Vehicle Charging Optimization Based on Particle Swarm Optimization - Matlab Realization
As electric vehicles gradually enter people's lives, the problem of vehicle charging has attracted more and more attention. How to optimize vehicle charging strategy and reduce charging cost and time has always been a difficult problem for the electric vehicle industry. This article will introduce a vehicle charging optimization algorithm based on particle swarm optimization, and use Matlab to implement it.
- Particle Swarm Optimization (PSO)
Particle swarm optimization algorithm is a meta-heuristic optimization algorithm, which simulates the behavior of birds foraging, and finds the optimal solution through continuous search and iteration. The algorithm is based on particle swarms, and each particle represents a solution. During the optimization process, each particle continuously updates its position and velocity according to its own historical optimal solution, global optimal solution, and neighbor's optimal solution, hoping to find a better solution.
- Vehicle Charging Optimization
In order to optimize vehicle charging schemes, we need to understand the basic concepts and issues of vehicle charging. Here we assume an electric vehicle charging station that can charge a large number of electric vehicles at the same time. The problem we want to solve is: how to minimize the charging cost and time of electric vehicles, so that all electric vehicles can be fully charged within the specified time.
To solve this problem, we need to know the basic model and constraints of EV charging. The charging model of electric vehicles can be simplified as a linear programming (Linear Programming) problem, which we will not introduce too much here. Here we only give the main constraints for vehicle charging optimization:
(1) The number of electric vehicle charging piles is limited;
(2) The charging rate of each charging pile is different;
(3) The power supply capacity and power supply time of charging piles are limited;
(4) The arrival time, departure time, charging start time and charging end time of each EV are known.
- Algorithm implementation
We transform the vehicle charging optimization problem into a particle swarm optimization problem, and