University of California

Bingjun Xiao

Jingsheng Jason Cong

Lei He

Rajit Gadh

Chi-Cheng Chu

Ching-Yen Chung

Yiyu Shi

Shivanand Prabhu

Omar Sheikh

Siddhartha Mal

A programmable logic circuit architecture using resistive memory elements. The proposed circuit architecture uses the conventional island-based Field Programmable Gate Array (FPGA) architecture, but with novel integration of CMOS-compatible resistive memory elements that can be programmed efficiently. In the proposed architecture, the programmable interconnects of FPGA are redesigned to use only resistive memory elements and metal wires. Then, the interconnects can be entirely fabricated over logic blocks to save area while keeping their architectural functions unchanged, and the programming transistors can be shared among resistive memory elements to save area. Finally, on-demand buffer insertion is proposed as the buffering solution to achieve more speedup.

Programmable logic circuit architecture using resistive memory elements

An expert system manages a power grid wherein charging stations are connected to the power grid, with electric vehicles connected to the charging stations, whereby the expert system selectively backfills power from connected electric vehicles to the power grid through a grid tie inverter (if present) within the charging stations. In more traditional usage, the expert system allows for electric vehicle charging, coupled with user preferences as to charge time, charge cost, and charging station capabilities, without exceeding the power grid capacity at any point. A robust yet accurate state of charge (SOC) calculation method is also presented, whereby initially an open circuit voltage (OCV) based on sampled battery voltages and currents is calculated, and then the SOC is obtained based on a mapping between a previously measured reference OCV (ROCV) and SOC. The OCV-SOC calculation method accommodates likely any battery type with any current profile.

Smart electric vehicle (EV) charging and grid integration apparatus and methods

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Los Angeles, CA, United States of America

Bingjun Xiao