Optimal design and control of battery-ultracapacitor hybrid energy storage system for BEV operating at extreme temperatures
Bibliographic record
Abstract
The battery energy storage system (BESS) is a critical and the costliest powertrain component for battery electric vehicles (BEVs). Extreme operating temperatures distort the battery's electrochemical reactions, causing permanent capacity loss, shortening operational life, and increasing lifecycle costs (LCC). In this work, new methods for optimizing battery and ultracapacitor (UC) hybrid energy storage system (HESS) design and the HESS' energy management strategy (EMS) and thermal management strategies (TMS) are introduced. In addition to altering the batteries' use pattern to extend operational life, this combination also improves battery performance and reduces the impacts on the batteries' operational life under low temperatures. First, performance and degradation data of commonly used lithium-ion (Li-ion) batteries under extreme temperatures and various use patterns are collected to form advanced battery performance, degradation and thermal models to facilitate the HESS's optimal design and energy management for a BEV under different operation conditions. Effective EMS and TMS are introduced to play the batteries and UCs to their strength and to use energy from the UCs to improve the batteries' operating temperature to extend battery life and minimize the BEV's LCC. The optimal sizing of the batteries and UCs and the HESS baseline optimal EMS are simultaneously generated using empirical data-based battery performance and degradation models and the BEV's operation cycle through global design optimization and dynamic programming (DP)-based optimal energy management. The accurate prediction of vehicle propulsion power is made through the extended Kalman filter (EKF) using both statistical and real-time data. The batteries' performance and degradation models are dynamically updated using the online battery voltage data and continuously calibrated state-of-health (SOH) model. These updates enable precise optimal control and EMS through model predictive control (MPC). The optimized battery-UC HESS design and dedicated optimal EMS and TMS extended BEV battery life by 47 %. In addition, the newly introduced real-time control reduced battery degradation by up to 23 %. • Optimal design and control of battery-UC HESS to extend performance and life of batteries under harsh operation conditions • Li-ion battery performance, thermal and degradation models under extreme temperatures and different operation conditions • A method for integrated HESS global optimal design and baseline optimal EMS generation • Real-time optimal control of BEV's HESS based on battery performance and degradation models and instant operation data • Optimal battery-UC HESS design and optimal EMS/TMS extended BEV battery life by 47%, and reduced degradation up to 23%
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".