Frontiers in Operations: Battery as a Service: Flexible Electric Vehicle Battery Leasing
Bibliographic record
Abstract
Problem definition: The electric vehicle (EV) manufacturer NIO adopts a swappable-battery design and a battery-leasing business model known as battery as a service (BaaS). It recently introduced flexible battery leasing, which allows customers to temporarily up-/downgrade their primary leased batteries based on the needs for range. We investigate whether this business model innovation is viable, namely whether introducing flexible battery leasing in BaaS could benefit the manufacturer, the customers, and the environment compared with simple battery leasing. Methodology/results: Adopting a game-theoretical model, we find that introducing flexible battery leasing in BaaS can simultaneously improve the manufacturer profit as well as reduce the total customer cost and the total battery capacity. Such win-win-win outcomes generally occur for large high-capacity battery ranges and moderate high-capacity battery costs—both consistent with the ongoing trend in the EV industry and a model-calibration exercise. We further show that this key finding is robust for correlated regular and peak needs for range and when launching BaaS with flexible battery leasing and that if the manufacturer was to choose a high-capacity battery range for flexible battery leasing, it would choose one such that battery reallocation alone can meet all battery up-/downgrade demand without acquiring additional batteries. Managerial implications: Our findings confirm that flexible battery leasing can be a viable BaaS business model innovation and offer insights into when this may be the case. This insight strengthens the strategic support for EV manufacturers’ potential adoption of the swappable-battery design and the BaaS model, and it may inform their operating policies to implement flexible battery leasing. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0587 .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".