What is the Importance of a Data-driven User Experience in Automotive?

 

Introduction

In the first blog of this series, How Data Analytics Enables Growth for EV Manufacturers we reviewed a brief history of electronic vehicles and related technologies, big data, data science, predictive analytics, and the data science lifecycle.

In this article, we examine the importance of a data driven user experience, as well as considerations around charging optimization, reliability, serviceability, maintainability, cost reductions, and the overall value that data analytics provides to the EV manufacturer.

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Today’s data-driven user experience

A data-driven user experience (UX) has real value with emerging EV technology. UX merges the digital and the physical, which neatly describes the very act of driving a car or riding in one. It is the tactile, logical, and emotional relationship between a modern vehicle and the humans interacting with it.

Every day, cars are becoming more like our smart phones, electrically powered and digitally connected devices with impressive capabilities and fluid ownership models. In the past, the human was the intelligent processor and decision maker, and the car was just a dumb machine that went where you pointed it and stopped when you pressed the right pedal… until something mechanical went wrong. Time marched onward, automotive technology improved, and vehicles became equipped with gauges and warning lights that provided the driver with more information, but still required the human to correctly assess the information and then properly act on it.

As vehicle capabilities increased, so did the quality of the UX. Today, hands-free Bluetooth communications have become the norm and, in some states, the law. Steering wheel controls can be discerned by touch thanks to their location, shape, and surface characteristics, empowering the driver to keep their eyes on the road. Headlights can be automatically turned on in low-light conditions, and they can be set to dim automatically when oncoming traffic is encountered. Temperatures in different interior zones can be dialed in and maintained independently. Cars have developed the ability to sense the proximity of objects around them and react with appropriate visual, audible, and tactile feedback warnings or, in some instances, can turn the wheel and apply the brakes. Lane-sensing feedback technology can transmit a mild vibration into the steering wheel if the car comes close to drifting out of the lane, and in some instances, can softly adjust the steering to keep the vehicle in the lane. Cruise control has become smarter and has incorporated the ability to sense and maintain a prescribed distance from the vehicle in front. Some cars can even parallel park themselves. Controls have become more ergonomic and easier to use. And GPS navigation is now pretty much standard, along with audible driving instructions in the voice of your choosing. And many cars have become wireless hotspots, enabling internet usage by passengers while underway.

The result of all these UX improvements, is a safer and more engaging vehicle. Today’s modern UX capabilities can be overwhelming, but they are well worth clarifying and communicating to the consumer. Most consumers love these UX improvements, are willing to pay for them, and they want more. Before they can use them, they must be taught how. Before they can be taught how, they must first be made aware. Consumer education and knowledge is the fastest path to growth, and the best method for boosting our partner’s return on investment.

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Charging Optimization

How does Smart Grid Technology Support EV’s?

An EV’s “fuel” is electrical power stored in on-board batteries. That electricity comes from the power grid. Traditional power grids were primarily controlled by humans in a central control room reacting to changing conditions. However, that scenario is being eclipsed by the increased use of both real-time and historical data leveraged in conjunction with more sophisticated power grid control systems.

A smart grid is an electrical power grid equipped with automation. It consists of the means to communicate and process data, the means to sense conditions and react appropriately, and the ability to deliver the power itself. A smart grid can monitor the flow of power from the sources of generation to the points of consumption (in some instances, all the way down to the individual appliance level), and then control the flow of power or reduce the load in real time to match the capacity of the generation system and its ability to provide power to that specific location.

This capability will become increasingly important as more EV’s are introduced to the market, placing a greater demand on the existing power infrastructure. Balancing loads so that charging is scheduled as much as possible outside of peak hours, empowers the EV and the smart grid system to work together with the utility company to manage loads against the availability of generated power in the most cost-effective manner. Demand is smoothed out over the course of the day, reducing the risk of brownouts and power failures while optimizing the use of the existing infrastructure and reducing the cost of constructing additional power grid capacity.

Data science and smart grid technologies form the heart and soul of today’s modern EV’s and their supporting infrastructure and systems. They must work together. Without the data, the smart grid becomes blind and does not know what to do. Without the smart grid, the data cannot be applied to do useful work in real time, nor proactively prevent issues. Without them both, an EV would be little more than a dumb pile of powerless electrical and automotive hardware, unable to move, connected to a power grid that cannot supply power to all of them. However, with data science and smart grid technologies working together, the whole is greater than the sum of its parts, empowering the EV to work in efficient harmony with its surrounding infrastructure, leveraging data analytics to optimize the use of both the vehicle and the power grid supporting it.

 

Benefits of State Of Health/State Of Charge data

With the advent of EV’s, the vehicle can provide information about the battery state of health (SOH) or state of charge (SOC). It can communicate charging optimization information for when to charge, how long it will take, and provide real-time options for where. All the while, the system can constantly analyze itself and learn, improving vehicle range and accuracy

Battery SOH or SOC provides benefits for both the customer and the manufacturer to improve customer retention or lifetime value:

  • Optimizing the state of health leads to longer vehicle lifespans, reducing total cost of ownership, and improving longevity.
  • Sending feedback from the data collected as the EV is operating allows modifications to the battery management system (BMS), associated algorithms, and enables incremental improvements over time for the vehicle and their owners.

There are two sub-components to charging optimization:

  • Speed: From a vehicle owner’s perspective, the quicker I can optimally charge a battery, the better.
    • Data analytics allow the manufacturers to optimize current and voltage along with owner-specific constraints to improve the long-term health of the battery or provide lifestyle options for their end users. (Example: is this a need for quick turnaround, or is an overnight charge more appropriate?)
    • Speed keeps customers sticking with their brand, for longer lifetime value.
  • Locations: The manufacturer can provide new geofencing coordinates or location updates to push or suggest their owners into optimal locations.
    • There can be greater confidence for the owner, knowing that they can continue to drive while being kept abreast of the charging options available.

This is a large focus for LHP’s partners. The work we do for them to optimize the engineering process and data management, is significant. LHP’s knowledge and experience is tested and proven and brings real value to the relationship.

 

Range improvement and accuracy

Just like a driving a traditional internal combustion vehicle, improving the vehicle’s range, and increasing the accuracy of that range estimation, are paramount concerns. Data analytics that detail the current driving environment, how an owner drives their car, and how best for the system to manage the energy, leads to optimization of the vehicle’s range and significantly increases user confidence.

The primary concern we at LHP see from our EV partners is the lack of confidence that still generally exists in this technology, which is still emerging. This lack of confidence primary stems from the recharge requirements. The charging infrastructure is growing to meet this need. However, range accuracy can go a long way towards attracting new customers and the first mover advantage in EV presently has a pronounced effect.

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Reliability, Serviceability, and Maintainability

With telematics or IoT capabilities paired with the analytics workflows, EV manufacturers can target an optimal service Net Promoter Score (NPS) by providing a positive service engagement:

  • EV’s need less service and maintenance than their internal combustion counterparts.
  • EV manufacturers with the data on hand can work to target keeping their customers in their service channel. Specific data bolsters trust.
  • Data analytics enables monitoring diagnostic trouble codes (DTCs) in real-time. This capability aids understanding, clarifying when these EV systems will need service or warranty responses. Scheduling can be optimized to bundle services into a single visit for the convenience of the customer, optimize shop utilization, or to encourage preventive maintenance rather than waiting for a breakdown.

With EV systems, it is not just the availability of Diagnostic Trouble Codes (DTCs) and how to respond or troubleshoot the issue, it is the fact that these systems have new complexities that are not as thoroughly known, quantified, and frankly assessed, versus internal combustion’s one hundred years of progress. Several recent engagements we have completed have also demonstrated the increased computing power needed at the edge to satisfy the demands of mobile assets in the field.

Our customers have provided feedback that LHP’s full integration capability from the embedded controls, functional safety, through the IoT components, and to the cloud enterprise integration and ev analytics capabilities, have been key to success in achieving the insights our customers need from end to end.

A high level of quality directly corresponds to the EV manufacturers’ bottom line, but also positively impacts the total revenue and value potential of their customers. Overall quality in the system improves with the constant monitoring of SOH/SOC, paired with Diagnostic Trouble Code (DTC) analysis, driving conditions, and other variables.

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Continuous Overall Cost Reductions, and Overall Value Optimization

Imagine a vehicle with fewer parts (EVs have much fewer moving parts than ICE vehicles), that can diagnose itself and manage its own service schedule or, even better, can remove any servicing requirements. Imagine it is connected to the manufacturer at every level via IoT devices and toss in a consumer whose behaviors and needs are fully understood and optimized via big data in the cloud. This is what LHP’s data analytics can offer. The addition of data analytics and IoT as an integrated part of the vehicle’s design can enable this vision and make this latest push to electrification not only a reality but a necessity of modern life.

 

Interested in learning more about Data Analytics for your organization? Contact our team today!

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Further reading and references

How Data Analytics Enables Growth for EV Manufacturers 

 

 

 

 

James Roberts

Written by James Roberts

James Roberts is the President and Chief Solution Architect of LHP Data Analytics and IoT Solutions. James joined LHP in 2016 and has 10 years’ experience in Advanced Analytics and IoT, technology consulting, and management prior to joining LHP. At LHP, He leads the technical team on projects from pre-sales through delivery, which includes the following verticals: Self-Service Analytics (Business Analytics), Advanced Analytics (Predictive Analytics/Machine Learning/Data Science and Optimization), IoT Digital Twins, IoT Application Enablement Platforms (AEP), Big Data/IoT/Cloud Infrastructure, Custom Web/App Development, Business Process Consulting, Technical Consulting, and IoT Analytics Training. James is actively working with his customers’ leadership and execution teams to transform their organizations into market and industry leaders by leveraging analytics. James and his team target quick win projects (6 weeks) with the customers via high-value deliverables that prove their ROI and methods.