emobility-smart-charging | Smart charging algorithms with REST API
kandi X-RAY | emobility-smart-charging Summary
kandi X-RAY | emobility-smart-charging Summary
emobility-smart-charging is a Java library. emobility-smart-charging has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
This repository is an implementation of smart charging for electric vehicles (EVs). It contains a charging optimizer which schedules EVs for charging throughout the day. The optimization algorithm addresses the following goals:. Refer to [1] for a detailed explanation of the algorithm.
This repository is an implementation of smart charging for electric vehicles (EVs). It contains a charging optimizer which schedules EVs for charging throughout the day. The optimization algorithm addresses the following goals:. Refer to [1] for a detailed explanation of the algorithm.
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Quality
Security
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emobility-smart-charging has a low active ecosystem.
It has 25 star(s) with 26 fork(s). There are 7 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 4 have been closed. On average issues are closed in 3 days. There are 14 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of emobility-smart-charging is current.
Quality
emobility-smart-charging has 0 bugs and 0 code smells.
Security
emobility-smart-charging has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
emobility-smart-charging code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
emobility-smart-charging is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
emobility-smart-charging releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
It has 21101 lines of code, 1545 functions and 312 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed emobility-smart-charging and discovered the below as its top functions. This is intended to give you an instant insight into emobility-smart-charging implemented functionality, and help decide if they suit your requirements.
- Entry point for simulation
- Get data generator
- Initialize CLI arguments
- Get the raw key value
- Invoked when a charging station arrives
- Reschedules a carlots for a given charging station
- Fills the battery plan with the given charging station
- Handle a single violation
- Generates a list of car process data using a given density distribution function
- Deserializes state from JSON
- Starts the simulation
- Clone measurement
- Entry point for testing
- This method is called when the measurement thread runs
- This method is called when a measurement is available
- Testing
- Applies a measurement to the database
- This method is called when the data generation is done
- This method is called when a new measurement is done
- Build a JSON representation of the solution
- This method is called when a measurement is received
- Main method
- Main method
- Process a performance measurement
- Start the simulation
- Main method for testing
Get all kandi verified functions for this library.
emobility-smart-charging Key Features
No Key Features are available at this moment for emobility-smart-charging.
emobility-smart-charging Examples and Code Snippets
No Code Snippets are available at this moment for emobility-smart-charging.
Community Discussions
No Community Discussions are available at this moment for emobility-smart-charging.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install emobility-smart-charging
The simplest way to run this application is to use the public Docker image. First, pull the Docker image:.
-d Detached mode: Run container in the background
-p Publish a container's port to the host: Change the first port in 8080:8080 to adjust which port you want the application to run on
-t Tag the image with a name
The purpose of this section is to get you started on using the charging optimizer API. The easiest way to understand the interface of the API is to tinker with the playground (/playground/index.html). The playground is a visual interface which lets you edit the input for the charging optimizer in a natural way. The playground translates your model into a JSON request which is the technical input to the charging optimizer. You can easily pick up how to assemble JSON requests for the optimizer by observing how your playground input is reflected in the generated request.
Current time: This is the actual time of day assumed by the optimizer. The optimizer can only schedule charging sessions after the current time, not before. By default, the playground uses midday as current time.
Charging infrastructure: The charging infrastructure consists of a hierarchy of fuses reflecting the technical installation of the charging hardware. In real life, fuses are installed in a tree structure. There is typically one fuse per charging station, another fuse for a set of charging stations, and then further fuses for sets of fuses. By default, the playground contains a charging infrastructure with two levels of fuses to illustrate the concept of the tree structure.
Fuse: Each fuse is characterized by the current at which the fuse cuts off the power supply. The charging optimizer assumes three-phase electrical circuits. Therefore, each fuse is defined by a triplet of current values, one per phase. The playground lets you add further fuses to the infrastructure by clicking the corresponding buttons. By default, the playground uses 32 Ampere per phase for new fuses.
Charging station: Each charging station is characterized by the current at which the built-in fuse cuts off the power supply. The playground lets you add further charging stations by clicking the corresponding buttons. By default, the playground uses charging stations with 32 Ampere fuses.
Car: In the playground, cars can be added to charging stations to express their arrival at the charging station. When you add cars via the corresponding button, semantically you create a charging demand. In the charging optimizer, the cars with their charging demands are the central items for the optimization process. The charging optimizer creates one charge plan per car. Therefore you need to have at least one car in your input for the charge optimizer to create a non-trivial output. The more cars you add to the input, the higher becomes the competition for the scarce resource of charging current. With more cars, the available charging capacity is divided and more cars are assigned only partial or no charging opportunities. When you check out the generated JSON request you will notice the long list of parameters per car.
-d Detached mode: Run container in the background
-p Publish a container's port to the host: Change the first port in 8080:8080 to adjust which port you want the application to run on
-t Tag the image with a name
The purpose of this section is to get you started on using the charging optimizer API. The easiest way to understand the interface of the API is to tinker with the playground (/playground/index.html). The playground is a visual interface which lets you edit the input for the charging optimizer in a natural way. The playground translates your model into a JSON request which is the technical input to the charging optimizer. You can easily pick up how to assemble JSON requests for the optimizer by observing how your playground input is reflected in the generated request.
Current time: This is the actual time of day assumed by the optimizer. The optimizer can only schedule charging sessions after the current time, not before. By default, the playground uses midday as current time.
Charging infrastructure: The charging infrastructure consists of a hierarchy of fuses reflecting the technical installation of the charging hardware. In real life, fuses are installed in a tree structure. There is typically one fuse per charging station, another fuse for a set of charging stations, and then further fuses for sets of fuses. By default, the playground contains a charging infrastructure with two levels of fuses to illustrate the concept of the tree structure.
Fuse: Each fuse is characterized by the current at which the fuse cuts off the power supply. The charging optimizer assumes three-phase electrical circuits. Therefore, each fuse is defined by a triplet of current values, one per phase. The playground lets you add further fuses to the infrastructure by clicking the corresponding buttons. By default, the playground uses 32 Ampere per phase for new fuses.
Charging station: Each charging station is characterized by the current at which the built-in fuse cuts off the power supply. The playground lets you add further charging stations by clicking the corresponding buttons. By default, the playground uses charging stations with 32 Ampere fuses.
Car: In the playground, cars can be added to charging stations to express their arrival at the charging station. When you add cars via the corresponding button, semantically you create a charging demand. In the charging optimizer, the cars with their charging demands are the central items for the optimization process. The charging optimizer creates one charge plan per car. Therefore you need to have at least one car in your input for the charge optimizer to create a non-trivial output. The more cars you add to the input, the higher becomes the competition for the scarce resource of charging current. With more cars, the available charging capacity is divided and more cars are assigned only partial or no charging opportunities. When you check out the generated JSON request you will notice the long list of parameters per car.
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Please use the GitHub issue tracker for any questions, bug reports, feature requests, etc.
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