Optimization of PHEV/EV Battery Charging Lawrence Wang CURENT YSP Presentations RM 525 11:00-11:25

Outline Background and Introduction Problem Statement and Technical Approach Battery charging system model and simulation Minimum loss algorithm formulation and test Conclusion and future work NISSAN LEAF NISSAN LEAF.

Background EV and PHEV will help solve energy dependency and environmental issues & are becoming popular All EV/PHEV rely on batteries One of challenges is battery charging – the success of the EV will depend on availability of charging infrastructure

Battery Charger Systems On-Board Charging System On board- size, weight. Off board- speed and efficiency should be more of a concern. EFFICIENCY IS A CONCERN. Zhang et al “Research on energy efficiency of the vehicle's battery pack arch on energy efficiency of the vehicle's battery pack,” 2011 ICEICE Off-Board Charging System

Review of Previous Charging Schemes Previous studies showed optimal charging (complex function) was very close to the CC-CV method. This very complex current is not much better. PEOPLE HAVE ALREADY RESEARCHED OPTIMAL CHARGING CURRENT, IT IS VERY COMPLEX AND ONLY ADDRESSES THE BATTERY, NOT FAR FROM CC-CV. THIS WORK PROMOTES CC-CV, BUT DID NOT ADDRESS HOW TO USE CC-CV. E. Inoa, Jin Wang, “PHEV Charging Strategies for Maximized Energy Saving,” IEEE Transactions on Vehicular Technology, 2011

Problem Statement & Approach Given a certain maximum time to charge a battery from a starting SOC to a final SOC, find the optimal current and voltage to minimize energy loss Approach Model for both battery and charger Determine total loss and charging time relationship with charging current, voltage and SOC Design an method for selection of current and voltage.

Battery Model & Verification Min Chen, G.A. Rincon-Mora, “Accurate electrical battery model capable of predicting runtime and I-V performance,” IEEE Transactions on Energy Conversion Battery model is modified to be consistent with the battery in the Nissan Leaf Electric Vehicle. Components are scaled by a factor of 96 cell units and a 66.2 Ah capacity. Verified to be consistent (16kWh) Battery model is modified to be consistent with the battery in the Nissan Leaf Electric Vehicle. Components are scaled by a factor of 96 cell units and a 66.2 Ah capacity. Simulation tests run with the model code in Matlab show results consistent with actual data. LI ION BATTERY SCALED TO MATCH NISSAN LEAF

Charger & Loss Model Charging efficiency variation with current. IGBT loss Inductor loss diode loss standard 50 KW Rectifier to match nissan leaf Charging efficiency variation with current.

Matlab Model For a given SOC Vary maximum current and voltage Calculate battery and charger power loss Integrate for energy loss End

Loss as Function of Current and SOC

Time as Function of Current and SOC

Voltage Relationship Loss vs. Current for 3 Maximum Voltages Time vs. Current for 3 Maximum Voltages The loss and time approaches an asymptote for higher maximum voltages. (E.g. setting a maximum voltage of 450 will yield the same result as 412.8V, as it cannot reach that voltage within the allotted time.)

Observation Raising the maximum voltage allows charging to be achieved faster, but also creates more loss at the same current. Ultimately, the decrease in time corresponds to a lower loss. There is a current that will lead to minimum loss. This current is consistent throughout all SOC. This means that for all currents below this minimum, it is better to use the threshold current.

Minimum Loss Algorithm

Implementation A neural network was developed as a function where t is the output time given the three inputs of maximum voltage, initial SOC, and charging current. A hidden layer between the inputs and output was comprised of ten linked neurons.

Test in Simulation # SOCinit treq (hrs) Loss (MJ) DLoss 1 20% 0.5 5.10 15.7% 2 1.0 3.53 41.7% 3 8.0 2.98 50.7% 4 50% 2.13 35.8% 5 1.74 47.6% 6 1.72 48.2%

Conclusion and Future Work A simple voltage and current selection criterion is developed in CC-CV charging mode that will result in minimal loss for a given charging time. Both battery and charger loss are considered. The algorithm tested in simulation benefits both slow and fast charging. Experimental verification will be performed in the future.

Acknowledgement CURENT faculty, staff, and graduate students

Questions