Power System in Self Driving Vehicle
Power System in Self Driving Vehicle
A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human intervention to a predetermined destination over roads that have not been adapted for its use.
Automated vehicles require sensors and computer processing that can perceive the surrounding environment and make real time decisions. These additional electrical loads expand the auxiliary load profile, therefore reducing the range of an automated electric vehicle compared to a standard electric vehicle. Furthermore, a fully automated vehicle must be fail-safe from sensor to vehicle control, thus demanding additional electrical loads due to redundancies in hardware throughout the vehicle.
Self-driving vehicles employ a wide range of technologies like radar, cameras, ultrasound, and radio antennas to navigate safely on our roads. In modern autonomous vehicles, these technologies are used in conjunction with one another, as each one provides a layer of autonomy that helps make the entire system more reliable and robust. For example, Tesla’s driverless car technology, known as “Autopilot”, uses eight cameras to provide 360-degree visibility, while twelve ultrasonic sensors and a front-facing radar work to analyze the vehicle’s surroundings for potential hazards. However, one key component still in development that will ultimately make autonomous cars more reliable is the implementation of 5G cellular networks. Like the 4G LTE connections we’re accustomed to on our smartphones, 5G is a type of mobile broadband that allows for the wireless transfer of data from one device to another, only at a much, much faster rate.
Power system:
Currently, vehicles have a multitude of sensors and electronics that are not directly related to the powertrain of the vehicle, called auxiliary loads. Depending on the size of these auxiliary loads, they can make a significant difference in the range of an electric vehicle. These auxiliary loads include heaters, fans, lighting, power steering, infotainment systems, and the air conditioning unit.
Fig.: Power system in self driving vehicle
The heating, ventilation, and air conditioning (HVAC) system is one of the largest auxiliary loads on an EV. Research has shown the HVAC electrical load is highly dependent on the ambient temperature, and HVAC systems can have up to a 35% impact on the range of the vehicle at extreme temperatures. Other research shows the range of an electric vehicle can drop to almost half of its maximum value at temperatures below freezing. Studies have also shown the average auxiliary load of an EV is slightly above 1 kW when the ambient air is between 60– 75◦F. It can be inferred from these references that at least 500 W of auxiliary load are used for HVAC on average, and the remaining auxiliary loads consume around 500 W of power, independent of ambient temperature. The absolute minimum auxiliary load required to keep a standard non-automated EV operating was reported to be approximately 200 W. Additional auxiliary power will be needed to supply energy to sensors and computer processors for an automated vehicle to drive. Above fig. shows a general wiring diagram of an EV. The HVAC is connected directly to the high voltage battery due to its large voltage input and potential power demand. All other auxiliary components, including additional auxiliary loads used for vehicle automation, are powered by the 12 V bus running throughout the vehicle or by a DC/DC converter connected to the 12 V bus. The 12 V bus must have sufficient charge to engage the relays and connect the high voltage battery to the motor drive to start the EV.
POWER DIVISION
Power Source:
The first and simplest energy system structure includes computing energy and electrical energy in a single space of a vehicle. In this configuration, the high-voltage battery, low-voltage battery, and computing hardware are located in the same overall space in the vehicle. This configuration can allow localized computing and redundancy in the functional space; however, due to the centralized placement of all critical power, computing and control resources without providing power and control redundancy for the sensors, the design is not fail-safe.
Distributed Power Sources:
Another solution that can be used to distribute power to the vehicle’s automatic sensors is to install two separate 12V batteries at each end of the vehicle. General Motors has already mentioned the use of this technology and has demonstrated multiple sensor power supplies in its autonomous vehicles. Some power sources are scattered over an area and each power source is assigned multiple loads. If one power source is no longer working, there is redundancy to allow other sources to compensate for the dysfunctional power source or cables. For the sake of integrity, the lines of communication between the centralized or distributed computer system and the autonomous driving sensors must also be redundant. The power supply is scattered throughout the vehicle, which will require additional costs and wiring. However, due to its redundancy, this configuration is more secure than the centralized configuration.
Power Consumption:
Better Route Choice:
Compared to drivers in non-familiar environments, drivers with autonomous vehicles can choose routes more efficiently. Autonomous vehicles without driving tasks can improve route selection capabilities by judging traffic conditions in real time. For example, autonomous vehicles can learn about traffic conditions through communication to find the shortest route more efficiently. They can optimize routes to meet certain goals, such as finding routes with fewer stops. If the route selection algorithm is combined with the computing power of automated vehicles, more efficient trips can be made. The energy consumption in this case would be reduced from enhanced route choice from as little as -5% to as much as -20%.
Route with Population:
Autonomous vehicles can reduce or even eliminate the burden of driving tasks, such that those who cannot drive can do so in autonomous vehicles. For example, the elderly and the disabled will benefit from autonomous vehicles and increase their travel options. By 2030, approximately 74 million older people are expected to live in the United States, representing 26% of the American population. Also, those who use public transportation for business trips tend to depend on cars after retirement. If 4,444 more autonomous cars make up the US fleet, these groups can either maintain this usage rate or increase this usage rate. This is because an Autonomous Vehicle driver’s license may not be required, because autonomous vehicles require relatively fewer driving tasks compared to traditional vehicles.
Long-distance Travel:
Since the Autonomous vehicle takes over the driving function, driving tasks are reduced. With the help of autonomous cars, the load required for driving can be reduced and drivers of the autonomous car can focus on other activities. For example, passengers can work while driving, use portable cellular or wireless devices, chat with other people, or even sleep. Therefore, the driving time value of the autonomous car will be less than that of the traditional car, and drivers will be less willing to travel longer distances in the autonomous car. Therefore, with the adoption of driverless vehicles, long-distance travel will increase. The increased travel distance with Autonomous vehicles would increase energy consumption for the ground transportation sector. However, airline travel would continue to be chosen by travelers for distances of 1,000 miles or more . The analysis predicted the increased rate of energy consumption to be from 6% to 18%.
Analog ICs in EV’s:
Fully autonomous cars will clearly have many different electronic systems with a mix of both digital and analog ICs. These will include advanced driver assistance systems (ADAS), automated driving computers, autonomous parking assist, blind spot monitoring, intelligent cruise control, night vision, lidar, and more—the list goes on. All of these systems require a variety of different voltage rails and current levels for their correct operation; however, they can be required to be powered directly from the automobiles battery or alternator and, in some instances, from a post regulated rail from one of these rails. This is usually the case for the core voltages of VLSI digital ICs such as FPGAs and GPUs that can need operating voltages sub-1 V at currents from a couple of amps to 10s of amps.
System designers must also ensure that the ADAS comply with the various noise immunity standards within the vehicle. In an automotive environment, switching regulators are replacing linear regulators in areas where low heat dissipation and efficiency are valued. Moreover, the switching regulator is typically the first active component on the input power bus line and therefore has a significant impact on the EMI performance of the complete converter circuit.
There are two types of EMI emissions: conducted and radiated. Conducted emissions ride on the wires and traces that connect up to a product. Since the noise is localized to a specific terminal or connector in the design, compliance with conducted emissions requirements can often be assured relatively early in the development process with a good layout or filter design as already stated.
However, radiated emissions are another story altogether. Everything on the board that carries current radiates an electromagnetic field. Every trace on the board is an antenna and every copper plane is a resonator. Anything other than a pure sine wave or dc voltage generates noise all over the signal spectrum. Even with careful design, a power supply designer never really knows how bad the radiated emissions are going to be until the system gets tested, and radiated emissions testing cannot be formally performed until the design is essentially complete.
Filters are often used to reduce EMI by attenuating the strength at a certain frequency or over a range of frequencies. A portion of this energy that travels through space is attenuated by adding metallic and magnetic shields. The part that rides on PCB traces is tamed by adding ferrite beads and other filters. EMI cannot be eliminated, but can be attenuated to a level that is acceptable by other communication and digital components. Moreover, several regulatory bodies enforce standards to ensure compliance.
Challenges:
1. Increased software complexity
Most of the autonomous vehicles being developed today are essentially testing the increased complexity of the sensor and software algorithms required to process the vast amount of information coming into the car, make the right decisions, and take action. This processing requires a large amount of software. By current estimates, we already have a billion lines of code to power fully autonomous cars. Compared to traditional automotive embedded processing, the computational requirements to run this large amount of software are more similar to server performance. This is driving a trend to integrate more powerful application processors and accelerator clusters into higher-performance multicore SoCs instead of discrete CPUs. This type of integration requires major changes to the software architecture and can also result in a significant increase in the software footprint.
The complexity of software applications is even more complicated than that of state-of-the-art passenger planes that are already full of autonomous driving functions, because autonomous vehicles will have to deal with the very chaos full of human drivers
and unpredictable pedestrians in front of a relatively unpredictable sky. path of. All professional pilots. This results in a lot of algorithmic processing that must be done in real time to understand everything that is happening around the car, and then all the autonomic computing components need to make the right decisions and execute the huge software stack required in the right way. .. This increased complexity helps to adopt a common, unified platform architecture on which to build a portable and easy-to-upgrade software stack.
2. Increased sensor complexity
The move from ADAS to autonomous demands a much greater awareness of everything around the car. In order to accomplish this, the number of sensors on the car are dramatically increasing, with multiple LiDAR, camera and radar sensors required to essentially replace and enhance human sight and situational awareness. Not only are these sensors expensive, but the processing required to understand what they are “seeing” and the situation evolving outside the car is dramatically different to the computer required by simpler ADAS functions like adaptive cruise control or emergency braking.
6. Prototype to production
The compute systems going into today’s autonomous prototypes are typically based on off-the-shelf server technology. The challenge with server technology is that the size, power consumption and thermal properties are not suitable for cars. There needs to be a significant reduction in all of these current attributes. The common belief is that the power consumption needs to be reduced by 10x, the size by 5x, and if both of these can be achieved then there will be a significant reduction in cost and dissipated heat, which also leads to simpler and more reliable cooling methods. These improvements will lead to the true deployment of self-driving cars, both in the consumer space and robotaxis.
Conclusion:
Automated vehicles are now being rapidly pushed into consumers’ hands. However, there are still many steps that need to be taken before full automation will be implemented on roadways. The basic sensor requirements have been discussed and
presented for different levels of automation, as well as the impact vehicle automation could have on the auxiliary power, reliability, and range of the vehicle. The wiring architecture necessary to power these additional automated vehicle sensors has also been discussed, and consideration has been given to communication and power wires along with potential hardware fail-safe measures. Moreover, sensor measurements were compared with data sheet values, and inferences were made about the effect of sensors on the 12 V bus. Highly automated vehicles still need improvement from a sensor, legislative, and computing standpoint before they become a commercial method of transportation.
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