Abstract
The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem.
Highlights
Nowadays, society is facing the increasing depletion of petrochemical energy, the serious destruction of the ecological environment, and increasing car ownership
Mode ton toff ton toff s s after optimization, distance = 200 km. This paper proposed another version of bird swarm algorithm (BSA), named as BSAII
In the version of BSAII algorithm, This paper proposed another version of BSA, named the version theConclusions spatial coordinates of birds in solution-space instead of as theBSAII
Summary
Society is facing the increasing depletion of petrochemical energy, the serious destruction of the ecological environment, and increasing car ownership. E-REV energy management is thatthe thedifferent use of driving conditions, the on-off time of RE for the is optimized with the target distance as the engine is as little as possible as well as keeps the vehicle running in pure electric mode. Used for powering the battery, to keep the state of charge (SOC) in the designed threshold and method like fuzzy control adopted in the energy It optimization has been used avoid Control overcharge and over discharge [4].has. Energy management strategy of E-REV based on dynamic and early closing analyzed, in optimal new European (NEDC), urbancorresponding dynamometertodriving programming was is designed, and controldriving rules ofcycle extender start-stop. Driving behavior based on prediction of vehicleprogramming speeds was was designed, and optimal control rules of electric extender start-stop integrated into the energy management of the vehicle [8].
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