Effects of Electrical Contact Resistance on External Energy Losses in Lithium-Ion Battery Packs for Hybrid and Electric Vehicles
Bibliographic record
Abstract
Lithium-ion (Li-ion) batteries are favored in hybrid-electric vehicles and electric vehicles for their outstanding power characteristics. In this paper the energy loss due to electrical contact resistance (ECR) at the interface of electrodes and current-collector bars in Li-ion battery assemblies is investigated for the first time. ECR is a direct result of contact surface imperfections and acts as an ohmic resistance at the electrode-collector joints. ECR is measured at electrode connections of a sample Li-ion battery, and a straightforward analysis is presented to evaluate the relevant energy loss. Through the experiments, it is observed that ECR is an important issue in energy management of Li-ion batteries. Effects of surface imperfection, contact pressure, joint type, collector bar material, and interfacial materials on ECR are highlighted. The obtained data show that in the considered battery, the energy loss due to ECR can be as high as 20% of the total energy flow in and out of the battery under normal operating conditions. However, ECR loss can be reduced to 6% when proper joint pressure and/or surface treatment are used. A poor connection at the electrode-collector interface can lead to a significant battery energy loss as heat generated at the interface. At sever conditions, heat generation due to ECR might cause serious safety issues, thermal runaway, sparks, and even melting of the electrodes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".