Smart 24 V Battery Switch for a Reliable Redundant Power Supply in Commercial, Construction, and Agriculture Vehicles (CAV)
Bibliographic record
Abstract
For highly automated driving, commercial vehicles require an Electric/Electronic (E/E) architecture, which - in addition to sensor fusion - ensures safety-critical processes such as steering and braking at all times. Among other things, a redundant 24 V supply with corresponding disconnection is required. The battery switch is a key component. Commercial, construction, and agricultural vehicles (CAV) need to operate at the highest possible availability and the lowest possible cost of ownership. This is why automated and autonomous driving has the potential to revolutionize the CAV sector. Driverless machines can be operated around the clock and almost non-stop. Platooning allows automated, interconnected trucks to drive in a convoy and very close to each other. Platooning saves fuel. The North American Council for Freight Efficiency has calculated the following: In a scenario with two virtually connected trucks, the fuel consumption of the front vehicle is reduced by 4.5%, and by 10% in the rear vehicle, due to reduced aerodynamic drag on all of the vehicles. In a scenario with three connected trucks, platooning even leads to an average savings of 10% for all three vehicles. This improves traffic flow, saves fuel, and reduces harmful emissions such as nitrogen oxides (Figure 1). In agriculture, automated, precisely controlled tractors spread fertilizer and seed much more accurately. In addition, automated systems significantly reduce the risk of accidents. All of this protects the environment and saves costs. This is why, in sectors such as logistics, the interest in automated driving is even higher than that of private car manufacturers. It is safe to assume that the first automated vehicles will be either trucks or other commercial vehicles. A key function of the system is the battery disconnect switch. This master cutoff introduces an isolator to ensure the batteries in the 24 V main network are properly separated from the redundant 24 V network. For guaranteed reliability in areas with extreme weather, from excessive cold to sweltering heat, a battery disconnect switch design must withstand and work reliably in a range of harsh conditions and environments.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".