Table 3 Control algorithms in bidirectional converters

PID control

-Power flow control issues

-Minimum cost

-Minimum efficiency

Smart grid systems[57]

-Analysis of mode of operation

-Good dynamic response

-Lack of robustness in presence of disturbance and uncertainty

Electric vehicles[59],

-Reducing the time delay and dead time during switching

-High reliability

-Poor in avoiding large transients between directions

Fuel cells and satellite applications [60]-[70]

- Safeguarding the devices from over current

Sliding mode control

-Taking the account of external perturbations in large signal

-Tracking the reference signal

Exact information of state variables and parameters are required

Hybrid electric vehicle[61],

-Speed of response in defined time

DC motor control[63],

-Large variations with load and line

-Powerful against variation of parameters and external disturbances

Dc micro-grid [64], Energy storage applications [67]

- Capable enough to estimate the system under both small and large signal conditions

Dynamic evaluation control

Voltage drop is avoided to the maximum extent for the change in load current

-Robust to the variation of load signal

Division parameter appears in the estimation of duty cycle of control signal which results in complexity in the implementation of analogue control form

Ultra-capacitor-based energy storage, fuel cell system [72]

-Exact knowledge of design model is not required

-Good performance

-Well tracking of reference

Model predictive control

-Power flow control issues

-Tracking the reference signal

This model can allow the algorithm to use mathematical model of the type linear with some limitations

Hybrid power trains[72]

-Establishing load and line regulation

-Fast dynamic response

DC distributed power system[73]

-Easy to implement, this credit goes to programmable devices like DSP

Battery applications [74]

Fuzzy control

-Minimizing control time

-No mathematical knowledge of the system is required

Vague approach to design control law

Automotive systems [75], consumer electronic goods [56], domestic goods, environmental control [63]

-Better dynamic response compare to PID control and reduces steady-state error

-Fast response

Approachable only when the things are simple

Needs heavy computation while converting the linguistic control rules into its respective control actions

-Flexible in setting design rules

-Easy to design, understand and expressing control rules in human language

-It can reduce the impact of imprecise, noisy and distorted input information on control action

Artificial neural network control

-Power flow control issues

-Expert law like MPC can be removed once data sets are ready for tuning ANN

-Large complexity of network structure

Battery applications [74]

-Establishing load and line regulation

-Less computation burden

-It needs the training to operate

Automotive systems [75], Consumer electronic goods [56], domestic goods, Environmental control [63]

-Minimizing control time

-Decreases the inaccuracy of the system model even with inaccurate parameters

-For large neural network, needs huge processing time

Superior system response

-The accuracy of the trained model is about 97%

-It has to balance number of neurons used in the system and system performance in DC-DC conversion system

-Easy to implement on microprocessor-based system

-High computation rate

Digital control

-Precise small signal modelling

-Increase the efficiency with charge and discharge speed

-Cause delay with ADC processing-control loop gain crosses more than one

Hybrid electric vehicle[61],

-With inrush current protection, changing the power flow directions smoothly

-Better EMI immunity

DC motor control[63],

-Use to use

DC micro-grid [64], energy storage applications [67]

-Low cost

Power management[72]

-more flexible than analogue design

-More reliable