Study of Replacing the Traditional Electromechanical Relay with the Full Semiconductor Solution of Bussed Electrical Center
Bibliographic record
Abstract
To face the challenges of CO2 emission and automated driving, the electrical distribution system (EDS), as the basis of all electronic loads, needs to be continuously changed. Traditional bussed electrical center (BEC) has limited functions such as simple switch and fuse protection, while the full semiconductor solution of smart BEC can provide more accurate diagnosis, faster response, higher reliability with lower power loss and smaller space. This paper will introduce the practical function of the smart BEC: in normal operation of the car, the voltage and current of the loads can be detected by the smart BEC. Once in abnormal, immediate feedback will be transferred from smart BEC to the whole system and a related response will be triggered in time, while the cost of power harness can also be optimized. In parking mode, the quiescent current of the loads from KL30 can be detected by smart BEC, which could prevent against leakage. Automated driving is a hot topic, many people focus on functional safety and redundancy of the actuators in the car, which can only be realized by the safe power supply. Therefore, this paper will also describe the fail safe and fail operational of power supply with smart BEC. Of course, replacing traditional relays with semiconductors will face many challenges, such as the switch off energy for inductive load, inrush current for capacitive load, thermal problem of the system, cost optimization and so on. The paper will introduce the solutions to these challenges. These solutions have practical significance because they are based on analysis of the loads in the real car. Finally, the paper will show the actual comparison with the traditional BEC and the smart BEC in terms of weight, size, power loss, wiring saving, and cost in the real car.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".