Design and Analysis of an On-Board Electric Vehicle Charger for Wide Battery Voltage Range  


The scarcity of fossil fuel and the increased pollution leads the use of Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV) instead of conventional Internal Combustion (IC) engine vehicles. An Electric Vehicle requires an on-board charger (OBC) to charge the propulsion battery. The objective of the project work is to design a multifunctional on-board charger that can charge the propulsion battery when the Electric Vehicle (EV) connected to the grid. In this case, the OBC plays an AC-DC converter. The surplus energy of the propulsion battery can be supplied to the grid, in this case, the OBC plays as an inverter. The auxiliary battery can be charged via the propulsion battery when PEV is in driving stage. In this case, the OBC plays like a low voltage DC-DC converter (LDC). An OBC is designed with Boost PFC converter at the first stage to obtain unity power factor with low Total Harmonic Distortion (THD) and a Bi-directional DC-DC converter to regulate the charging voltage and current of the propulsion battery. The battery is a Li-Ion battery with a nominal voltage of 360 V and can be charged from depleted voltage of 320 V to a fully charged condition of 420 V. The function of the second stage DC-DC converter is to charge the battery in a Constant Current and Constant Voltage manner. While in driving condition of the battery the OBC operates as an LDC to charge the Auxiliary battery of the vehicle whose voltage is around 12 V. In LDC operation the Bi-Directional DC-DC converter works in Vehicle to Grid (V2G) mode. A 1KW prototype of multifunctional OBC is designed and simulated in MATLAB/Simulink. The power factor obtained at full load is 0.999 with a THD of 3.65 %. The Battery is charged in A CC mode from 320 V to 420 V with a constant battery current of 2.38 A and the charging is switched into CV mode until the battery current falls below 0.24 A. An LDC is designed to charge a 12 V auxiliary battery with CV mode from the high voltage propulsion battery.


  1. Bi-directional DC-DC converter
  2. Boost PFC converter
  3. Electric vehicle
  4. Low voltage DC-DC converter
  5. Vehicle-to-grid.



Fig 1 Block Diagram of Power distribution in EV











Fig 2 Simulated Results of Charging operation of the propulsion battery (a)Voltage and (b)Current in Beginning Point(c)voltage and (d)current in Nominal Point (e)voltage and (f)current in Turning Point(g)voltage and (h)current in End Point

Fig 3 DC link voltage and current during G2V operation (The current is multiplied by 100 for batter visibility)

Fig 4 Voltage and Current of Auxiliary battery during charging (Current is multiplied by 10 for better visibility)


An On-Board Electric Vehicle charger is designed for level 1 charging with a 230 V input supply. Different stages of an OBC is stated and the challenges are listed. The developments have been implemented to overcome key issues. A two stage charger topology with active PFC converter at the front end followed by a Bi-directional DC-DC converter is designed. The active PFC which is a Boost converter type produces less than 5 % THD at full load. Moreover, the PFC converter is applicable to wide variation in loads. The detailed design of the power stage, as well as the controller, is discussed with the simulated results.

A second stage DC-DC converter is designed and simulated for the charging current and voltage regulation. The converter performs very precisely by charging the propulsion battery in CC/CV mode over a wide range of voltage. A V2G controller has been developed for the DC-DC converter in order to supply power to the grid from the propulsion battery. A new Low-Voltage DC-DC converter is proposed to charge the Auxiliary battery via the propulsion battery utilizing the same OBC. The battery voltage and current waveforms are presented and the performance of the designed converter is verified.


[1] “No Title,” .

[2] S. S. Williamson, Energy management strategies for electric and plug-in hybrid electric vehicles. Springer, 2013.

[3] a. Emadi and K. Rajashekara, “Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles,” IEEE Trans. Ind. Electron., vol. 55, no. 6, pp. 2237–2245, 2008.

[4] M. Yilmaz and P. T. Krein, “Review of charging power levels and infrastructure for plug-in electric and hybrid vehicles,” 2012 IEEE Int. Electr. Veh. Conf. IEVC 2012, vol. 28, no. 5, pp. 2151–2169, 2012.

[5] H. Wang, S. Dusmez, and A. Khaligh, “Design and analysis of a full-bridge LLC-based PEV charger optimized for wide battery voltage range,” IEEE Trans. Veh. Technol., vol. 63, no. 4, pp. 1603–1613, 2014.


Reduction of Commutation Torque Ripple in a Brushless DC Motor Drive



This paper describes the reduction in torque ripple due to phase commutation of brushless dc motors. With two-phase 1200 electrical conduction for the inverter connected to the conventional three-phase BLDC machine, the commutation torque ripple occurs at every 60 electrical degrees when a change over from one phase to another occurs. This effect increases the commutation time at higher speeds which increases the torque ripple. The torque ripple is reduced by changing the switching mode from 1200 to a dual switching mode with 1200 switching at lower speeds and 1800 electrical for the inverter at higher speeds.


  1. Brushless dc motor
  2. Current commutation
  3. Torque ripple
  4. Electric vehicle




Fig. 1. PWM inverter and equivalent circuit of BLDC motor



Fig.2. (a) Relative torque ripple amplitude and (b) The duration of commutation time



This paper has presented an analytical study of torque ripple comparison due to commutation of phase currents in a brushless dc motor for both 1200 and 1800 conduction modes. The results have been validated by simulation and experimental verification. In three-phase switching mode at high speeds the torque ripple and losses are minimized and therefore the efficiency of the machine is increased. But the same cannot be achieved at low speed in this mode. On the other hand, the 1200 situation is exactly opposite. Thus a composite switching scheme is proposed for satisfactory operation of the machine at all speeds. The effectiveness of the method is validated by suitable experiments.


[1] T. Li, and G. Slemon, “Reduction of cogging torque in permanent magnet motors,” IEEE Trans. on Magnetics, vol.24, no.6, pp.2901-2903, Nov. 1988.

[2] R. Carlson, M. Lajoie-Mazenc, and J.C.D.S. Fagundes, “Analysis of torque ripple due to phase commutation in brushless DC machines,” IEEE Trans. Ind. Appl., vol.28, no.3, pp. 632-638, May/Jun. 1992.

[3] H. Tan, “Controllability analysis of torque ripple due to phase commutation in brushless DC motors,” in Proc. 5th int. conf. Elect. Mach. And Syst., Aug. 18-20, 2001, vol.2, pp. 1317-1322.

[4] Y. Murai, Y. Kawase, K. Ohashi, K. Nagatake and K. Okuyama, “Torque ripple improvement for brushless DC miniature motors,” IEEE Trans. Ind. Appl., vol.25, no.3, pp. 441-450, May/Jun. 1989.

[5] C.S. Berendsen, G. Champenois, and A. Bolopion, “Commutation strategies for brushless DC motors: Influence on instant torque,” IEEE Trans. Power Electron., vol.8, no.2, pp. 231-236, Apr.1993.

The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer


With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.


  1. Battery
  2. Electric vehicle
  3. Lithium-ion (Li-ion) battery
  4. Proportional-integral (PI) observer
  5. Sliding-mode observer
  6. State of charge (SOC)



Fig. 1. Block diagram of different observer-based SOC estimation methods for Li-ion batteries. (a) Block diagram of the common structure. (b) Block diagram of a PI observer.


 Fig. 2. Identification results.

Fig. 3. UDDS current profile.

Fig. 4. SOC estimation results when the initial SOC is given.

Fig. 5. SOC estimation results when the initial SOC is unknown.


A battery SOC estimation algorithm based on a PI observer has been proposed for Li-ion batteries. Acceptable accuracy has been verified by experiments on battery bench testing for both known and unknown initial SOC. The PI-based SOC estimation has a simple structure and is easy to implement. The compensation properties of the PI observer demonstrate that a simple RC model can be utilized to model the Li-ion battery. The estimated SOC with the PI observer converges to the reference SOC quickly, and the SOC estimation errors are maintained in a small band. Most of the errors of the PI-based SOC estimation method are confined to 2% when compared with the reference SOC that is based on Coulomb counting with known initial SOC.


[1] B. Pattipati, C. Sankavaram, and K. Pattipati, “System identification and estimation framework for pivotal automotive battery management system characteristics,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 6, pp. 869–884, Nov. 2011.

[2] K. Kutluay, Y. Cadirci, Y. S. Ozkazanc, and I. Cadirci, “A new online state-of-charge estimation and monitoring system for sealed lead-acid batteries in Telecommunication power supplies,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1315–1327, Oct. 2005.

[3] M. Charkhgard and M. Farrokhi, “State-of-charge estimation for Lithiumion batteries using neural networks and EKF,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4178–4187, Dec. 2010.

[4] L. Xu, J.Wang, and Q. Chen, “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model,” Energy Convers. Manag., vol. 53, no. 1, pp. 33–39, Jan. 2012.

[5] X. Hu, F. Sun, and Y. Zou, “Estimation of state of charge of a Lithium-ion battery pack for electric vehicles using an adaptive Luenberger observer,” Energies, vol. 3, no. 9, pp. 1586–1603, 2010.