Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

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. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, 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. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.

Excluding mammalian predators from diamondback terrapin nesting beaches with an electric fence

Gemstone Team Saving Testudo

Design and Evaluation of a Retrofittable Electric Snow Melting System for Pavements

Gemstone Team SnowMelt

Increasing Stability of Lithium-Ion Batteries with Ordered Graphene Silicon Negative Electrodes

Currently, lackluster battery capability is restricting the widespread integration of Smart Grids, limiting the long-term feasibility of alternative, green energy conversion technologies. Silicon nanoparticles have great conductivity for applications in rechargeable batteries, but have degradation issues due to changes in volume during lithiation/delithiation cycles. To combat this, we use electrochemical deposition to uniformly space silicon particles on graphene sheets to create a more stable structure. We found the process of electrochemical deposition degraded the graphene binding in the electrode material, severely reducing charge capacity. But, the usage of mechanically mixing silicon particles with grapheme yielded batteries better than those that are commercially available.