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Tesla’s damage monitoring patent hints at cars driving to repair centers autonomously
Despite being cutting-edge machines that could be described as “the most fun thing” that anyone can possibly buy, Tesla’s electric cars are still subjected to a great deal of stress during operation. Electric cars have fewer moving parts than their fossil fuel-powered counterparts, but nevertheless, the components that move, such as their electric motors and suspension, are still subject to different types of stress.
One of Tesla’s recently published patent applications, titled “System and Method for Monitoring Stress Cycles,” discusses this particular issue. As noted by the electric car maker, machines may heat up or cool down, or speed up and slow down at different times during operation, resulting in thermal and mechanical stress. Over time, such stress could result in decreased performance, which is referred to as damage.
Damages are costly and hazardous. Stress-related damage results in equipment downtime, performance degradation, safety hazards, and maintenance expenses, to name a few. In the case of Tesla’s electric cars, these damages can cause breakdowns, or worse, accidents. To prevent this, strategies are usually employed to detect and address stress-related damage, such as repairing damaged parts or replacing components at set intervals. Tesla notes in its patent application that both practices are time-consuming and costly.
“Even regular inspections may not provide adequate protection against stress-related damage. For example, the inspections may not provide sufficient insight into the characteristics of the stresses imposed on a given component to accurately assess its condition. Moreover, the inspections themselves may be burdensome and costly,” the company wrote.
With this in mind, there is a need for a system that can detect and address stress-related damage in a more efficient and cost-effective manner.
Tesla’s recently published patent application outlines a system involving a processor configured to monitor stress imposed on subsystems while determining the cumulative damage to a vehicle’s systems. Tesla notes that a stress monitoring system would work optimally if the processor is configured to monitor stress cycles in real-time, allowing the system to avoid using too much memory in the process. Tesla describes the concept in the following discussion.
“To address these challenges, processor 140 may be configured to monitor stress cycles in real-time. For example, processor 140 may identify and record stress cycles concurrently while receiving the series of stress values from stress sensors 131-139. In some embodiments, for each received stress value in the series of stress values, processor 140 may perform one or more operations to determine whether a stress cycle has been completed. When processor 140 detects the end of a stress cycle, processor 140 may record the stress cycle immediately, such that the cumulative damage model can be continuously updated to reflect the latest recorded stress cycle.
“In some examples, real-time monitoring of stress cycles may be performed without storing the series of stress values in memory 150. For example, rather than storing a complete series of stress values for later data processing, a comparatively small number of stress values may be stored temporarily to track in-progress stress cycles, but other stress values may be discarded as soon as they are received. Accordingly, the amount of memory used during real-time monitoring of stress cycles may be reduced in comparison to alternative approaches.”
Adopting such a system gives notable benefits to electric car owners. By using a real-time monitoring model, for one, drivers would be notified by their vehicles once a component needs maintenance. In some instances, the car could immediately send stress and damage data to the company. Taking the concept even further, Tesla notes that a vehicle equipped with autonomous driving features would be able to drive itself to a service center when it needs repairs.
“In some embodiments, an operator of vehicle 110 may be notified when damage to subsystems 121-129 is detected. For example, the operator may be alerted when the level of damage reaches a predetermined threshold, such that the operator may take an appropriate remedial action (e.g., bringing vehicle 110 in for maintenance). In one illustrative example, when the level of damage is represented as a damage fraction, the operator may be alerted when the fractional damage to a given subsystem reaches 70%. In some examples, the alert may be communicated to the operator via a dashboard 160 (and/or another suitable control/monitoring interface) of vehicle 110.
“In some examples, processor 140 may be coupled to one or more external entities over a network 170. Accordingly, processor 140 may be configured to send stress cycle and/or damage data over network 170 to various recipients. For example, processor 140 may send stress cycle and/or damage data to a service center, such that service center may contact the operator to schedule a maintenance appointment when a damaged subsystem is identified. Additionally or alternately, when vehicle 1 10 is an autonomous vehicle, vehicle 110 may be instructed to drive autonomously to service center for repairs.”
Tesla is arguably one of the most proactive companies in the auto industry. For example, automotive teardown expert Sandy Munro has already dubbed the company’s batteries as the best in the market today, but Tesla’s Automotive President Jerome Guillen has stated that the company is still constantly making its batteries even better. In an interview with CNBC, Guillen pointed out that the design of Tesla’s battery cells is “not frozen.” With this in mind, it is not very surprising to see Tesla exploring proactive new ways to figure out more effective ways to monitor damages on its electric vehicles.
Tesla’s constant initiative to improve is teased somewhat in the patent applications from the company that has been published over the past few months. Among these include an automatic tire inflation system that teases off-road capabilities for the company’s vehicles, a system that addresses panel gaps during vehicle assembly, a way to create colored solar roof tiles, and even a system that uses electric cars as a way to improve vehicle positioning.
The full text of Tesla’s recently published patent application could be accessed here.
Elon Musk flew under the radar with his recent purchase of a $1 billion energy company, according to Federal Trade Commission (FTC) documents.
Transaction number 202612350 listed Tesla and SpaceX frontman Elon Musk as the acquiring party and CF APR Super Holdings LLC as the seller, with New APR Energy, LLC as the acquired entity. The deal, which closed without public announcement, came to light on May 14.
BREAKING: Elon Musk acquires Jacksonville power company APR Energy in a deal valued at more than $1,000,000,000.00.
— Polymarket Money (@PolymarketMoney) July 15, 2026
Analysts inferred the deal’s scale from minority stakeholder disclosures, including one report of a 5 percent interest sold for approximately $50.4 million. Fortress Investment Group had purchased APR’s assets in late 2024, rebranded the operation as New APR Energy, and subsequently transferred ownership to Musk.
APR Energy specializes in rapidly deployable power infrastructure. The company maintains one of the world’s largest fleets of mobile gas and diesel turbines, with more than 1.1 gigawatts of generation capacity. Its modular units, which are often trailer-mounted, enable turnkey installations ranging from 20 MW to over 500 MW.
APR provides full engineering, procurement, construction, operation, and maintenance services for behind-the-meter power plants, serving everything from data centers, utilities, and industrial clients.
The firm has expanded aggressively to meet surging demand, recently adding turbines and deploying over 100 MW for a major AI hyperscaler. Its solutions bridge critical gaps where grid interconnections face delays of two to five years, according to Yahoo.
The acquisition means something more for Musk. As he continues to expand projects in artificial intelligence, especially xAI, his AI venture, there is a greater need to supply energy-intensive supercomputing clusters, including the Colossus project, with what they need: reliable and high-capacity power.
Ownership of APR provides immediate access to flexible generation assets that can be deployed adjacent to data centers, reducing dependence on a strained infrastructure. It also complements Tesla’s energy storage business, so Musk will be able to pull from his own entities to address the rapid scaling demands of AI training and compute.
Tesla had a petition protesting a recall to fix a potential issue with 2017-2023 Model Y and Model 3 vehicles’ headlights was denied, as the National Highway Traffic Safety Administration (NHTSA) disagreed with the company’s opinion of things.
The recall covers approximately 19,917 Model Y and Model 3 vehicles built from 2017 to 2023. Tesla initially submitted a noncompliance report for the headlights on these vehicles on March 15, 2024. Tesla then petitioned for an exemption from the fix, which violated FMVSS No. 108 (40 CFR 571.108), arguing that the “noncompliance is inconsequential as it relates to motor vehicle safety.
🚨 Tesla was denied a petition by the NHTSA to avoid a recall of 19,900 2017-2023 Model 3 and Model Y vehicles.
The NHTSA found that the vehicles’ headlights may exceed maximum lighting levels. Tesla argued it was inconsequential and did not require a recall. pic.twitter.com/m8Jmm1teLL
— TESLARATI (@Teslarati) July 16, 2026
The NHTSA disagreed, stating that Tesla’s conclusion that the headlights do not increase any risk was not an opinion it shared. The agency said it disagreed with Tesla’s assumption that glare is not increased to surrounding traffic. This issue could be highlighted even more in certain weather conditions.
Tesla will be required to remedy the issue, the NHTSA ruled:
“In consideration of the foregoing, NHTSA has decided that Tesla has not met its burden of persuasion that the subject FMVSS No. 108 noncompliance is inconsequential to motor vehicle safety. Accordingly, Tesla’s petition is hereby denied, and Tesla is consequently obligated to provide notification of and free remedy for that noncompliance under 49 U.S.C. 30118 and 30120.”
The issue here appears to be the angle of the headlights and the brightness they emit during operation. The NHTSA report states that:
“Tesla’s headlamp supplier, Marelli Automotive Lighting, tested 25 right-hand and 25 left-hand lamps, and for this sample, found the maximum photometric intensity measured in the 10°U to 90°U and 90°L to 90°R zone was between 136.2 cd and 230.1 cd for the right-hand lamps and between 117.5 cd and 160.3 cd for the left-hand lamps. According to Tesla, these tests revealed that the photometric intensity of the right-hand and left-hand headlamp lower beam on the subject vehicles may measure as much as 230.1 cd in the 10°U to 90°U and 90°L to 90°R zone, exceeding the maximum photometric intensity by 105.1 cd. Additionally, Tesla states that a left-hand lamp tested by a Transport Canada recognized laboratory measured a maximum of 171.27 cd in the 10°U to 90°U and 90°L to 90°R zone. Despite these measurements exceeding the allowed photometric maximum of 125 cd, Tesla believes that the subject noncompliance is inconsequential to motor vehicle safety.”
Tesla also argued at some points that the headlights had not been deemed responsible for any complaints, accidents, or injuries related to the noncompliance.
Lifestyle
NTSB findings on fatal Tesla crash tell a very different story
The NTSB confirmed the driver, not Tesla’s FSD, caused the fatal Texas house crash.
The National Transportation Safety Board released preliminary findings Wednesday confirming that a Tesla driver, not the vehicle’s software, caused a fatal crash in Katy, Texas in June. The driver, 44-year-old Michael Butler, had engaged Full Self-Driving Supervised mode on Rose Hollow Lane, a residential street with a 30 mph speed limit, before manually overriding the system by pressing the accelerator pedal all the way to 100%. Data recovered from the 2025 Tesla Model 3 showed the vehicle was traveling over 70 miles per hour when it struck a home and killed 76-year-old Martha Avila, who was inside. Weather was clear, the road was dry, and it was daylight.
Texas man charged in fatal Tesla crash where he blamed Autopilot
Butler told authorities he had passed out at the wheel. But security camera footage obtained by the NTSB told a different story, and showed the car accelerating through an intersection before leaving the road entirely. Police also found that Butler’s phone had Google searches including the terms “Tesla FSD not aggressive enough 2026” and “Tesla FSD too timid,” raising serious questions about how he was using the system before the crash. Butler has since been charged with manslaughter. The victim’s family has filed a lawsuit against both Butler and Tesla, alleging negligence.
The NTSB findings aligned directly with what Tesla VP of AI Software Ashok Elluswamy had already stated publicly on X in the weeks after the crash, writing that “the driver manually overrode self-driving by pressing the accelerator all the way to 100%.” The data confirmed his account.
Yup. In this case, the driver manually overrode self-driving by pressing the accelerator all the way to 100% of the accel pedal in this residential area. They reached a speed of 73 mph during the crash, and had the accelerator pressed even after the crash.
— Ashok Elluswamy (@aelluswamy) June 22, 2026
Elon Musk secretly acquires $1B energy company to power the AI future
Tesla has to fix a big problem with its old headlights, NHTSA says
