|2020/2/25 10:30||Green Car Congress||
Terberg introduces new generation of electric terminal tractor; hydrogen fuel-cell version possible
The Netherlands-based Terberg is introducing a new generation of electric vehicles with new EV technology, including the YT203-EV terminal tractor (which replaces the older YT202-EV) and the BC202-EV body carrier. New generation YT203-EV The performance of the new electric drive system is comparable with that of diesel engines and the large battery option offers a significantly greater operating range. Additionally, the new battery technology has an extended temperature range and can be used worldwide in both very cold and warm climates. The first BC202-EV body carrier will be delivered in April 2020 and the new generation YT203-EV terminal tractor will become available at the end of 2020. With the new technology Terberg delivers significant improvements. Customers can choose from a number of battery capacity combinations so they can select the option best suited to their operations. Applications with high vehicle usage will benefit from the battery with the increased capacity, resulting in a longer range. Customers with operations involving lower vehicle usage and with more opportunities to charge the vehicle during the day can opt for a smaller battery pack, at a lower price. The new electric drive system has fewer moving parts than a diesel engine and the previous EV generation, resulting in lower maintenance costs. The new EV drive supports regenerative braking, reducing energy consumption. Finally, the vehicles can operate indoors, with zero emissions and a lower noise level, which is particularly relevant for terminal tractors. The vehicles use DC chargers and can therefore be charged at standard charging stations. The new batteries comply with the ECE-R100 rev. 2 regulation and withstand both very high and very low temperatures. This means the new Terberg YT203-EV can be deployed anywhere in the world. Additionally, the new charger connector complies with the CCS2.0 automotive standard. This charger technology is also available in the United States. The Terberg Connect telematics system provides remote monitoring of the status and performance of each vehicle, including the charge cycle and remaining battery capacity. This enables operators to charge their vehicles at just the right time, and avoid unscheduled downtime. Terberg developed the new generation electric drive as a multifunctional, modular concept. This makes it easier to adapt the EV-technology to a range of vehicles, such as the new YT203-EV terminal tractor and the BC202-EV body carrier. The design also allows for the use of hydrogen fuel cells in future. Proof-of-concept hydrogen fuel-cell-powered yard tractor YT203-H2. In January 2019, Terberg Special Vehicles and Zepp.solutions unveiled a proof-of-concept hydrogen fuel cell yard tractor. The YT203-H2 specification covers all the operational requirements for different applications such as logistics, distribution and ports for the global market. This project was supported by the DKTI-Transport regulation of the Dutch Ministry of Infrastructure and Water Management.
|2020/2/25 10:00||Green Car Congress||
SwRI motion prediction system enhances pedestrian detection for automated vehicles
Southwest Research Institute (SwRI) has developed a motion prediction system that enhances pedestrian detection for automated vehicles. The computer vision tool uses a novel deep learning algorithm to predict motion by observing real-time biomechanical movements with the pelvic area being a key indicator for changes. SwRI’s human motion prediction system overlays sparse skeletal features on pedestrians to predict movement. The blue dot in this image predicts the future direction of pedestrian on right. Deep learning algorithms predict motion based on pelvic biomechanical movement. Credit: Courtesy of Southwest Research Institute For instance, if a pedestrian is walking west, the system can predict if that person will suddenly turn south. As the push for automated vehicles accelerates, this research offers several important safety features to help protect pedestrians.—SwRI’s Samuel E. Slocum, a senior research analyst who led the internally funded project Recent accidents involving automated vehicles have heightened the call for improved detection of pedestrians and other moving obstacles. Although previous technologies could track and predict movements in a straight line, they were unable to anticipate sudden changes. Motion prediction often uses “optical flow” algorithms to predict direction and speed based on lateral motion. Optical flow, a type of computer vision, pairs algorithms with cameras to track dynamic objects. The accuracy of optical flow diminishes, however, when people move in unexpected directions. To improve accuracy, SwRI compared optical flow to other deep learning methods, including temporal convolutional networks (TCNs) and long short-term memory (LSTM). After testing several configurations, researchers optimized a novel TCN that outperformed competing algorithms, predicting sudden changes in motion within milliseconds with a high level of accuracy. The temporal design uses a convolutional neural network to process video data. SwRI’s novel approach optimizes dilation in network layers to learn and predict trends at a higher level. Dilated convolutions are structures that store and access video data for spatial observations. People draw on experience and inference when driving near pedestrians and cyclists. SwRI’s research is a small step for autonomous systems to react more like human drivers. If we see a pedestrian, we might prepare to slow down or change lanes in anticipation of someone crossing the street. We take it for granted, but it’s incredibly complex for a computer to process this scene and predict scenarios.—Dr. Douglas Brooks, a manager in SwRI’s Applied Sensing Department The research team leveraged SwRI’s markerless motion capture system, which automates biomechanical analysis in sports science. Using camera vision and perception algorithms, the system provides deep insights into kinematics and joint movement. Applications for the project, titled “Motion Prediction from Sparse Skeletal Features,” include human performance, automated vehicles and manufacturing robotics. The algorithms can work with a variety of camera-based systems, and datasets are also available to SwRI clients.
|2020/2/25 9:30||Green Car Congress||
Northwestern team develops decentralized swarming algorithm for autonomous vehicles
Self-driving vehicles need to navigate safely and flawlessly without crashing into others or causing unnecessary traffic jams. To make this possible, Northwestern University researchers have now developed the first decentralized algorithm with a collision-free, deadlock-free guarantee. The researchers tested the swarming algorithm in a simulation of 1,024 robots and on a swarm of 100 real robots in the laboratory. The robots reliably, safely and efficiently converged to form a pre-determined shape in less than a minute. If you have many autonomous vehicles on the road, you don’t want them to collide with one another or get stuck in a deadlock. By understanding how to control our swarm robots to form shapes, we can understand how to control fleets of autonomous vehicles as they interact with each other.—Northwestern Engineering’s Michael Rubenstein, who led the study A paper on the work is published in the journal IEEE Transactions on Robotics. Rubenstein is the Lisa Wissner-Slivka and Benjamin Slivka Professor in Computer Science and Mechanical Engineering in Northwestern’s McCormick School of Engineering. He’s also a member of Northwestern’s Center for Robotics and Biosystems. The advantage of a swarm of small robots—versus one large robot or a swarm with one lead robot—is the lack of a centralized control, which can quickly become a central point of failure. Rubenstein’s decentralized algorithm acts as a fail-safe. If the system is centralized and a robot stops working, then the entire system fails. In a decentralized system, there is no leader telling all the other robots what to do. Each robot makes its own decisions. If one robot fails in a swarm, the swarm can still accomplish the task.—Michael Rubenstein Still, the robots need to coordinate in order to avoid collisions and deadlock. To do this, the algorithm views the ground beneath the robots as a grid. By using technology similar to GPS, each robot is aware of where it sits on the grid. Before making a decision about where to move, each robot uses sensors to communicate with its neighbors, determining whether or not nearby spaces within the grid are vacant or occupied. The robots refuse to move to a spot until that spot is free and until they know that no other robots are moving to that same spot. They are careful and reserve a space ahead of time.—Michael Rubenstein Even with all this careful coordination, the robots are still able to communicate and move swiftly to form a shape. Rubenstein accomplishes this by keeping the robots near-sighted. Each robot can only sense three or four of its closest neighbors. They can’t see across the whole swarm, which makes it easier to scale the system. The robots interact locally to make decisions without global information.—Michael Rubenstein In Rubenstein’s swarm, for example, 100 robots can coordinate to form a shape within a minute. In some previous approaches, it could take a full hour. Rubenstein imagines that his algorithm could be used in fleets of driverless cars and in automated warehouses. Large companies have warehouses with hundreds of robots doing tasks similar to what our robots do in the lab. They need to make sure their robots don’t collide but do move as quickly as possible to reach the spot where they eventually give an object to a human.—Michael Rubenstein Resources H. Wang and M. Rubenstein (2020) “Shape Formation in Homogeneous Swarms Using Local Task Swapping,” in IEEE Transactions on Robotics doi: 10.1109/TRO.2020.2967656
|2020/2/25 9:00||Green Car Congress||
CNCDA: registration of electrified vehicles up 3.6% in California in 2019, while total car reg down 5.5%
Registrations of new electrified light-duty vehicles (HEVS, PHEVs and EVs) in California rose 3.6% to 250,566 units in 2019, up from 241,970 units, according to the California New Car Dealers Association (CNCDA), using data from IHS. Overall, the California new light-duty vehicle market declined 5.5% from 2018 to 2019 (2,002,047 to 1,892,672 units), while the total US market fell 1.1% (17,115,072 units to 16,931,012 units). The California new vehicle market recorded its 11th consecutive year-over-year quarterly decline in the Fourth Quarter of 2019. New vehicle registrations in the state fell 7.0% in 4Q19 vs. the 3.8% drop in 3Q19. The electrified vehicle market put in a strong showing due partly to a resurgence in demand for conventional hybrids. After annual declines for several years, registrations of new HEVs in 2019 jumped 24.2% to 104,702 units, according to CNCDA, even outpacing the strong California EV market. Registrations of new light-duty EVs in California climbed only 5% in 2019, to 99,704 units from 94,801 the year before. PHEVs, which have been outsold by both HEVs and EVs since 2014, saw their registrations drop to 46,160 units in 2019, a decline of 26.6%. Data: IHS, CNCDA Overall, according to CNCDA, registrations of new plug-in vehicles (PHEVs and EVs) dropped 7.5% from 2018 to 2019, from 157,648 to 145,864 units, with the result driven by the slowing rate of increased EV sales not able to offset the drop in PHEV sales. Market share. As a result, HEVs hels a 5.5% market share in California in 2019, EVs held a 5.3% marketshare and PHEVs held a 2.4% marketshare, for a combined hybrid/electric vehicle market share in 2019 of 13.2%. Tesla by itself took a 3.8% market share in 2019 in California, up 0.3 percentage points from the year before. However, 4Q19 California new vehicle registrations for Tesla were down 46.1% from 4Q18—from 25,961 units to 13,999 units. Tesla holds a 1.1% market share in the US. Overall, the California light truck share was up 3.2 percentage points in 2019 to 58.4%. US light truck share was up 3.1 percentage points to 71.9% in 2019.
|2020/2/25 8:30||Green Car Congress||
Bosch and Human Horizons partner on Battery in the Cloud service
At the Bosch Connected World 2020 (BCW) conference in Berlin, Human Horizons initiated an agreement with Bosch Connected Mobility Solutions for further cooperation on Bosch’s Battery in the Cloud technology. (Earlier post.) This technology connects electric-vehicle batteries with the cloud to extend battery life, substantially improve the battery’s performance and service. The Battery in the Cloud is based on three key steps: Current battery data is collected and pre-filtered by the telematics control unit (TCU) in the vehicles and is then transmitted to a server—generally the Bosch cloud. The data in the cloud is analyzed using software algorithms based on electrochemical, physical, and statistical models. Artificial intelligence methods are then used to calculate predictions about the condition of the respective vehicle battery and to determine its optimal parameter configuration. Additional external cloud data, such as weather and traffic conditions, map data, and available charging stations, is also factored into these calculations. The results that have been determined for the individual vehicle battery (e.g. recommendations, condition reports, new operating parameters) are transmitted from the cloud to the vehicle, and the new parameter configuration is subsequently implemented in the battery. rvice models are also an option depending on the customer’s wishes. At each of these steps, the Bosch cloud provides a maximum in security during data transmission through a sophisticated security architecture involving end-to-end encryption. In April 2019, Human Horizons and Bosch entered into strategic cooperation on Firmware Over-the-Air (FOTA) solutions for broader collaboration in addition to many technologies such as remote diagnostics, which will be applied to the HiPhi 1. HiPhi is a premium brand created by Human Horizons; HiPhi 1 is a premium EV with a lightweight hybrid-aluminum construction. Human Horizons is an innovative technology company committed to future intelligent mobility with its 3-Smart strategy of Smart Vehicle, Smart Transportation and Smart City. Over the past years, Human Horizons has embraced collaboration with Bosch Connected Mobility Solutions to bring smart vehicle technologies to Human Horizons’ smart vehicle. Today, the power of such collaboration is further strengthened. Bosch and Human Horizons will work together to connect the system and services inside and outside vehicles, transforming them into intelligent mobility solutions. Including FOTA and Battery in the Cloud, and in the near future, this close relationship will play an important role in the cooperation in remote diagnostics, and other intelligent and connected mobility solutions.—Dr. Elmar Pritsch, President of Bosch Connected Mobility Solutions In this agreement, Bosch and Human Horizons plan to cooperate on Battery in the Cloud service. This technology intends to improve the performance of EVs in the areas of HV battery life, charging efficiency, EV user experience and prediction of battery failures and downtime. EV battery life is expected enhanced by up to 20%, or charging speed increased by up to 20%. Very precise State of Health (SOH) calculations and forecasts can be used to further strengthen early detection of battery-related malfunctions, and establish the foundation for predictive maintenance to avoid downtime and enhance the user experience.