IEEE Transactions on Industrial Informatics · 2014 · 424 citations · 24 references
EngineeringAutomotive EngineeringElectric Vehicle EngineeringHybrid Vehicle SystemsOptimizationBattery SystemsElectric Vehicle ArchitectureElectric Vehicle SimulationEnergy StorageEnergy System ModelingPower Electronics For Hybrid VehiclesBattery Cycle LifeBattery Energy StorageEnergy Storage OptimizationEnergy Storage System ModelingEnergy Storage System OptimizationGlobal Search AlgorithmsHybrid Energy Storage SystemsPeak Power Operation
EV batteries degrade from high peak power and harsh cycles in urban driving, and while oversized ESS can meet power demands, it increases size, volume, and cost. The study aims to reduce overall ESS size and extend battery cycle life by employing a battery/ultracapacitor hybrid ESS that uses ultracapacitors as a power buffer. A multiobjective optimization framework using a sample‑based DIRECT algorithm and an Autonomie simulation model was developed to size the HESS and maximize battery cycle life for a midsize EV. The optimized HESS achieved a 76 % extension of battery cycle life under the UDDS, using 72 ultracapacitor cells, outperforming a battery‑only ESS.
Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/ discharging cycles during acceleration and deceleration periods, particularly in Urban driving conditions. Oversized energy storage system (ESS) meets the high power demand; however, in tradeoff with increased ESS size, volume, and cost. In order to reduce overall ESS size and extend battery cycle life, battery/ultracapacitor (UC) hybrid ESS (HESS) has been considered as a solution in which UCs act as a power buffer to charging/discharging peak power. In this paper, a multiobjective optimization problem is formulated to minimize the overall ESS size, while maximizing the battery cycle life according to the assigned penalty functions. An integrated framework for HESS sizing and battery cycle life optimization applied in a midsize EV, using an Autonomie simulation model, is described and illustrated in this paper. This multidimensional optimization is realized by a sample-based global search oriented DIviding RECTangles (DIRECT) algorithm. The optimization results under Urban Dynamometer Driving Schedule (UDDS) are compared with the battery-only ESS results, which demonstrate significant battery cycle life extension of 76% achieved by the optimized HESS with 72 UC cells.
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