A new IT challenge is emerging: building a vast infrastructure for electric vehicles, or EVs. Information technology is needed to give the electric car a much-needed push -- handling the vast data processing required to optimize power utilization from the generation plant all the way down to an individual owner's garage. These functions are needed to make the new cars successful, analysts say.
New EV models from Chevy and Nissan, with Ford and BMW following this summer, can already connect to a smart grid and transmit a wealth of data about battery usage and driving patterns over 3G. Toyota and Microsoft jointly announced in early April that they are co-developing an Azure-based service to provide data to Toyota EV drivers.
"IT can analyze the onboard and off-board energy management required for electric cars and help the driver find the next charging station," says Thilo Koslowski, vice president for automotive at Gartner. Other tasks tech can assist with, he says, include reserving a charging station, routing drivers to charging stations and starting the billing process to pay for the car's charging, as well as controlling the power load for electric utilities. "We can also analyze driving behavior and decide where to put stations," he adds.
If any of this sounds familiar, it's because we've been down this road before. In the late 1990s, General Motors introduced the first production EV from a major automaker, called the EV1. It was doomed from the start. A documentary about the vehicle presented a few theories about why it failed, but one major reason was that IT was not prepared for a major transition from gas-powered cars to those that juice up on a power cord.
According to several carmakers and those who follow the auto industry, this time IT appears ready for the electric car. Here's how information technology will make sure you can make it to work on battery power and locate a charging station.
Addressing range-anxiety issues
The auto industry uses the term "range anxiety" for good reason. Electric cars can go only about 50 to 100 miles before they need to be recharged, and there are precious few charging stations -- there are only a few dozen in San Francisco, considered a major hub of EV activity, for example. This hurdle of making sure drivers can find a station is the most critical for the EV to succeed.
"The ownership experience for electric cars has to be as comfortable and comparable to an internal combustion engine as possible," says Koslowski, explaining that if EVs do become popular, the need for infrastructure management will shift from being somewhat needed to critical.
The car companies know full well that EV infrastructure is in an early stage, so they are developing systems that help relieve some of the range anxiety.
The Nissan Leaf, for example, runs only on electric power and has a range of about 100 miles. It uses a system called Carwings that's based on Microsoft Windows Embedded Automotive 7. Through an in-dash system, the driver can plan a trip and determine whether he will find electric charging stations along the way and where they are. Existing EV stations are most often located at gas stations. Carwings also works on the iPhone and shows the current charge level, or state, so the driver can make decisions about routes before even getting into the car. The tool also lets the driver set climate controls to preheat or precool the car while it's being charged so that in cold weather, for instance, the heat is already flowing.
Mainly, the goal is to help drivers understand more about the onboard charge state and range, so they can decide whether they want to start driving. Nissan also sends a monthly update on power usage patterns via e-mail. With that data, a driver can change a commuter route or adjust climate settings and see how that impacts the battery.
Gregg Hedgren, the EV project manager at Nissan, says the company uses algorithms to look for patterns in the EV data, such as the total miles driven per charge on average and how climate controls, including air conditioning, impact range. "We're mainly focused on the unique aspects of the EV, and there is not a tremendous amount of data [collected for EVs] beyond battery and powertrain," he says. "We will continue to refine our research and algorithms, such as how frequent acceleration impacts range, and feed that data back to the driver."
For now, charging station location data is updated only once per quarter, says Hedgren, and that's often enough in this early phase of the EV infrastructure.
However, Nissan is looking at how it can partner with power companies to help drivers not just find charging stations, but also reserve them during prime charging times or even prepay for charging from the car. Another goal is to work together to help balance the overall power utility load.
Helping design the electric vehicle
IT has also been heavily involved in the design of the electric car itself. This includes developing software for charging sensors and creating industry standards for charging ports.
One of the best examples of how IT helped with designing an electric car is the Chevy Volt. GM deployed a fleet of 350 preproduction Volts to early testers, many of them GM employees, and captured data from each one. In part because of the data analysis, GM was able to design the Volt in just three years rather than the usual five.
According to Jeff Liedel, OnStar's CIO, Chevy captured data, including the current charge state and trouble codes from the engine. (OnStar is owned by General Motors, as is Chevrolet, and provides the in-car safety systems that connect drivers with emergency and towing services, among other things.) Many of the onboard EV control modules are brand-new so, Liedel says, it was very important to capture specific data from the modules, including real-time diagnostics that showed how each module was performing. Then engineers were able to track and record this, analyze a data warehouse of diagnostic information, and tweak the design.
This kind of IT involvement is not new -- OnStar has captured test-fleet data from gas-powered cars before -- but the difference with the Volt was how quickly designers captured the data and made changes.
GM used IBM Rational software for logging changes, understanding the sensor data and communicating with the team about the changes. Designers used Compuware software to run computations on the collected data, to determine, for example, how often the car needed to be charged.
"We collected on the order of hundreds of individual pieces of data from each battery pack during the test fleet development," says Liedel. "We fed that data directly back to the individuals working in our battery lab. It's all about life-cycle testing and about correlating the results in the lab to real-world field trials as we developed the vehicle." The team also considered how charging affected battery performance for the cars that were operating in hot vs. cold climates, he explains.
For its part, BMW took a similar approach in designing the Mini E production car and BMW ActiveE concept car, which the German automaker will use for field trials this summer.
During the development phase, BMW worked with UC Davis to analyze data from test cars, says Rich Steinberg, BMW's manager of electric vehicles operations and strategy. BMW analyzed data on the battery, such as how turning on climate controls remotely while the car was connected to a charging station -- something you can do using Nissan's Carwings iPhone app -- impacted battery performance over the life of the test compared with not preheating or precooling.
To the BMW researchers' surprise, they found that most EV drivers tend to recharge at night, even if the EV is nowhere near empty. This habit will play well with the version of the Mini E that will be released in the United States. Leaving the car plugged in all night helps precondition the battery, which increases the car's driving range.
BMW was able to use this data, stored on a central server in Munich, to make design changes not just in the battery pack, but also in how the vehicles are made. For example, with the Mini E test, the company found that it could use lighter materials in construction that helped with overall range.