EV Intelligence Dataset

Most EV data sites compare cars for consumers. ElectronsX was built for something much larger: mapping the electrified, autonomous, software-defined mobility ecosystem from end to end. That requires a deeper, EV-native data model built around voltage architecture, charging curves, compute platforms, ride tech systems, depot integration, over-the-air behavior, and autonomy readiness.

The ElectronsX database includes over a thousand EVs worldwide. Most are not fleet vehicles. That is intentional. To understand why certain platforms succeed in commercial deployments, the dataset has to represent the full design space, from luxury SUVs with swivel captain’s chairs to compact robotaxis to megawatt-class heavy trucks.

ElectronsX captures both extremes: the premium comfort features of high-end EVs and the operational metrics that define high-duty fleet performance.

It is not a consumer database. It is not a fleet-only database. It is an EV-native intelligence layer engineered for the next decade of electrification, autonomy, and robotics.

Global Market Coverage

The dataset spans all major EV markets. In addition to the standard US, EU, Japan, and South Korea models, it includes:

• Full India EV market coverage – cars, compact SUVs
• Full China EV market coverage – sedans, SUVs, MPVs, vans, robotaxis
• Emerging-market EVs – Turkey

Why an EV-native dataset is needed

Legacy car databases were retrofitted to include batteries. They still sort vehicles by length, weight, and zero-to-sixty times, metrics that have little to do with how an EV behaves as an energy, compute, and autonomy node.

ElectronsX inverts the hierarchy. The dataset puts EV-critical architecture first:

• battery chemistry and usable capacity
• voltage platform
• charging curve shape
• thermal management system
• platform control systems such as steer-by-wire, brake-by-wire, and torque vectoring
• driver assistance and autonomy stack
• operating system, update cadence, and app ecosystem
• compute platform, including inference system-on-chip and accelerators

Dimensions like wheelbase, tires, or trunk size are trivial to find elsewhere. Voltage sag behavior or inference chipsets are not.

This makes the dataset useful for engineers, fleet operators, depot designers, autonomy researchers, and city-scale deployment planners.

What the dataset includes that others don't

ElectronsX tracks categories rarely found in one place.

Battery and electrical architecture

• usable capacity versus gross capacity
• chemistry family, including NMC, LFP, LMFP, and emerging solid-state formats
• cell and pack supplier, such as CATL, BYD, LGES, Panasonic, or in-house packs
• voltage platform, such as 400 volt, 800 volt, or higher-voltage architectures
• pack type, including cell-to-pack, modular, and structural designs
• thermal strategy, including heat pumps, refrigerant loops, and advanced approaches

Charging behavior

• peak direct current charging power and the sustained plateau
• twenty to eighty percent charge window and typical dwell times
• alternating current depot charging capability, including single-phase and three-phase configurations
• supported connector sets, including NACS, CCS, GB/T, and megawatt charging readiness for heavy-duty vehicles

Ride technology stack

ElectronsX treats ride and handling as software-defined systems rather than static suspension geometry.

The dataset goes beyond consumer-spec sheets and captures deeper EV-native technologies:

• Steer-by-wire
• Four-wheel steering
• Independent wheel drive
• Torque vectoring
• Adaptive suspension + predictive damping
• Platform voltage / inverter architecture

Ride tech matters for fleets and autonomy because robotaxis need precise low-speed control, humanoid and robot co-existence requires predictable motion behavior, and energy efficiency is shaped by control algorithms as much as by hardware.

Autonomy + Compute Fields

• ADAS platform (NVIDIA Drive, Tesla FSD, Mobileye, Huawei ADS, etc.)
• Inference chipset + NPU architecture
• Perception sensor suite mapping
• OTA cadence, telematics bandwidth, data pipeline characteristics

Robotaxi platforms

ElectronsX tracks more than ten robotaxi and autonomy-first platforms, including:

• Tesla Cybercab
• Zoox purpose-built robotaxi
• Waymo Jaguar I-Pace and Zeekr-based robotaxis
• Motional Ioniq 5 robotaxi
• Baidu Apollo robotaxi platforms
• WeRide robotaxi and autonomous shuttle platforms
• emerging OEM-native robotaxi trims from major automakers

These are indexed with the same metadata fields as road EVs, plus autonomy-specific extensions.

What the Dataset Intentionally Omits

ElectronsX deprioritizes attributes that do not materially illuminate electrification performance, such as:

• length, width, and height
• wheelbase
• curb weight
• trunk or frunk volume
• cosmetic trims and minor option packages

These values are widely available from consumer-oriented sites and do not significantly affect fleet behavior, depot design, autonomy integration, or energy performance.

Luxury and Experience-Centric Features

ElectronsX captures premium features because they are infrastructure signals as well as user-experience differentiators.

• rear cinema screens and multi-screen cabins
• branded audio systems with significant power draw
• swivel and lay-flat second-row captain’s chairs
• integrated fridges, tables, and comfort modules
• cabin-first layouts that resemble autonomous shuttles and robotaxis

Luxury EVs often preview future autonomy interiors and energy usage patterns. ElectronsX tracks them for exactly that reason and also to support cross-linking with high-end mobility content.

Fleet-Relevant Subset: Hero 75

A distilled subset of 75 models optimally suited for commercial, fleet, depot, and autonomy use cases, scored using the Fleet 4-Score framework.

The Hero 75 models are:

• aligned with realistic depot charging patterns
• capable of useful duty cycles under load
• supported by credible service and upfit ecosystems
• material for serious fleet evaluation and procurement decisions

Each Hero 75 model is scored using the Fleet Pro four-score system, combining Fleet Utility, Total Cost of Ownership, Charging and Depot Readiness, and Driver Assistance and Experience into a composite score. This subset becomes the core reference set across ElectronsX fleet analyses.

Why the ElectronsX Dataset Is Different

EV-Native Architecture

ElectronsX treats energy, voltage, charging, compute, and software updates as primary attributes. Traditional consumer fields become secondary supporting context.

Fleet-Centric Intelligence

The dataset is engineered to support Fleet Pro scoring, depot planning, autonomy readiness, and energy autonomy systems, not just consumer shopping comparisons.

Cross-Domain Integration

ElectronsX data flows into Fleet Energy Depot analysis, charging corridor modeling, Energy Autonomy Yard systems, microgrid and battery energy storage sizing, fleet scheduling research, autonomy simulations, and humanoid and industrial robot adjacency.

Why This Matters

EVs are no longer cars with batteries. They are energy clients, grid nodes, software endpoints, autonomy platforms, and robotic fleet assets. They are inputs to microgrids, fleet depots, and AI-driven infrastructure.

To analyze them properly, the dataset must reflect this reality. ElectronsX is built for the mobility and energy systems we are entering, not the ones we are leaving.

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