Advanced Electrolyte Additives for Lithium-Ion Batteries: Classification, Function, and Future Directions
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are widely employed as energy storage devices, particularly in portable electronics and electric vehicles, owing to their high energy density and efficiency. Among the key components of LIBs, the electrolyte plays a crucial role in determining capacity, cycling stability, rate performance, the electrode/electrolyte interface, and overall battery efficiency. However, traditional electrolytes face significant challenges, including severe structural degradation and interfacial side reactions under high-voltage and high-temperature conditions. Protective layers, such as the cathode-electrolyte interphase (CEI) and solid-electrolyte interphase (SEI), are essential for addressing these issues. These layers inhibit electron transfer while allowing lithium-ion (Li + ) transport, preserving the structural and electrochemical integrity of the battery. A cost-effective strategy to further enhance the electrode–electrolyte interface and boost LIB performance is the incorporation of carefully designed electrolyte additives. While some articles discuss the use of electrolyte additives in LIBs, there is a lack of detailed studies classifying these additives based on their chemical composition-based grouping. Such a classification enables a more focused examination of the roles and mechanisms by which these additives improve LIB performance. This review paper bridges this gap by examining various electrolyte additives and their contributions to enhancing the safety and performance of next-generation LIBs. It provides valuable insights into the current progress and challenges associated with additives in liquid electrolytes. The article is organized into seven sections, addressing boron-based electrolyte additives (Section 2), sulfur-based electrolyte additives (Section 3), phosphorus-based electrolyte additives (Section 4), fluorine-based electrolyte additives (Section 5), and nitrogen-based electrolyte additives (Section 6). Each section discusses specific examples, the formation of SEI and CEI layers, and the electrochemical properties of these additives. Furthermore, the article concludes with a summary and outlook, advocating for continued advancements in electrolyte engineering for LIBs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".