A Novel Design of Hybrid Energy Storage System for Electric Vehicles

ABSTRACT:

 In order to provide long distance endurance and ensure the minimization of a cost function for electric vehicles, a new hybrid energy storage system for electric vehicle is designed in this paper. For the hybrid energy storage system, the paper proposes an optimal control algorithm designed using a Li-ion battery power dynamic limitation rule-based control based on the SOC of the super-capacitor. At the same time, the magnetic integration technology adding a second-order Bessel low-pass filter is introduced to DC-DC converters of electric vehicles. As a result, the size of battery is reduced, and the power quality of the hybrid energy storage system is optimized. Finally, the effectiveness of the proposed method is validated by simulation and experiment.

 KEYWORDS:

1.      Hybrid energy storage system

2.      Integrated magnetic structure

3.      Electric vehicles

4.      DC-DC converter

5.      Power dynamic limitation

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig.1 Topology of the hybrid energy storage system

EXPECTED SIMULATION RESULTS:

 

(a) Power command and actual power

 

(b) Power of the super-capacitor and Li-ion battery

Fig.2 Simulation results of the proposed HESS

(a) Battery current

 

(b) Super-capacitor current

 

(c) Load current

 

(d) Load voltage

Fig.3 Simulation results of the proposed HESS applied on electric vehicles

 

CONCLUSION:

In this paper, a new hybrid energy storage system for electric vehicles is designed based on a Li-ion battery power dynamic limitation rule-based HESS energy management and a new bi-directional DC/DC converter. The system is compared to traditional hybrid energy storage system, showing it has significant advantage of reduced volume and weight. Moreover, the ripple of output current is reduced and the life of battery is improved.

REFERENCES:

[1] Zhikang Shuai, Chao Shen, Xin Yin, Xuan Liu, John Shen, “Fault analysis of inverter-interfaced distributed generators with different control schemes,” IEEE Transactions on Power Delivery, DOI: 10. 1109/TPWRD. 2017. 2717388.

[2] Zhikang Shuai, Yingyun Sun, Z. John Shen, Wei Tian, Chunming Tu, Yan Li, Xin Yin, “Microgrid stability: classification and a review,” Renewable and Sustainable Energy Reviews, vol.58, pp. 167-179, Feb. 2016.

[3] N. R. Tummuru, M. K. Mishra, and S. Srinivas, “Dynamic energy management of renewable grid integrated hybrid energy storage system, ” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7728-7737, Dec. 2015.

[4] T. Mesbahi, N. Rizoug, F. Khenfri, P. Bartholomeus, and P. Le Moigne, “Dynamical modelling and emulation of Li-ion batteries- supercapacitors hybrid power supply for electric vehicle applications, ” IET Electr. Syst. Transp., vol.7, no.2, pp. 161-169, Nov. 2016.

[5] A. Santucci, A. Sorniotti, and C. Lekakou, “Power split strategies for hybrid energy storage systems for vehicular applications, ” J. Power Sources, vol. 258, no.14, pp. 395-407, 2014.

Seven-Level Inverter with Reduced Switches for PV System Supporting Home-Grid and EV Charger

ABSTRACT:

This paper proposes a simple single-phase new pulse-width modulated seven-level inverter architecture for photovoltaic (PV) systems supporting home-grid with electric vehicle (EV) charging port. The proposed inverter includes a reduced number of power components and passive elements size, while showing less output-voltage total harmonic distortion (THD), and unity power factor operation. In addition, the proposed inverter requires simple control and switching strategies compared to recently published topologies. A comparative study was performed to compare the proposed inverter structure with the recent inverter topologies based on the number of components in the inverter circuit, number of components per output-voltage level, average number of active switches, THD, and operating efficiency as effective parameters for inverter performance evaluation. For design and validation purposes, numerical and analytical models for a grid-tied solar PV system driven by the proposed seven-level inverter were developed in MATLAB/Simulink environment. The inverter performance was evaluated considering grid-integration and stand-alone home with level-2 AC EV charger (3–6 kW). Compared with recently published topologies, the proposed inverter utilizes a reduced number of power components (7 switches) for seven-level terminal voltage synthesis. An experimental prototype for proposed inverter with the associated controller was built and tested for a stand-alone and grid-integrated system. Due to the lower number of ON-switches, the inverter operating efficiency was enhanced to 92.86% with load current THD of 3.43% that follows the IEEE standards for DER applications.

KEYWORDS:

1.      DC-AC converter

2.      Electric vehicles

3.      Home grid

4.      Maximum power point tracking (MPPT)

5.      Multilevel inverter

6.      Photovoltaic (PV) system

7.      Seven-level inverter

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:  

Figure 1. Circuit configuration of solar PV system in integrated with the grid and EV loads via the proposed 7level-inverter.

 EXPECTED SIMULATION SYSTEM:

(a) Solar irradiation

(b) PV current

(c) PV voltage

Figure 2. Cont

time(s)

(d)PV power

Figure 3. the pv panel current, voltage, and power.

 Figure 4. multi- Level inverter output voltage.

Figure 5. the injected current, voltage, and power variation. (a) Grid voltage and current; (b) Grid injected power.

 

Figure 6. The reference and actual injected currents of the seven-level inverter at irradiance variation.

 

Figure 7. Simulation results of the proposed 7-level inverter as level-2 EV charger (240 V, 3:6 kW); (a) loading profile, (b) multilevel output voltage, and (c) inverter voltage/pulsating current

Figure 8. Simulation results of the proposed 7-level inverter for house loads voltage control (2 kW). (a) Load reference and actual voltages, (b) Load voltage and current

CONCLUSION:

 This paper has presented a new topology of a single-phase seven-level inverter as an interface for grid-integrated and stand-alone solar PV systems. The circuit configuration This paper has presented a new topology of a single-phase seven-level inverter as an interface for grid-integrated and stand-alone solar PV systems. The circuit configuration This paper has presented a new topology of a single-phase seven-level inverter as an interface for grid-integrated and stand-alone solar PV systems. The circuit configuration and operation principle of the proposed inverter have been presented in detail a long with    the switching patterns and control strategy. A comparative study between the proposed inverter structure and the recent MLI topologies is enriched to reveal the features of the proposed inverter. The proposed MLI structure considers a reduced number of power switches, NC/L, and NAVG/Pole, which enhances the inverter operating efficiency and decreases its cost. Only seven switches have been utilized to synthesis voltage waveform of seven levels at the output terminals. The performance of the proposed inverter and associated control was investigated for grid-integrated and stand-alone PV systems based on simulation and experimental tests. The test platform includes a boost converter with MPPT control, which feeds the front-end of the proposed MLI. The results show that the proposed inverter exhibits an improved steady state response, and minimum settling time (i.e., 5 ms). THD of both voltage and current waveforms during grid-integration and stand-alone operations is 3.43%, which follows the IEEE-1547 harmonic standards for DER applications. In addition, the inverter offers a high operating efficiency of 92.86%, compared to most of the recently published topologies surveyed in this paper.

REFERENCES:

1. Solangi, K.; Islam, M.; Saidur, R.; Rahim, N.; Fayaz, H. A review on global solar energy policy. Renew. Sustain. Energy Rev. 2011, 15, 2149–2163. [CrossRef]

2. Ali, A.I.; Sayed, M.A.; Mohamed, E.E. Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. Int. J. Electr. Power Energy Syst. 2018, 99, 192–202. [CrossRef]

3. Sayed, M.A.; Mohamed, E.; Ali, A. Maximum Power Point Tracking Technique for Grid tie PV System. In Proceedings of the 7th International Middle-East Power System Conference, (MEPCON’15), Mansoura University, Dakahlia Governorate, Egypt, 15–17 December 2015.

4. Ali, A.I.; Mohamed, E.E.; Sayed, M.A.; Saeed, M.S. Novel single-phase nine-level PWM inverter for grid connected solar PV farms. In Proceedings of the 2018 International Conference on Innovative Trends in Computer Eng. (ITCE), Aswan, Egypt, 19–21 February 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 345–440.

5. Youssef, A.-R.; Ali, A.I.; Saeed, M.S.; Mohamed, E.E. Advanced multi-sector P&O maximum power point tracking technique for wind energy conversion system. Int. J. Electr. Power Energy Syst. 2019, 107, 89–97.

 

Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent +

ABSTRACT:

The field‐oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI‐type controllers. In addition to their low complexity (an advantage for real‐time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of id and iq currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor‐load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC‐type strategy, both when using simple PI‐type controllers or in the case of complex SMC or synergetic‐type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC‐type strategy, both in the case of simple PI‐type controllers and complex SMC or synergetic‐type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL‐DDPG.

KEYWORDS:

1.      Permanent magnet synchronous motor

2.      Sliding mode control

3.      Synergetic control

4.      Reinforcement learning

5.      Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Figure 1. Block diagram for FOC‐type control of the PMSM based on PI‐type controllers using RL.

EXPECTED SIMULATION RESULTS:

 

 

Figure 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC‐type strategy.

Figure 3. Time evolution for the numerical simulation of the PMSM control system based on the RL‐TD3 agent for the correction of iqref.

Figure 4. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref and uqref.

Figure 5. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref, uqref, and iqref.

Figure 6. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers.

Figure 7. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers using an RL‐TD3 agent for the correction of iqref.

 

CONCLUSION:

This paper presents the FOC‐type control structure of a PMSM, which is improved in terms of performance by using a RL technique. Thus, the comparative results are presented for the case where the RL‐TD3 agent is properly trained and provides correction signals that are added to the control signals ud, uq, and iqref. The FOC‐type control structure for the PMSM control based on an SMC speed controller and synergetic current controller is also presented. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by the RL on process control can also be used. This improvement is obtained using the correction signals provided by a trained RL‐TD3 agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The parametric robustness of the proposed PMSM control system is proved by very good control performances achieved even when the uniformly distributed noise is added to the load torque TL, and under high variations in the load torque TL and the moment of inertia J. Numerical simulations are used to prove the superiority of the control system that uses the RL‐TD3 agent.

REFERENCES:

1. Eriksson, S. Design of Permanent‐Magnet Linear Generators with Constant‐Torque‐Angle Control for Wave Power. Energies 2019, 12, 1312.

2. Ouyang, P.R.; Tang, J.; Pano, V. Position domain nonlinear PD control for contour tracking of robotic manipulator. Robot. Comput. Integr. Manuf. 2018, 51, 14–24.

3. Baek, S.W.; Lee, S.W. Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles. Appl. Sci. 2020, 10, 3235.

4. Amin, F.; Sulaiman, E.B.; Utomo, W.M.; Soomro, H.A.; Jenal, M.; Kumar, R. Modelling and Simulation of Field Oriented Control based Permanent Magnet Synchronous Motor Drive System. Indones. J. Electr. Eng. Comput. Sci. 2017, 6, 387.

5. Mohd Zaihidee, F.; Mekhilef, S.; Mubin, M. Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review. Energies 2019, 12, 1669.

Improvement of PMSM Control Using Reinforcement Learning Deep Deterministic Policy Gradient Agent

ABSTRACT:

Based on the advantage of using the reinforcement learning on process control, provided by the fact that it is not necessary to know the exact mathematical model and the structure of its uncertainties, this article approaches the possibility of improving the performances of the Permanent Magnet Synchronous Motor (PMSM) control system based on the Field Oriented Control (FOC) type control strategy, by using the correction signals provided by a trained reinforcement learning agent, which will be added to the control signals ud, uq, and iqref . The type of reinforcement learning used is the Deep Deterministic Policy Gradient (DDPG). The combination possibilities of these control structures are presented, and their superiority over the FOC type control strategy is validated by numerical simulations.

KEYWORDS:

1.      Permanent magnet motors

2.      Field oriented control

3.      Reinforcement learning

4.      Intelligent agent

5.      Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig. 1. Block diagram for FOC-type control of the PMSM based on reinforcement learning.

EXPECTED SIMULATION RESULTS:

Fig. 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC-type strategy.

 

Fig. 3. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref and uqref .

 

Fig. 4. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of iqref.

 

Fig. 5. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref, uqref and iqref.

 

CONCLUSION:

This article presents the FOC-type control structure of a PMSM, which is improved in terms of performance by using a reinforcement learning technique. Thus, the comparative results are presented for the case where the reinforcement learning agent is properly trained and provides correction signals that will be added to the control signals ud, uq, and iqref. Numerical simulations are used to demonstrate the superiority of the control system that uses the reinforcement learning, and the following papers will study the possibilities for optimization in terms of the implementation of the reinforcement learning in the PMSM control.           

REFERENCES:

[1] B. Wu and M. Narimani, Control of Synchronous Motor Drives, in High-Power Converters and AC Drives , Wiley-IEEE Press, 2017, pp.353-391.

[2] B. K. Bose, Modern power electronics and AC drives, Prentice Hall, Knoxville, Tennessee, USA, 2002.

[3] H. Wang and J. Leng, “Summary on development of permanent magnet synchronous motor,” Chinese Control And Decision Conference (CCDC), Shenyang, China, 2018, pp. 689-693.

[4] Z. Liu, Y. Li, and Z. Zheng, “A review of drive techniques for multiphase machines,” in CES Transactions on Electrical Machines and Systems, vol. 2, pp. 243-251, June 2018.

[5] S. Sakunthala, R. Kiranmayi, and P. N. Mandadi, “A Review on Speed Control of Permanent Magnet Synchronous Motor Drive Using Different Control Techniques,”International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, China , 2018, pp. 97-102.