Systems and methods for adaptive EV charging
Systems and methods in accordance with embodiments of the invention impalement adaptive electric vehicle (EV) charging. One embodiment includes one or more electric vehicle supply equipment (EVSE); an adaptive EV charging platform, including a processor; a memory containing: an adaptive EV charging application; a plurality of EV charging parameters. In addition, the processor is configured by the adaptive EV charging application to: collect the plurality of EV charging parameters from one or more EVSEs, simulate EV charging control routines and push out updated EV charging control routines to the one or more EVSEs. Additionally, the adaptive EV charging platform is configured to control charging of EVs based upon the plurality of EV charging parameters collected from at least one EVSE.
1. An adaptive electric vehicle charging system, comprising:
one or more electric vehicle supply equipment (EVSE);
an adaptive electric vehicle (EV) charging platform, comprising:
a processor; and
a memory containing an adaptive EV charging application executable by the processor to:
collect a plurality of EV charging parameters from the one or more EVSE,
simulate EV charging control routines, and
push out updated EV charging control routines to the one or more EVSE,
wherein the adaptive EV charging platform is configured to control charging of EVs based upon the plurality of EV charging parameters collected from at least one EVSE, and
wherein the EV charging parameters include an underlying distribution of EV arrival time, session duration, and energy delivered using Gaussian mixture models (GMMs).
2. The adaptive electric vehicle charging system of claim 1 , wherein the GMMs are used to predict EV users' charging behavior.
3. The adaptive electric vehicle charging system of claim 1 , wherein the adaptive EV charging application is further executable by the processor to use the GMMs to control charging of large numbers of EVs in order to smooth a difference in electricity demand and amount of available solar energy throughout the day (Duck curve).
4. The adaptive electric vehicle charging system of claim 1 , wherein the adaptive EV charging application is further executable by the processor to train the GMMs based on a training dataset and predict a charging duration and energy delivered.
5. The adaptive electric vehicle charging system of claim 1 , wherein the GMMs are population-level GMMs (P-GMM).
6. The adaptive electric vehicle charging system of claim 1 , wherein the GMMs are individual-level GMMs (I-GMM).
7. The adaptive electric vehicle charging system of claim 1 , further comprising a power distribution network.
8. The adaptive electric vehicle charging system of claim 1 , wherein the plurality of EV charging parameters comprises EV driver laxity data.
9. The adaptive electric vehicle charging system of claim 1 , wherein the adaptive EV charging application is further executable by the processor to:
receive an EV request for charging,
determine an amount of energy and a duration for delivering the amount of energy to the EV,
optimize time-varying charging rate based on time of day and electric system load, and
synchronize with one or more EVSE to deliver optimum charge to the EV.
10. An adaptive electric vehicle charging platform, comprising:
a processor; and
a memory containing an adaptive EV charging application executable by the processor to:
collect a plurality of EV charging parameters from the one or more EVSE, simulate EV charging control routines, and push out updated EV charging control routines to the one or more EVSE,
receive an EV request for charging,
determine an amount of energy and a duration for delivering the amount of energy to the EV,
optimize time-varying charging rate based on time of day and electric system load,
synchronize with an electric vehicle charging station to deliver optimum charge to the EV, and
learn an underlying distribution of EV arrival time, session duration, and energy delivered using Gaussian mixture models (GMMs).
11. The adaptive electric vehicle charging platform of claim 10 , wherein the GMMs are used to predict EV users' charging behavior.
12. The adaptive electric vehicle charging platform of claim 10 , wherein the adaptive EV charging application is further executable by the processor to train the GMMs based on a training dataset and predict a charging duration and energy delivered.
13. The adaptive electric vehicle charging platform of claim 10 , wherein the adaptive EV charging application is further executable by the processor to learn an underlying model of an electric vehicle's battery charging behavior.
14. The adaptive electric vehicle charging platform of claim 10 , wherein the adaptive EV charging application is further executable by the processor to learn battery models based on a training dataset, and to predict a maximum charging rate and threshold state of charge for a linear 2 -stage battery model.
15. The adaptive electric vehicle charging platform of claim 10 , wherein the adaptive EV charging application is further executable by the processor to use learned battery models to simulate EV charging control routines.
16. The adaptive electric vehicle charging platform of claim 10 , wherein the adaptive EV charging application is further executable by the processor to use learned battery models to predict energy delivered.