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.Index Terms-Battery cycle life estimation, electric vehicle (EV), hybrid energy storage system (HESS), multiobjective optimization, ultracapacitor (UC).

One of the major challenges in a battery /ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This manuscript presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc-dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle datasets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NN). The proposed online intelligent energy management controller is applied to a midsize EV. A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy.Index Terms -Electric vehicle, hybrid energy storage system, multi-objective optimization, neural networks, ultra-capacitor.

No abstract

No abstract

No abstract