6 strategies for OEMs to improve Electric Vehicle (EV) range

 The automotive industry is changing right before our very eyes. Today, services based on the CASE model are looming on the horizon. They are capturing an increasing market share and gaining more and more each year in total dollar value. What's in store for the automotive sector and how automotive enterprises can seize these opportunities?

New mobility services are emerging rapidly

By 2030, over 30 percent of the projected increase in vehicle sales due to urbanization and macroeconomic growth will be unlikely to happen owing to the shared mobility expansion.

In China, the European Union, and the United States, which are countries supporting  shared mobility solutions , the mobility market  could reach 28 percent annual growth from 2015 to 2030 . Of course- this would be the most optimistic scenario. FutureBridge specialists expect the shared mobility market to grow significantly over the next five to seven years at a CAGR of 16 percent from 2018, reaching  180 billion dollars by 2025 . How can the growing demand for new mobility services be explained?

On the one hand,  the automotive industry deals with  changing consumer preferences . One travels by car covering shorter distances, but much more frequently. And it doesn’t have to be by car at all, as new means of transportation are becoming more accessible.

On the other hand, soaring car prices (though cars lose their value a few months after the purchase) prompt us to search for other, cheaper alternatives that provide optimal driving comfort anyway.

How will companies relying on the traditional car ownership model respond to this trend? They will provide new services such as substitution models, in which, for a once-off monthly payment, you can have a new car with insurance, maintenance, roadside assistance, etc.  Subscriptions will soon account for about 15% of new car sales and should have risen to 25% by 2025. In this context, new mobility in the form of rental and ride-sharing services, which are also part of the transformation on the roads, also becomes significant.

The third thing is growing technology,  based on the CASE model(Connectivity, Autonomous driving, Shared mobility, Electrification,) that empowers the development of new mobility services on an unprecedented scale. According to Microsoft experts, by 2030 virtually all new cars will have been connected devices, functioning as data centers on wheels.

6 leading new mobility services

Carsharing

A short-term car rental model that allows users to choose a vehicle and pick-up/drop-off location. Users can determine vehicles and flexible rent times. Operators gain high ROI with high utilization and minimal staffing.

 Examples: citybee, E-VAI, fetch

Ride-hailing

A form of cab rental in which the drivers are usually contractors using their private vehicles rather than direct employees. The user has immediate availability and payment is handled through the operator. The benefits are also the ability to track and monitor journeys. For operators instead, traditional fleet costs must be handled by the drivers. It’s an easily scalable service.

 Examples: Uber, Lyft, Bolt, marcel, OLA

P2P Sharing

this service allows vehicle owners to rent their vehicles when they are not currently in use. BMW-run ReachNow is piloting a version of this type of service, which allows Mini owners to offer their currently unused vehicles for rent. The benefits for users are the lower costs than traditional vehicle rental. Meanwhile, the operator has no fleet to manage and gets access to an easily scalable model of business.

 Examples: HoppyGo, SnappCar

Carpooling

Allows users to join an already scheduled trip. The operating company acts as an "intermediary" through which rides can be announced and joined. Carpooling can apply both to people taking a trip alone and to those who want to share rides to reduce the total cost of the trip for a single passenger. It’s a cheap and environmentally friendly service. What is more, the operator has a higher margin per ride and no fleet to manage.

 Examples: BlaBlaCar, GoMore, liftshare

Car rental

The evolution of the traditional car rental by the day, allowing users to rent cars for different periods without the traditional hassle associated with this type of service. From the user's point of view, such new services enable an easier and quicker process of vehicle rental. Also, it’s possible to choose a vehicle before finalizing the rental. In turn, the operator has less staffing than a traditional rental and can utilize already existing fleets.

 Examples: Audi Silvercar, Hertz, Sixt, PORSCHE DRIVE, UBEEQO

Multimodal

An integrator of public transport mobility services, as well as other modes of transportation, such as public transportation, rail networks, and even cabs. The goal of such services is to get people from their starting point to their destination in the fastest, cheapest, or most efficient way, depending on individual needs. In this model, the operator gets access to additional potential users and has relatively low costs of deployment due to a lack of physical assets.

 Examples: FREE2MOVE, whim, Google Maps

Which new mobility services are growing the fastest?

Of the 55 providers of the aforementioned new mobility services operating in European countries, the most popular are those in the area of  carsharing  (51%) . The second most popular are  car rental services  (20%) , followed by  P2P sharing  (13%) .

In terms of ownership, most new mobility services were OEM owned (over 36%), although many of them were independent (over 38%). Also included were OEM invested services (31%).

Technologies and functionalities fueling the development of new mobility services

Mobility services are based on advanced software that uses, at least, the Internet of Things, to transfer data from the vehicle to the cloud. Then the individual information is available on the user's mobile application.

For services based on unmanned vehicle rental, modern security features have been considered when it comes to opening and closing the car.

With a view to minimizing possible problems, the developers of digital new mobility services are also introducing a  fault reporting option.

 Below is a selection of the most common functionalities and technologies in detail for each new mobility service in Europe.

All of these and other options provide guidance and a certain pattern of behavior for future developing OEMs.

Key factors crucial for the development of new mobility services

CASE trends provide new opportunities for the vehicles of the future. However, the interrelationships between software, in-car sensors, and electronic systems require a  huge amount of resources , especially when we are talking about reliable operations that translate into a competitive advantage of new mobility services and popularity among potential users.

 Therefore, if you want to develop in this area, consider at least these few factors.

1.     Cybersecurity    . In addition to creating huge amounts of code, what also matters is that your user data tracking processes comply with the standards and regulations that apply in your geographic region.

2.  Careful listening to user needs. In order to compete with technology start-ups, OEMs should focus on innovative digital solutions oriented towards     actual consumer expectations    . What matters is flexibility, when it comes to the portfolio of functionalities.

3.  Certainly,     emotion    is a factor that must be taken into account. Solution providers should care about providing unique experiences and sensations that will make the user eager to re-use a particular service, and in the process, spread it to their community.

4.     Flexibility and scalability    . You need to be prepared not only to meet the changing expectations of customers who come in with feedback but also to expand functionality to include those that competitors already have (or to offer completely innovative solutions).

5.     Being ready to expand the offering    . For example, with new types of vehicles: not only internal combustion but also hybrid, electric; not only cars but also city scooters, etc.

If you want to deal with the challenges that come with developing new mobility services and are considering the above and other growth factors, contact Grape Up. We can help you expand your business in terms of features and values appreciated by today's conscious consumers.

 Ever since Henry Ford implemented the first production line and launched mass production of the Ford Model T, the automotive industry has been on the constant lookout for ways to boost performance. This aspect has become even more relevant today, given the constant market and social unrest. Coming to rescue supply chain management and product lifecycle optimization is predictive maintenance. Not only OEMs, but the entire automotive industry: insurers, car rental companies and vehicle owners are benefiting from the implementation of this technology.

Predictive maintenance explained

Predictive maintenance is an advanced maintenance approach that utilizes data science and predictive analytics to anticipate when equipment or machinery requires maintenance before it faces a breakdown.

The primary aim is to schedule maintenance at optimal times, considering convenience and cost-effectiveness while maximizing the equipment's longevity. By identifying potential issues before they become critical, predictive maintenance significantly reduces the likelihood of equipment breakdowns.

Various types of maintenance strategies are employed in different industries:

1.     Reactive Maintenance:    Also known as "run-to-failure," this method involves waiting for equipment to fail before conducting maintenance. Therefore, unscheduled downtime and higher repair costs may occur.
2.     Periodic Maintenance    : This approach entails performing maintenance tasks at regular intervals, regardless of the equipment's condition. It helps prevent unexpected breakdowns but may lead to unnecessary maintenance if done too frequently.
3.     Smart Maintenance    : Smart maintenance utilizes advanced technologies like IoT devices and sensors to monitor equipment in real-time and identify anomalies or potential failures.
4.     Condition-Based Maintenance    : This strategy relies on monitoring the equipment's condition while it is in operation. Maintenance is only carried out when data indicates a decline in performance or a deviation from normal parameters, optimizing maintenance schedules and reducing unnecessary work.
5.     Predictive Maintenance    : The most advanced type of maintenance uses real-time operational data and predictive analytics to forecast when maintenance is required. It aims to schedule maintenance before equipment failure occurs based on data-driven predictions, thus minimizing downtime, reducing costs, and prolonging equipment lifespan.

Predictive maintenance employs various techniques, such as vibration analysis, acoustic monitoring, infrared technology, oil analysis, and motor circuit analysis. These methods enable continuous equipment condition monitoring and early detection of potential failures, facilitating timely maintenance interventions.

Differentiation between predictive maintenance and preventive maintenance

 Predictive maintenance hinges on the real-time condition of assets and is implemented only when the need arises. Its purpose is to anticipate potential failures by monitoring assets while they are actively operational. Unlike  preventive maintenance , this approach is rooted in the current operational state of an asset rather than statistical analysis and predetermined schedules.

Essential steps in creating a predictive maintenance solution

Predictive maintenance solutions utilize a combination of sensors, artificial intelligence, and data science to optimize equipment maintenance.

The development of such solutions varies depending on equipment, environment, process, and organization, leading to diverse perspectives and technologies guiding their creation. However, there are steps common to every project: data collection and analysis, model development and deployment, as well as continuous improvement.

Here is a step-by-step process of how solutions are developed in the  automotive industry :

•     Data Collection    : Relevant data is collected from sensors, equipment logs, vehicle diagnostics, telemetry, and other sources. This data includes information about the performance, condition, and behavior of the vehicles, such as engine temperature, fuel consumption, mileage, and more. Telematics systems can provide real-time data on vehicle location, speed, and usage patterns, while maintenance logs record historical maintenance activities, repairs, and part replacements.

•     Data Preprocessing    : The collected data is organized, and prepared for analysis. Data preprocessing involves cleaning the data by removing outliers or erroneous values, handling missing values through imputation or interpolation, and converting the data into a suitable format for analysis.

•     Feature Engineering    : Important features or variables that can provide insights into the health and performance of the vehicles are selected from the collected data. These features can include engine vibration, temperature, fuel consumption, mileage, and more. Feature selection step involves identifying the most relevant features that have a strong correlation with the target variable (e.g., equipment failure). It helps to reduce the dimensionality of the data and improve the model's efficiency and interpretability. Later, selected features are transformed to make them more suitable for modelling. The process may include techniques such as logarithmic or exponential transformations, scaling, or encoding categorical variables.

•     Model Development    : Machine learning algorithms are applied to the selected features to develop predictive models. These models learn from historical data and identify patterns and relationships between various factors and equipment failures. The algorithms used can include regression, decision trees, random forests, neural networks, and more.

•     Model Training and Validation    : The developed models are trained using historical data and validated to ensure their accuracy and performance. This involves splitting the data into training and testing sets, evaluating the model's performance metrics, and fine-tuning the model if necessary.

•     Deployment and Monitoring    : The trained models are deployed into the predictive maintenance system, which continuously monitors real-time data from sensors and other sources. Telematics systems are used to collect GPS and vehicle-specific data, which it transmits through different methods (cellular network, satellite communication, 4G mobile data, GPRS) to the central server. The system detects anomalies, recognizes patterns, and provides insights into the health of the vehicles. It can alert maintenance teams when potential issues are detected.

•     Continuous Improvement    : The predictive maintenance solution is continuously improved by collecting feedback, monitoring its performance, and updating the models and algorithms as new data becomes available.

Most common problems in deploying predictive maintenance solutions

Implementing predictive maintenance solutions in a fleet of vehicles or in a vehicle factory is a process that requires time, consistency and prior testing. Among the main challenges of rolling out this technology, the following aspects in particular are noteworthy.

Data integration

Integrating data from many sources is a significant barrier to implementing predictive maintenance solutions. To accomplish this with a minimum delay and maximum security, it is necessary to streamline the transfer of data from machines to ERP systems. To collect, store, and analyze data from many sources, businesses must have the proper infrastructure in place.

Insufficient data

Lack of data is a major hindrance to implementing predictive maintenance systems. Large amounts of information are needed to develop reliable models for predictive maintenance. Inadequate information might result in inaccurate models, which in turn can cause costly consequences like premature equipment breakdowns or maintenance.

To get over this difficulty, businesses should collect plenty of data for use in developing reliable models. They should also check that the data is relevant to the monitored machinery and of high quality. Businesses can utilize digital twins, or digital representations of physical assets, to mimic the operation of machinery and collect data for use in predictive maintenance systems.

Process complexity

Transitioning from preventive to predictive maintenance is complex and time-intensive. It requires comprehensive steps beyond technology, including assembling a skilled team and managing upfront costs. Without qualified experts versed in software and process intricacies, project success is doubtful.

High costs

The implementation of predictive maintenance programs comes with substantial costs. These upfront expenses pose challenges, including the need to invest in specialized sensors for data collection, procure effective data analysis tools capable of managing complexity, and possibly hire or train personnel with technical expertise.

To address these hurdles, collaboration with specialized vendors and the utilization of cloud-based solutions can prove cost-effective. Additionally, digital twin technology offers a way to simulate equipment behavior and minimize reliance on physical sensors, potentially reducing overall expenses.

Privacy and security issues

The implementation of predictive maintenance involves extensive data collection and analysis, which can give rise to privacy concerns. Companies must adhere to applicable data protection laws and regulations, and establish proper protocols to safeguard the privacy of both customers and employees. Even though predictive maintenance data may be anonymized and not directly linked to specific individuals, it still necessitates robust security measures, since preventing data breaches and unauthorized access to vital company information is crucial for overall success.

What Are the Benefits of Predictive Maintenance?

Life cycle optimization, stock management, or even recycling management - in each of these fields predictive maintenance can bring substantial benefits. And this is not only for OEMs but also for fleet operators, transportation or logistics companies. And even for the end user.

Below we list the key benefits of implementing  predictive maintenance in an automotive-related company:

•     Extended lifespan:    Predictive maintenance technology detects early signs of wear and potential malfunctions in-vehicle components such as engines, transmissions, and brakes. By addressing these issues proactively, vehicles experience fewer major breakdowns and continue to operate efficiently over a longer period.

•     Cost savings:    By addressing issues at an early stage, automotive companies can avoid expensive breakdowns and prevent further damage. This proactive approach not only reduces the need for costly replacement parts but also minimizes the labor and operational costs associated with major repairs, resulting in significant long-term cost savings.

•     Minimized downtime    : Through continuous monitoring and analysis, predictive maintenance predicts when maintenance or repairs are needed and schedules them during planned downtime. This minimizes the likelihood of unexpected breakdowns that can disrupt operations and lead to extended periods of vehicle inactivity. By strategically timing maintenance activities, vehicles spend more time on the road.

•     Increased efficiency    : Any iissues are detected early, enabling timely corrective actions. This proactive approach leads to improved fuel economy, reduced emissions, and overall enhanced efficiency. Vehicles operate at their peak performance, contributing to a more sustainable and environmentally friendly fleet.

•     Enhanced security:    Constant monitoring for abnormal vibrations, temperature variations, and fluid leaks ensures that potential issues compromising vehicle safety and security are detected promptly. By addressing these concerns before they escalate, predictive maintenance contributes to ensuring the security of both the vehicle and its occupants. This feature is particularly valuable in critical applications where reliable vehicle performance is paramount, such as emergency response scenarios.

•     Avoiding over-maintenance    : If you over-maintain corporate resources, it can have the same negative consequences as when failing to maintain them on time. With predictive maintenance, you can focus on maintaining crucial resources at the best possible time and with the best possible results.

•     Compliance with required standards and regulations    : Laws and regulations related to vehicle production are constantly evolving and pushing OEMs to make numerous production changes (e.g. the legislation related to EV production). Predictive maintenance allows you to better suit the new expectations of legislators and monitor the points of production that are most dependent on the legal context.  
  
•     Easier management of parts and materials    : As connected cars diagnostic systems become more sophisticated, drivers have the option to make small repairs sooner and keep their vehicles in a better condition. All this means that OEMs and licensed repair shops need fewer parts and can better manage supply chains.

 Predictive maintenance clearly is not a one-size-fits-all solution for all sectors. Notably, it will work well for high production volumes and short lead times and anywhere you need to ensure reliability, security and convenience.

The automotive industry is a perfect fit for this model. As shown in the examples featured in the second part of the article, the top players in the market are tapping into this technology.

According to  Techsci Research , “  The global predictive maintenance market was valued at USD 4.270 billion in 2020 and is projected to grow around USD 22.429 billion by 2026”.

 The development of connected car technology accelerated digital disruption in the automotive industry. Verified Market Research valued the connected car market at USD 72.68 billion in 2019 and projected its value to reach USD 215.23 billion by 2027. Along with the rapid growth of this market’s worth, we observe the constant development of new customer-centric services that goes far beyond driving experience.

While the development of connected car technology created a demand for connectivity solutions and drive-assistance systems, companies willing to build their position in this market have to face some significant challenges. This article is the first one of the mini-series that guides you through the main obstacles with building software for connected cars. We start with the basics of a connected vehicle, then dive into the details of prototyping and providing production-ready solutions. Finally, we analyze and predict the future of verticals associated with automotive-rental car enterprises, insurers, and mobility providers.

This series provides you with hands-on knowledge based on our experience in developing production-grade and cutting-edge software for the leading automotive and car rental enterprises. We share our insights and pointers to overcome recurring issues that happen to every software development team working with these technologies.

What is a Connected Car?

A Connected Car is  a vehicle that can communicate bidirectionally with other systems outside the car , such as infrastructure, other vehicles, or home/office. Connected cars belong to the expanding environment of devices that comprise the Internet of Things landscape. As well as all devices that are connected to the internet, some functions of a vehicle can be managed remotely.

Along with that, IoT devices are valuable resources of data and information that enable further development of associated services. And while most car owners would describe it as the mobile application paired with a car that allows users to check the fuel level, open/close doors, control air conditioning, and, in some cases, start the ignition, this technology goes much further.

V2C - Vehicle to Cloud

Let’s focus on some real-case scenarios to showcase the capabilities of connected car technology. If a car is connected, it may also have a sat-nav system with a traffic monitoring feature that can alert a driver if there is a traffic jam in front of them and suggest an alternative route. Or maybe there is a storm at the upcoming route and navigation can warn the driver. How does it work?

That is mostly possible thanks to what we call V2C - Vehicle to Cloud communication. Utilizing the fact that a car is connected, and it is sending and gathering data, a driver may also try to find it, in case it was stolen. Telematics data is also helpful to understand the reasons behind an accident on the road - we can analyze what happened before the accident and what may have led to the event. The data can be also used for predictive maintenance, even if the rules managing the dates are changing dynamically.

While this seems just like a nice-to-have feature for the drivers, it allows car manufacturers to provide an extensive set of subscription-based features and functionalities for the end-users. The availability of services may depend on the current car state - location, temperature, and technical availability. As an example: during the winter, if the car is equipped with heated seats and the temperature drops under 0 Celsius, but the subscription for this feature expires, the infotainment can propose to buy the new one - which is more tempting when the user is at this time cold.

V2I - Vehicle to Infrastructure

A vehicle equipped with connected car technology is not limited to communicating only with the cloud. Such a car is capable of exchanging data and information with road infrastructure, and this functionality is called V2I - Vehicle to Infrastructure communication. A car processes information from infrastructure components - road signs, lane markings, traffic lights to support the driving experience by suggesting decision makings. In the next steps, V2I can provide drivers with information about traffic jams and free parking spots.

Currently, in Stuttgart, Germany, the city’s infrastructure provides the data live traffic lights data for vehicle manufacturers, so drivers can see not just what light is on, but how long they have to wait for the red light to switch to green again. This part of connected car technology can rapidly develop with the utilization of wireless communication and the digitalization of road infrastructure.

V2V - Vehicle to Vehicle

Another highly valuable type of communication provided by connected car technology is V2V - Vehicle to Vehicle. By developing an environment in which numerous cars are able to wirelessly exchange data, the automotive industry offers a new experience - every vehicle can use the information provided by a car belonging to the network, which leads to more effective communication covering traffic, car parking, alternative routes, issues on the road, or even some worth-seeing spots.

It may also significantly increase safety on the road, when one car notifies another that drives a few hundred meters behind him that it just had a hard breaking or that the road surface is slippery, using the information from ABS, ESP, or TC systems. That has not just an informational value but is also used for Adaptive Cruise Control or Travel Assist systems and reduces the speed of vehicles automatically increasing the safety of the travelers. V2V communication makes use of network and scale effects - the more users have connected to the network, the more helpful and complete information the network provides.

The list of use cases for connected car technology is only limited by our imagination but is excelling rapidly as many teams are joining the movement aiming to transform the way we travel and communicate. The Connected Car revolution leads to many changes and impacts both user experience and business models of the associated industries.

How connected car technology impacts business models of the automotive industry

Connected cars bring innovative solutions to the whole environment comprising the automotive landscape. Original Equipment Manufacturers (OEMs) have gained new revenue streams. Now vehicles allow their users to access stores and purchase numerous features and associated services that enhance customer experience, such as infotainment systems. By delivering aftermarket services directly to a car, the automotive industry monetizes new channels. Furthermore, these systems enable automakers to deliver advertisements, which become an increasing source of revenue.

 The development of new technology in automotive creates a similar change as we observed in the mobile phone market. When smartphones equipped with operating systems had become a new normal, significantly increased the number of new apps that now allow their users to manage numerous services and tasks using the device.

But it is just an introduction to numerous business opportunities provided by connected cars. Since data has become a new competitive advantage that fuels the digital economy, collecting and distributing data about user behavior and vehicle performance is seen as highly profitable, especially when taking into account the potential interest of insurers providers.

Assembled data while used properly gives OEMs powerful insights into customer behavior that should lead to the rapid growth of new technologies and products improving customer experiences, such as predictive maintenance or fleet management.

The architecture behind connected car technology

Automotive companies utilize data from vehicle sensors and allow 3rd party providers to access their systems through dedicated API layers. Let’s dive into such architecture.

 High-Level Architecture

System components

Digital Twin in automotive

A digital twin is a virtual replica and software representation of a product, system, or process. This concept is being adopted and developed in the automotive industry, as carmakers utilize its powerful capabilities to increase customer satisfaction, improve the way they develop vehicles and their systems, and innovate. A digital twin empowers automotive companies to collect various information from numerous sensors, as this tool allows to capture operational and behavioral data generated by a vehicle. Equipped with these insights, the leading automotive enterprises work on enhancing performance and customize user experience, but meanwhile, they have to tackle significant challenges.

First of all, getting data from vehicles is problematic. Hardware built-in vehicles have particular limits, which leads to reduced capabilities in providing software. Unlike software, once shipped hardware cannot be easily adjusted to the changing conditions and works for several years at least. Furthermore, while willing to deliver a customer-centric experience, automakers still have to protect their users from numerous threats. To protect vehicles from denial of service attacks, vehicles can throttle the number of requests. Overall, it’s a good idea but can have a terrible impact when multiple applications are trying to get data from vehicles, e.g., in the rental domain. This complex problem can be simply solved by Digital Twin. It can expose data to all applications without them needing to connect to the vehicle by simply gathering all real-time vehicle data in the cloud.

Implementation of this pattern is possible by using NoSQL databases like MongoDB or Cassandra and reliable communication layers, examples are described below. Digital Twin may be implemented in two possible ways, uni- or bidirectional.

Unidirectional Digital Twin

Unidirectional Digital Twin is saving only values received from the vehicle, in case of conflict it resolves the situation based on event timestamp. However, it doesn’t mean that the event causing the conflict is discarded and lost, usually every event is sent to the data platform. The data platform is a useful concept for data analysis and became handy when implementing complex use cases like analyzing driver habits.

Bidirectional Digital Twin

The Bidirectional Digital Twin design is based on the concept of the current and desired state. The vehicle is reporting the current state to the platform, and on the other hand, the platform is trying to change the state in the vehicle to the desired value. In this situation, in case of conflict, not only the timestamp matters as some operations from the cloud may not be applied to the vehicle in every state, eg., the engine can’t be disabled when the vehicle is moving.

However, meeting the goal of developing a Digital Twin may be tricky though as it all depends on the OEM and provided API. Sometimes it doesn’t expose enough properties or doesn’t provide real-time updates. In such cases, it may be even impossible to implement this pattern.

API

At first, designing a Connected Car API isn’t different from designing an API for any other backend system. It should start with an in-depth analysis of a domain, in this case, automotive. Then user stories should be written down, and with that, the development team should be able to find common parts and requirements to be able to determine the most suitable communication protocol. There are a lot of possible solutions to choose from. There are several reliable and high-traffic oriented message brokers like Kafka or hosted solutions AWS Kinesis. However, the simplest solution based on HTTP can also handle the most complex cases when used with Server-Sent Events or WebSockets. When designing API for mobile applications, we should also consider implementing push notifications for a better user experience.

When designing API in the IoT ecosystem, you can’t rely too much on your connection with edge devices. There are a lot of connectivity challenges, for example, a weak cellular range. You can’t guarantee when your command to a car will be delivered, and if a car will respond in milliseconds or even at all. One of the best patterns here is to provide the asynchronous API. It doesn’t matter on which layer you’re building your software if it’s a connector between vehicle and cloud or a system communicating with the vehicle’s API provider. Asynchronous API allows you to limit your resource consumption and avoid timeouts that leave systems in an unknown state. It’s a good practice to include a connector, the logic which handles all connection flaws. Well designed and developed connectors should be responsible for retries, throttling, batching, and caching of request and response.

OEM’s are now implementing a unified API concept that enables its customers to communicate with their cars through the cloud at the same quality level as when they use direct connections (for example using Wi-Fi). This means that the developer sees no difference in communicating with the car directly or using the cloud. What‘s also worth noting: the unified API works well with the Digital Twin concept, which leads to cuts in communication with the vehicle as third-party apps are able to connect with the services in the cloud instead of communicating directly with an in-car software component.

What’s next for connected car technology

Once the challenges become tackled, connected vehicles provide automakers and adjacent industries with a chance to establish beneficial co-operations, build new revenue streams, or even create completely new business models. The possibilities delivered thanks to over-the-air communication (OTA) allowing to send fixes, updates, and upgrades to already sold cars, provide new monetization channels, and sustain customer relationships.

As previously mentioned, the global connected car market is projected to reach USD 215.23 billion by 2027. To acquire shares in this market, automotive companies are determined to adjust their processes and operations. Among key factors that impact the development of connected car technology, we can point out a few crucial. The average lifecycle of a car is about 10 years. Today, automakers make decisions regarding connected cars that will go into production two to four years from now. For the cellular connectivity strategy to remain relevant over 12 to 15 years, significant challenges and assumptions need to be collaboratively addressed by OEMs, telematics control unit suppliers, and service providers.

 Automakers must manage software in the field reliably, cost-efficiently, and, most importantly, securely – not just patch fixes, but also continually upgrade and enhance the functionality. The availability of OTA updates reduces the burden on dealerships and certified repair centers but requires better and more extensive testing, as the breakage of critical features is not an option.

 Cellular solutions need to be agile to be compatible with emerging network technologies over the vehicle lifetime, e.g., 5G to be the industry standard in the next few years. The chosen solution must deliver reliable, seamless, uninterrupted coverage in all countries and markets where the vehicles are sold and driven.

 Solution developers must offer scalable, cost-effective ways to develop upgradeable software that can be universally deployed across technologies, hardware, and chipsets. A huge focus must be put on testing the changes automatically on both the cloud platform side and the vehicle side.

As Connected Vehicles proliferate, the auto industry will need to adapt and transform itself into the growing technological dependency. OEMs and Tier-1 manufacturers must partner with technology specialists to thrive in an era of software-defined vehicles. As connectivity requires skills and capabilities outside of the OEMs’ domain, automakers will necessarily have to be software developers. An open platform environment will go a long way to encourage external developers to design apps for vehicle connectivity platforms.