Tesla's (NASDAQ:TSLA) ability to raise capital should mitigate fears over solvency. The $350 million equity and $850 million in convertible notes offers improved operating leverage with minimal dilution of approximately 4.6 million shares or 3% dilution. Not too bad considering expectations of generating futures revenues of over $12 billion in EVs.
In the big picture, let's focus on how Tesla is positioned to leverage the additional capital and build the infrastructure to effectively compete in the EV, energy storage, solar and in particular, what IP can be gained from competencies in self-driving vehicles. Tesla's position in energy storage is a differentiator in EVs where it commands the lead in battery efficiency and vehicle range. Tesla can gain a cost advantage by continuing to drive lower battery cost per kWh.
Tesla Vision can generate substantial value because of the competencies extended through image and sensor processing that are also a catalyst for AI. Tesla Vision is leveraging testing and data compiled from its relationship with Mobileye (NYSE:MBLY), recently announced target of acquisition by Intel (NASDAQ:INTC) for $15 billion. Image and data processing of numerous data points from RADAR, LIDAR, and cameras are complex requiring graphic floating-point intensive operations and neural network architecture. The capabilities gained through autonomous driving offer substantial competitive advantages that create additional value for Tesla.
Energy Storage and EV Performance
Tesla has improved its EV positioning as the leader in the EV market by extending the EV range battery efficiency can be measured by energy density or kW per kilogram of battery weight power duration. In practicable measures the vehicle range relative battery kWh size is a relevant measure. However, vehicle size and weight will have an impact on the range metric. To compensate for vehicle weight a measure of distance and weight may offer a more impartial performance metric. The following chart shows the battery kWh size relative to vehicle ton-miles similar to comparing freight costs.
Figure 1 Battery Performance Metrics
Tesla demonstrates superior performance when ton-miles are used to evaluate EV distance performance. Tesla is able to leverage its leading battery design in differentiating EVs, energy storage and solar.
We can use battery efficiency as measured by ton-miles per kWh and compare to distance in miles to evaluate vehicle performance, irrespective of battery efficiency. The following chart illustrates EV battery efficiency relative to miles of driving range.
Figure 2 Vehicle Performance Metrics
Figure 2 reveals that Chevy Bolt by GM (NYSE:GM) is very close to reaching parity with Tesla in terms of battery efficiency but Tesla is able to achieve a longer driving distance in EV design.
Two key performance metrics in the energy storage market are power as measured in kW and duration as measured by kWh. There is an array of technologies addressing the utility, commercial, and residential markets. Super capacitors, lead acid, lithium-ion as well as redox flow - reduction-oxidation approaches are being used to increase both power and duration of batteries.
Redox flow battery designs offer higher scale power and duration but come at a cost. Size of the containers and pipes used in redox flow is proportional to their capacity and duration. Some of the elements used in redox flow batteries are extremely dangerous substances so remediation should be introduced as part of the battery total cost. Redox flow systems include chromium, vanadium and bromine. While these approaches may serve well in utility and industrial applications, the key variable is delivering feasible and viable energy storage is cost per kWh.
In term of cost per kWh, Tesla is able to offer batteries at a cost below $200 per kWh. A cost advantage in energy storage dramatically improves Tesla's position because they can address a wider market by concentrating on energy storage costs thus helping to deliver a lower operating cost structure.
Figure 3 Energy Storage Cost per kWh
Tesla enjoys a cost advantage with respect to these competing approaches and a large portion of the redox flow approaches is the remediation involved after the useful life of the battery. There are significant costs with lithium-ion batteries such as those used by Tesla. Lithium-ion also is constrained by the number of recharge cycles they can endure. Also cold weather can drain battery power. However, Tesla has developed battery replacement and recycling as part of their strategy and is effective in reducing their battery cost per kWh.
To effectively address the utility and commercial applications, components and infrastructure to provide cost effective grid interconnects are required. Renewable energy such as solar and wind are intermittent energy sources meaning they are weather dependent. The problem with the renewable energy and the utility is electric must be pulled from consumption not pushed from generation. If consumption is elevated power disruption can occur if supply is unavailable. Extra electric generation can't be used without energy storage. Energy storage is crucial to manage supply of solar and wind with grid. Energy storage also requires cost effective grid connections that are simple to deploy and manage.
Production scale and modular design should enable Tesla to deliver cost-effective energy storage systems that can address these more energy intensive markets. Price per kWh and modular grid interconnects will drive the utility energy storage market. Tesla has the modular design, low cost, and vertical integration to provide complementary set of energy products.
Tesla Vision and IP
While battery technology and EV design position Tesla as a leading EV and energy storage provider, the value generated through its experience and data in developing autonomous driving should carry a higher market multiple. In developing advanced driver assistance systems, a host of disparate data sets from image processing to sensors streams of RADAR, LIDAR, and SONAR are compulsory in truly delivering complete vehicle driving autonomy. Tesla is gaining IP from the data collected from image and sensor processing which includes street level visual data, traffic patterns and vehicle data that can enable Tesla to improve algorithms related to autonomous driving.
It is not just image and data processing that is brought into play but the network architecture, integrated circuit selection that are geared toward graphical processing that are also needed. Tesla has been using Nvidia's (NASDAQ:NVDA) technology after braking from Molbileye to improve the data and visual processing. Nvidia offers a parallel platform and API to interface with its GPU dedicated to image processing.
To gain a better perspective on the value of autonomous driving can mean for Tesla, let's review Intel's pending $15.3 billion acquisition of Mobileye. Mobileye is an Israeli-based company leading in autonomous driving where computer vision and machine learning together with sensor data capture and mapping are orchestrated to develop self-driving machines. Mobileye software EyeQ is used by most of the world's auto manufactures where it's imaging and navigational mapping technologies help to deliver the algorithms that are used to perfect self-driving vehicles
The explosion of data generated from autonomous driving is huge and the data is crucial in perfecting autonomous driving algorithms. The issue for conventional automakers is that the value resides in the data. There is a tremendous amount of data pulled from video cameras covering roads, streets, and interstates as well as traffic patterns, visual landmarks and vehicle sensor data. To generate autonomous driving algorithms, cameras and sensors data collection and requisite competencies in data and image processing is required. These competencies help Tesla to differentiate products and open new market opportunities. Value will migrate to technology firms such as Tesla that are gaining these capabilities.
Tesla is positioned to effectively compete in the EV, energy storage, and solar markets by leading in EV and energy storage performance. Tesla's leading position in energy storage is a key differentiator in EVs and its battery efficiency and lower cost per kWh enhances its advantage in energy storage. The IP and technical competencies gained in these technologies should enable Tesla to address new markets and lower cost structure. Expanding market opportunities and improving competitive position should enable Tesla to create value and drive stock price.
Tesla Vision data and image processing from autonomous self-assist driving drive the value of its IP. Tesla Vision drives additional value because of the competencies gained through image and sensor processing perfect the algorithms in autonomous driving and enable an option play in AI.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.