We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption. We combine our novel architecture Spectral Parameter Operator built on a Fourier Neural Operator backbone for global context and a differentiable physics module in the forward pass. From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies, as well as aerodynamic drag, rolling resistance, effective mass, and auxiliary power. These parameters drive a physics-embedded estimate of battery power, eliminating any separate physics-residual loss. The modular design lets representations converge to physically meaningful parameters that reflect the current state and condition of the vehicle. We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9. The surrogate achieves a mean absolute error of 0.2kW (about 1% of average traction power at highway speeds) for Tesla vehicles and about 0.8kW on the Kia EV9. The framework is interpretable, and it generalizes well to unseen conditions, and sampling rates, making it practical for path optimization, eco-routing, on-board diagnostics, and prognostics health management.

Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method learns real-world parameters such as motor efficiency, regenerative braking efficiency, vehicle mass, coefficient of aerodynamic drag, and coefficient of rolling resistance using automatic differentiation based on dynamics and ensures consistency with ground truth vehicle data. EV-PINN was validated using 15 and 35 minutes of in-situ battery log data from the Tesla Model 3 Long Range and Tesla Model S, respectively. With only vehicle speed and time as inputs, our model achieves high accuracy and generalization to dynamics, with validation losses of 0.002195 and 0.002292, respectively. This demonstrates EV-PINN's effectiveness in estimating parameters and predicting battery usage under actual driving conditions without the need for additional sensors.

A Tesla owner is blaming his vehicle's Full Self-Driving feature for veering toward an oncoming train before he could intervene. Craig Doty II, from Ohio, was driving down a road at night earlier this month when dashcam footage showed his Tesla quickly approaching a passing train with no sign of slowing down. He claimed his vehicle was in Full Self-Driving (FSD) mode at the time and didn't slow down despite the train crossing the road - but did not specify the make or model of the car. In the video, the driver appears to have been forced to intervene by veering right through the railway crossing sign and coming to a stop mere feet from the moving train. Tesla has faced numerous lawsuits from owners who claimed the FSD or Autopilot feature caused them to crash because it didn't stop for another vehicle or swerved into an object, in some cases claiming the lives of drivers involved.

The sun had yet to rise in Delray Beach, Fla., when Jeremy Banner flicked on Autopilot. His red Tesla Model 3 sped down the highway at nearly 70 mph, his hands no longer detected on the wheel. Seconds later, the Tesla plowed into a semi-truck, shearing off its roof as it slid under the truck's trailer. Banner was killed on impact. Banner's family sued after the gruesome 2019 collision, one of at least 10 active lawsuits involving Tesla's Autopilot, several of which are expected to go to court over the next year. Together, the cases could determine whether the driver is solely responsible when things go wrong in a vehicle guided by Autopilot -- or whether the software should also bear some of the blame.

As if Elon Musk did not have enough on his plate with Twitter, Tesla is now under fire in a full-page advertisement in the New York Times that warns its'Full Self-Driving presents a life-threatening danger to child pedestrians.' The ad, which cost about $150,000, is from software maker The Dawn Project and claims to highlight safety testing conducted by the firm in October. A video of the experiment suggests the system does not register or stop for small mannequins crossing a road, according to the group. The testing involved a man driving in a Tesla on a back road and running over child-size mannequins in his path. Using the Tesla Full Self-Driving Beta 10.69.2.2, which is the latest version of the system, the vehicle collided with a 29-inch mannequin at speeds as low as 15 miles per hour and it ran over a four-foot-tall one at 20 miles per hour.

Tesla revealed on Friday a prototype of a humanoid robot that it says could be a future product for the automaker. The robot, dubbed Optimus by Tesla, walked stiffly on stage at Tesla's AI Day, slowly waved at the crowed and gestured with its hands for roughly one minute. Tesla CEO Elon Musk said that the robot was operating without a tether for the first time. Robotics developers often use tethers to support robots because they aren't capable enough to walk without falling and damaging themselves. The Optimus' abilities appear to significantly trail what robots from competitors like Hyundai-owned Boston Dynamics are capable of.

Tesla Director of Artificial Intelligence and Autopilot Andrej Karpathy is leaving the company at a critical time - as it faces renewed probes over crashes and growing scrutiny. Tesla's head of artificial intelligence and autopilot Andrej Karpathy, pictured above at a conference, is leaving the company at a critical time'It's been a great pleasure to help Tesla towards its goals over the last 5 years and a difficult decision to part ways. In that time, Autopilot graduated from lane keeping to city streets and I look forward to seeing the exceptionally strong Autopilot team continue that momentum,' he wrote on Twitter, noting that he has no plans for what's next. Tesla CEO Elon Musk replied to thank him for his work at the company. The leadership change comes at a challenging time, as Tesla faces renewed scrutiny from US regulators over crashes involving drivers who used Autopilot and works to expand the latest version of Full Self Driving (FSD) to a larger number of customers.

A Tesla Model 3 car in'Full Self-Driving' mode has been captured colliding with a bike lane barrier post, in a potential setback for Elon Musk's firm. The footage was captured during a drive in downtown San Jose, California, by a YouTuber who goes by the name AI Addict, and provides the first recorded evidence that the feature has been directly responsible for an accident. It shows the latest version of Tesla's self-driving software, Full Self-Driving (FSD) Beta version 10.10, veering the Model 3 into the bollard separating a bike lane. Even though the driver is hitting the brakes and furiously spins the steering wheel away from the obstacle, the AI-powered FSD system hits the bollard with a big thud. Worryingly, at other points in the video the Model 3 appears to run a red light and attempts to go down a railroad track and later a tram lane.

Electric Vehicles (EVs) are a rapidly growing component of the automotive industry and are projected to have over 30 percent of the overall United States light duty vehicle market by 2030 (Wolinetz and Axsen, 2017). It's very different from traditional researches realated to transportation about road conditions (Huang et al., 2019), aviation (Bauranov et al., 2021) and manned driving (Chai et al., 2021). Furthermore, the US and other countries have bet big on Battery Electric Vehicles (BEVs), allotting funding for charging infrastructure, subsidies and tax credits and setting deadlines to phase out combustion engine vehicles. Correspondingly, the stock price of EV companies like Tesla have recently far exceeded those of traditional auto manufacturers, helping to illustrate the bullish outlook many consumers and investors have toward EVs in general. Despite this, there remain concerns among both consumers and experts about various aspects of electric cars, and despite the excitement surrounding them, EV adoption rates hovered around 1.8% in 2020 (energy.gov,

Washington, DC (CNN)Drivers with Tesla's "full self-driving" software often don't know what their cars will do next. Tesla owners have been wowed by their cars' new abilities, but some say they have also been alarmed and frustrated by the accompanying flaws. One second drivers find themselves praising the cars' skills; the next moment they're grabbing the wheel to avoid crashing or breaking the law. "Full self-driving" is a suite of driver-assist features that Tesla hopes can one day enable cars to drive themselves. Other automakers like Mercedes-Benz, GM, Ford and Volvo offer cars with similar features that may change lanes, parallel park, identify speed limit signs and brake for pedestrians.