Dr. VipinKumar Rajendra Pawar

Dr. VipinKumar Rajendra Pawar

PhD in Remote Sensing | EV & Avionics Architect | EV System Integration & validation | UDS | Diagnostics | Navigation | Telematics | ADAS | MATLAB/Simulink/ MBD | Li-ion Battery & BMS Expert

Research Excellence Award (2021) recipient with strong expertise in Automotive Embedded Systems, EV Architecture, ADAS, Navigation, and Telematics. Passionate about developing intelligent, safe, and sustainable mobility solutions.

EV Systems ADAS UDS & Diagnostics Navigation Telematics Li-ion BMS MATLAB/Simulink RTOS Embedded Linux

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Sunday, April 9, 2023

EV Automotive Power Distribution unit





An automotive power distribution unit (PDU) is a component that manages the electrical power supply to various systems and components in a vehicle. The PDU is responsible for receiving power from the battery and distributing it to different electrical subsystems, such as lighting, infotainment, power windows, and other systems. The PDU typically consists of fuses or circuit breakers, relays, and wiring connections that allow power to be routed to different parts of the vehicle. The fuses and circuit breakers protect the electrical system from overloading and short circuits, while the relays control the flow of power to different subsystems. Some modern PDUs are computer-controlled, allowing for more efficient management of power distribution and monitoring of system performance. They may also include features such as smart fuses, which can detect faults and shut off power to prevent damage to the system. Overall, the PDU plays an important role in ensuring the safe and reliable operation of the electrical system in a vehicle.


The formula for calculating the power distribution unit (PDU) in an electric vehicle (EV) depends on several factors, including the size of the battery, the voltage of the battery, and the power demands of the various electrical subsystems in the vehicle.

The PDU must be capable of handling the maximum power demands of all the subsystems in the vehicle, which can vary depending on the specific EV model and its features. Generally, the formula for calculating the PDU power rating in an EV is:

PDU Power Rating = Total Power Demand / Battery Voltage

Where the Total Power Demand is the sum of the power requirements of all the electrical subsystems in the vehicle, including the motor controller, battery management system, heating and cooling systems, infotainment, lighting, power windows, and other electrical components.

For example, if the total power demand of an EV is 50 kW and the battery voltage is 400 V, the PDU power rating would be:

PDU Power Rating = 50 kW / 400 V = 125 A

This means that the PDU must be able to handle a maximum current of 125 A to meet the power demands of all the subsystems in the vehicle.




Saturday, April 8, 2023

EV Automotive Algorithm: Optimal Mixed Integer Programming (MIP)




#Scheduling electric vehicles (EVs) in a #Mobility-#on-#Demand (#MoD) scheme involves determining the optimal assignment of EVs to customer requests while minimizing the overall cost or maximizing the revenue. The problem can be formulated as a mixed-integer programming (#MIP) problem as follows:


Decision Variables:

*x_ij: binary variable that takes value 1 if customer i is assigned to EV j, 0 otherwise

*y_j: binary variable that takes value 1 if EV j is in use, 0 otherwise


Objective Function:

*Minimize the total cost or maximize the total revenue:

Min/Max Σ(i,j) c_ij * x_ij - Σ(j) r_j * y_j


where c_ij is the cost (or revenue) of assigning customer i to EV j, and r_j is the cost (or revenue) of keeping EV j in use.



Constraints:

*Each customer is assigned to exactly one EV:

Σ(j) x_ij = 1, for all i


*Each EV can serve at most one customer at a time:

Σ(i) x_ij ≤ y_j, for all j


*Capacity constraints:

Σ(i) a_i * x_ij ≤ b_j * y_j, for all j


where a_i is the demand of customer i and b_j is the capacity of #EV j

Non-negativity constraints:

x_ij ≥ 0, for all i,j

y_j ≥ 0, for all j



The above MIP formulation can be solved using a range of #optimization software tools, such as #Gurobi, #CPLEX, or #SCIP. The solution to the MIP problem provides an optimal schedule of #EVs in the #MoD scheme, which can be used to minimize the #cost or maximize the revenue of the system while meeting the #demand of #customers.


#electricvehicles #software #programming

Aviation Communication: ARINC429 protocol



#ARINC429 is a digital communication protocol that is widely used in aviation and other industries for transmitting data between avionics systems. It was first introduced by the Aeronautical Radio, Incorporated (ARINC) in 1977.
The ARINC 429 protocol is a unidirectional, point-to-point protocol, meaning that data is transmitted from one system to another in a single direction, and each system can only communicate with one other system at a time. It uses a differential, balanced voltage signal to transmit data, with a bit rate of 100 kilobits per second (#Kbps).
The ARINC 429 protocol supports up to 20 bits of data per message, including a 24-bit label that identifies the type of data being transmitted, and up to 16 bits of data that can be used for various purposes, such as measurements, status information, and control commands. The protocol also includes error checking and correction mechanisms to ensure the #accuracy and #reliability of the transmitted data.
ARINC 429 is widely used in #avionics #systems for transmitting data such as #altitude#airspeed#engine performance, and other critical parameters. It is also used in other #industries such as #marine#automotive, and industrial #automation, where #high-reliability, #real-time data communication is required.

An ARINC 429 frame is a unit of data transmission in the ARINC 429 protocol. It consists of 32 bits of data, including the following components:
#Synchronization (#Sync) Bit: A single bit that indicates the beginning of the frame and serves as a timing reference for the receiver.

#Source/Destination Identifier (#SDI) Bits: Two bits that identify the source and destination of the data transmission. The SDI bits are used to distinguish between different avionics systems that may be communicating on the same data bus.

#Label Bits: A 24-bit label that identifies the type of data being transmitted. The label is assigned by the avionics manufacturer and is used to ensure that the correct data is received by the appropriate system.

#Data Bits: Up to 16 bits of data that carry the actual information being transmitted, such as altitude, airspeed, or other parameters.
Parity Bit: A single bit that provides error detection for the frame. The parity bit is set so that the total number of ones in the frame is always odd or even, depending on the parity setting.


The ARINC 429 frame is transmitted over a differential, balanced voltage signal, with a bit rate of 100 kilobits per second (Kbps). The frame format allows for efficient transmission of multiple data types over a single data bus, making it a popular choice for avionics systems.

#aviation #communication #communication #digital #reference

Friday, April 7, 2023

Navigation Algorithms for Unmanned Aerial Vehicles (UAVs)







Unmanned Aerial Vehicles (#UAVs) require precise navigation algorithms to ensure that they can fly safely and achieve their objectives. The navigation algorithm of a UAV typically involves three main stages:

#Localization: determining the position of the UAV relative to its environment, typically using #GPS, inertial sensors, and other #sensors like cameras or #LIDAR.

#Guidance: determining the optimal path for the UAV to follow based on its current location and its desired destination, taking into account obstacles and other constraints.

#Control: executing the path determined by the #guidance #algorithm, adjusting the UAV's #altitude, orientation, and #velocity to keep it on track and avoid collisions.


There are several approaches to designing navigation algorithms for UAVs, including:

Classical control theory: using #mathematical models of the UAV's dynamics and control laws to ensure stability and performance.

#Model-based reinforcement learning: training a #neural #network to predict the optimal control actions based on the UAV's state and the desired objective.

Probabilistic methods: using probabilistic models of the environment and the UAV's motion to estimate the UAV's location and plan its path.
Vision-based navigation: using computer vision techniques to extract information about the environment from images or other visual data, and using this information to navigate the UAV.


Ultimately, the choice of #navigation #algorithm will depend on the specific requirements of the UAV application, such as the level of accuracy required, the complexity of the environment, and the available sensors and processing power.


The Ultimate Global EV Compliance Matrix: Country‑Wise Standards for Every Component

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