A Reconfigurable PV Array Scheme Integrated Into an Electric Vehicle

Author(s):  
P.M. Armstrong ◽  
D.A. Howey ◽  
R.W. Armstrong ◽  
R. Camilleri ◽  
M.D. McCulloch ◽  
...  
2020 ◽  
Vol 10 (3) ◽  
pp. 291-300
Author(s):  
Sujitha Nachinarkiniyan ◽  
Krithiga Subramanian

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5120 ◽  
Author(s):  
Olga Kanz ◽  
Angèle Reinders ◽  
Johanna May ◽  
Kaining Ding

This paper presents a life cycle assessment (LCA) of photovoltaic (PV) solar modules which have been integrated into electric vehicle applications, also called vehicle integrated photovoltaics (VIPV). The LCA was executed by means of GaBi LCA software with Ecoinvent v2.2 as a background database, with a focus on the global warming potential (GWP). A light utility electric vehicle (LUV) named StreetScooter Work L, with a PV array of 930 Wp, was analyzed for the location of Cologne, Germany. An operation time of 8 years and an average shadowing factor of 30% were assumed. The functional unit of this LCA is 1 kWh of generated PV electricity on-board, for which an emission factor of 0.357 kg CO2-eq/kWh was calculated, whereas the average grid emissions would be 0.435 kg CO2-eq/kWh. Hence, charging by PV power hence causes lower emissions than charging an EV by the grid. The study further shows how changes in the shadowing factor, operation time, and other aspects affect vehicle’s emissions. The ecological benefit of charging by PV modules as compared to grid charging is negated when the shadowing factor exceeds 40% and hence exceeds emissions of 0.435 kg CO2-eq/kWh. However, if the operation time of a vehicle with integrated PV is prolonged to 12 years, emissions of the functional unit go down to 0.221 kg CO2-eq/kWh. It is relevant to point out that the outcomes of the LCA study strongly depend on the location of use of the vehicle, the annual irradiation, and the carbon footprint of the grid on that location.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.


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