scoring & elicitation28 Effort & peer prediction9 Verification games & slashing19 Detection & tolerances14 Markets as routers13 Prediction-market design9 Paying contributors8 Decentralised training systems23 Digital twins & sim-to-real164 LLM chip design loops40 Robot manufacturing loops53 Ambient AI assistant46 Self-replicating manufacturing34 Simulation-first design64 Engineered clothing45 Task-specific AI accelerators40 Algorithmic chip-design loops42 Open-source purpose-built robots44 The software-engineering loop43 Adversarial human imitation41 Open-source residential construction41 Autonomous multi-angle capture34 Sensing, imaging and 3D31 Energy supply and dense energy storage39 Robot societies among people31 Agentic co-design of low-cost robots27 Laundry-hanging robots25 Machine learning on qualitative data23 Robots on the building site27 Civilizations & group psychology1 History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.