Because of the jamming signal is real-time changeable and control algorithm cannot timely tracking control flywheel rotor, this paper takes vehicle maglev flywheel battery as the research object. One kind of dual-model control strategy is developed based on the analysis of the vibration response impact of the flywheel battery control system. In view of the complex foundation vibration problems of electric vehicles, the nonlinear dynamic simulation model of vehicle maglev flywheel battery is solved. Through analyzing the nonlinear vibration response characteristics, one kind of dual-mode adaptive hybrid control strategy based on

As the future main traffic tools, electric vehicle (EV) is required in the performance of starting, acceleration, and climbing; however, this performance depends largely on the power battery performance [

Maglev flywheel can be applied in EV electric power system, aerospace, and other fields because of having high specific energy, high power, fast charge and discharge, long service life, no waste gas pollution, environment-friendly advantages, and so on [

For the scientific research and practical application of maglev flywheel, a dual-mode adaptive hybrid control strategy is studied based on

The below force and movement differential equation of the flywheel are discussed in order to solve the maglev flywheel nonlinear dynamic model. Figure

Flywheel below force analysis.

In Figure

For the convenience of analysis, type (

The state space of the flywheel system transfer function matrix

So far, the nonlinear dynamic model

To improve the control stability and the energy storage density of vehicle maglev flywheel, a dual-mode switch control strategy is study based on

The dual-mode switch control strategy diagram.

The basic response is a random signal when EV is driving on the bumpy road or start-stop, acceleration-deceleration, and steering. To reduce the random impact and improve the control robustness, a

A kind of

The diagram of

In Figure

The corresponding sensitivity and complementary sensitivity matrix functions

The structural parameters of the maglev flywheel are calculated and got as

In addition,

Because the maglev flywheel is axially symmetric distribution in four radial freedom degrees, the four radial discrete transfer functions are the same. In order to make the control cycle consistent with the actual flywheel control system, the simulation sampling frequency is selected as 20000 Hz.

To reduce the radial run-out of the flywheel and solve the problem of variable speed, the AILC algorithm is adopted as the feed-forward controller to implement vibratory displacement compensation [

AILC compensation principle of AMB system.

To improve control performance and enhance the convergence rate of the learning law, there are two modifications in AILC. The first one is enhancing the error information action of previous control period and the second one is proposing a novel impacting factor

The functions of AILC can be introduced by the iterative formulas in discrete domain. The error formula is given.

The update learning law of AILC is summarized as

To understand the action of AILC better, the control input signal of generalized plant is written as

The discrete transfer function of the learning law of (

The PID controller with incomplete differential part is given in time domain:

The discrete function of (

The

The discrete function of error signal can be obtained as

Putting (

Equation (

If

Equation (

That is, the controller signal

The convergence of AILC has been demonstrated according to (

According to the start-up time and the variability of the rotor frequency from static suspension to one fixed speed, the equation of

First, the stability of

Sensitivity, complementary sensitivity, and the corresponding weighted function singular value relations.

The choice principle of

Second, the simulation parameters of AILC are selected as

Flywheel trajectory with AILC compensation.

Rotor position orbit

Rotor run-out in axis

The flywheel has a regular circular trajectory in Figure

In Figure

Flywheel displacement and control current in start-up process by

Figure

Flywheel displacement and control current in rotating process by

The compensated effect by AILC algorithm at 600 Hz is shown in Figure

Flywheel displacement and control current in AILC compensation process at 600 Hz.

Figure

EV primary battery output power in different modes.

Through analyzing the nonlinear dynamic characteristics and the application on EV, one kind of dual-model control strategy based on

The authors declare that they have no conflict of interests regarding this paper.

This work is supported by the National Natural Science Foundation of China (51405244), the China Postdoctoral Science Foundation (2014M551634), and the Jiangsu Province Natural Science Foundation of China (BK20140880). The authors would like to thank the editor and the reviewers for their constructive comments and suggestions to improve the quality of the paper.