A Robust Regenerative-Braking Control of Induction Motors for EVs Applications

Youssef, Omar E. M.; Hussien, Mohamed G.; El-Wahab Hassan, Abd

doi:https://doi.org/10.1155/2024/5526545

International Transactions on Electrical Energy Systems

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Abstract Introduction Results and Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 5526545 | https://doi.org/10.1155/2024/5526545

A Robust Regenerative-Braking Control of Induction Motors for EVs Applications

Omar E. M. Youssef,^1,2Mohamed G. Hussien ,^3,4and Abd El-Wahab Hassan^2,4

Academic Editor: Flah Aymen

Received21 Aug 2023

Revised07 Dec 2023

Accepted01 Feb 2024

Published12 Feb 2024

Abstract

EVs suffer from short driving range because of limited capacity of the battery. An advantage of EVs over internal-combustion vehicles is the ability of regenerative braking (RB). By this advantage, EVs can develop energy by RB which can be stored in the battery for later use to increase the driving range of EVs. There are different motors that can be used in EVs, and the control during RB mode is dedicated for certain motor types. However, the previous studies for EV-based IM drives consider the motor-speed control without considering its RB. This paper proposes a robust control of induction motor (IM) during RB mode of EVs. The proposed control system is simple and depends only on mathematical calculations. The obtained results confirm the effectiveness and accuracy of the suggested control strategy with a good dynamic behavior under different operating conditions. Also, the results assure the robustness of control capabilities under parameters uncertainties during the RB mode of EV-based IM drives.

1. Introduction

Electric vehicles (EVs) play a pivotal role in mitigating carbon emissions and fostering a low-carbon future. By relying on electricity as a power source, EVs significantly reduce reliance on traditional fossil fuels, thereby lowering the carbon footprint associated with transportation [1]. The integration of renewable energy sources for electricity generation further enhances the environmental benefits of EVs. As the grid becomes increasingly powered by renewable energy, the overall lifecycle emissions of electric vehicles continue to decrease [2, 3]. This symbiotic relationship between electric vehicles and a low-carbon future underscores the transformative potential of clean transportation in the global effort to combat climate change. The adoption and advancement of electric vehicles represent a crucial step towards achieving sustainable, low-carbon mobility on a large scale.

The integration of autonomous driving technology in electric vehicles represents a significant leap forward in enhancing overall efficiency and sustainability in the automotive industry. By combining the advancements in electric propulsion with autonomous capabilities, these vehicles offer a seamless and intelligent transportation solution [4]. Autonomous electric vehicles (AEVs) have the potential to optimize energy consumption, improve traffic flow, and reduce environmental impact [5, 6]. The efficiency gains stem from the ability of autonomous systems to optimize route planning, adapt driving behavior to real-time traffic conditions, and maximize energy recovery through regenerative braking. Moreover, autonomous driving can enhance safety by mitigating human errors, leading to a reduction in accidents. This synergy between autonomy and electric propulsion not only redefines the driving experience but also contributes to a more sustainable and efficient future for urban mobility.

Efforts to replace internal-combustion-engine vehicles with electric vehicles (EVs) are motivated by environmental concerns which encourage increasing of renewable energy usage and decreasing of fossil fuel consumption [7, 8]. EVs can depend on renewable energy sources, and propulsion of EVs is through powertrains of higher efficiency [9, 10]. A weakness of EVs is the relatively short driving range because of limited capacity of the battery [11, 12]. This weakness can be alleviated by increasing the efficiency of EVs using regenerative braking (RB), which is an advantage of EV over internal-combustion vehicle. Using RB, the kinetic energy of the vehicle can be converted to electrical energy, instead of wasted as heat by friction braking, and this energy is stored in the battery for later use [9]. During braking of EVs, the braking force is applied on both the front axle and rear axle of the vehicle [13, 14]. The division of the braking force between the two axles may be according to the ideal braking-force-distribution curve for best stability, or other braking-force distribution methods for improved recovered braking energy [15, 16].

In general, braking systems include both the friction braking and RB. This is because RB is insufficient at high braking requirements and at low ability of the battery to store the generated energy, where the regeneration is inappropriate once the battery is fully charged [17, 18]. For emergency braking, only the friction braking is used to obtain a safe braking, and there is no recovered energy [17, 18]. Also, the regeneration is inappropriate at low-speed values because the power is supplied by the battery instead of stored in it. The low-speed cutoff point (LSCP), below which RB is disabled, may be taken as a fixed value, or a varied value according to the operating conditions.

For a front-wheel drive EV, the rear braking force is only exerted by friction braking, and the front-braking force is shared between the friction barking and RB, where the demanded RB force is bounded according to the maximum value of the motor-braking torque. The general configuration of regenerative-braking strategies of the front-wheel drive EV can be as shown in Figure 1 [19, 20]. By pressing the braking pedal, the value of so-called barking intensity (z) is determined, and this value is used to determine the braking-force distribution between the front axle and rear axle of the vehicle. The rear braking force () is a friction force, while the front-braking force () is divided into friction-front-braking force () and regenerative-braking force (f_f,reg) by a front-braking-force distribution controller. The operation of this controller is depending on braking situation (emergency braking or not), and the values of the speed, state of charge and maximum motor-braking torque (T_max), which is corresponding to the force value F_max. The operation of this controller can be as shown in Figure 2. The force f_f,reg is used to estimate regenerative-braking torque which is the reference torque (T_ref) used by the motor control. The motor control is a regenerative-braking control that is dedicated for a certain motor. In [21], the motor-regenerative-braking control is optimized for switched reluctance motor (SRM). In [7, 20], the control is enhanced for brushless dc (BLDC) motor, and in [22], it is enhanced for permanent-magnet synchronous motor (PMSM). However, it seems that there are no thoughtful attempts to obtain dedicated control for induction motor during regenerative braking, although there is dedicated speed control such as in [13, 23].

This paper proposes a control scheme of three-phase induction motor (IM) for the regenerative-braking operation of EVs. The adopted control scheme is simple, which leads to easy implementation. Also, there is no need of core machine parameters in the control procedure, and this will increase the robustness of the control performance during the regenerative-braking mode. Moreover, the control of induction motor during the regenerative-braking mode has not been considered in the previous studies for EV-based IM drives.

2. Mechanical System

The mechanical equation can be given by [24, 25]where is the mechanical angular speed, is the driving torque, T_Leq is the equivalent load torque, J_eq is the equivalent inertia of the vehicle, and is the friction coefficient.

In RB operation, is a negative value equal to the equivalent total braking torque (T_brake), corresponding to total braking force ( + ), where where is the front-braking torque (corresponding to ), is the rear braking torque (corresponding to ), is the friction-front-braking torque (corresponding to ), and T_f,reg is the regenerative-front-braking torque (analogous to f_f,reg).

The value of T_f,reg is taken as a reference value for the motor control (T_ref). Thus,

3. Proposed Regenerative-Braking Control System

Many control loops were used for enhancing the electric motor drive and the vehicle efficiency. The major of these researchers were exposed a standard control loops as it is in [26, 27]. Other intelligent control topologies were exposed in [28, 29], where deep learning is used for enhancing the efficiency of the electrical motor generator.

Predictive control and sliding mode control forms were also exposed in many applications for enhancing the overall system as it is in [30, 31]

From the other side, Kalman filter was used in many applications in order to enhance the system control loop and this reference can give an example [32].

In the proposed control system, the regenerative-braking operation of the induction motor is obtained according to the reference values T_ref and λ_s,ref. The outputs of the control system are the reference stator voltages (u_qs(λ),ref and u_ds(λ),ref), where the subscript (λ) is used to indicate the stator-flux frame. The low-speed cutoff point (LSCP) considered here is taken as a fixed value. Once this speed is reached, the reference values T_ref and λ_s,ref are taken equal to zero. The complete motor control system is shown in Figure 3.

3.1. Calculation of Reference D-Axis Voltage (u_ds(λ),ref)

The d-axis component of the stator voltage (u_ds(λ)) can be given bywhere is the stator resistance, i_ds(λ) is the d-axis component of stator current in the stator-flux frame, and λ_s is the stator flux.

The proposed reference voltage u_ds(λ),ref, appropriate to obtain the reference stator flux λ_s,ref, is obtained bywherewhere Δt is the time step of estimation, u_Rs is the voltage drop across the resistance , u_RsO is the voltage drop across the resistance at time (t-Δt), u_ds(λ),refO is the reference d-axis voltage at time (t-Δt), λ_s,refO is the reference stator flux at time (t-Δt), and λ_sO is the stator flux at time (t-Δt).

The stator flux () and its angle (θ_λ), in the stationary reference frame, can be obtained as follows:where λ, u, and i mean stator flux, voltage, and current, respectively, and the subscripts mean q-axis and d-axis components.

It should be noted that the integration is applied in the implementation process using the discrete calculations.

3.2. Calculation of Reference Q-Axis Voltage (u_qs(λ),ref)

The q-axis component of the stator voltage (u_qs(λ)) can be given bywhere i_qs(λ) is the q-axis component of stator current and is the angular speed of the stator-flux reference frame.

The proposed reference voltage u_qs(λ),ref, appropriate to obtain the reference torque T_ref, is obtained bywherewhere P is the number of pole pairs, i_qs(λ) is the q-axis component of stator current, ω_sl is the angular slip speed, ω_slO is the angular slip speed at time (t-Δt), u_qs(λ),refO is the reference q-axis voltage at time (t-Δt), and ω_mO is the angular speed at time (t-Δt).

In the second expression of equation (17), the numerator is increased by the value because when the sign of is changed from negative to positive, the difference between T_ref (negative value) and is increased by the value .

4. Results and Discussion

In order to ensure the effectiveness of the proposed control system, 100 hp induction motor is used with the complete parameters given in Table 1. Results are obtained when the total braking torque (T_brake) is equal to −1.0 pu, the equivalent load torque (T_Leq) is equal to 1.0 pu, and the low-speed cutoff point (LSCP) is taken equal to 90 rpm. There are different values of the reference torque (T_ref), which is a part of T_brake. The remaining part of T_brake is a friction braking torque. The values of T_ref are taken equal to −1.0 pu, −0.5 pu, −0.25 pu, and −0.1 pu, and the corresponding friction braking torque is equal to zero, −0.5 pu, −0.75 pu, and −0.9 pu, respectively.

Figure 4 shows the motor speed, where the braking is started at time equal to 4.0 s, and Figure 5 shows a fast and an accurate tracking of T_ref. This accurate tracking leads to nearly constant value of T_brake, equal to −1.0 pu, and therefore independent of deceleration on T_ref, as shown by Figure 4. Figure 6 shows the charging current of the battery, where the negative current is proportional to T_ref. When the LSCP is reached during deceleration of the motor, the reference values T_ref and λ_s,ref are taken by the control and equal to zero, as shown in Figures 5 and 7. The corresponding motor currents are shown in Figures 8–12.

To show the effect of parameters uncertainty on the robustness of the control system, the results are obtained again when the stator resistance () is increased and decreased by 30%, where is the only parameter which is involved in the calculations of the proposed control system. The obtained results related to T_ref equals to 1.0 pu are given in Figures 13–16. Furthermore, to show insignificant variation of the performance of the system, the results illustrated in Figures 17–20 are corresponding to T_ref equals to 0.1 pu.

5. Conclusion

Electric vehicles (EVs) need regenerative braking (RB) because it increases the efficiency of the vehicle and therefore extends the vehicle driving range. In the front-wheel drive EV, the rear braking force is only exerted by friction braking and the front-braking force is shared between the friction barking and RB, where the motor control scheme receives the appropriate reference torque (T_ref) signal, during RB, from a front-braking-force distribution controller. The establishing of the motor control scheme depends on the type of electrical motor used in EV. However, in EV-based IM drives, the interest was only for motor-speed control, and RB control of induction motors was absent. This paper has proposed a motor control scheme dedicated to induction motors for RB mode. The implementation of the proposed control scheme has assured a simplicity and controllability under various operating states. Moreover, the presented results have confirmed the robust behavior of the proposed control system for induction motor drives under RB mode of EVs.

6. Future Perspectives

The future of electric vehicles (EVs) holds tremendous promise, and integrating more intelligent control topology stands as a key catalyst for their advancement [33]. Intelligent control systems, encompassing sophisticated algorithms and machine learning, have the potential to optimize various aspects of EV performance. Enhanced energy management, predictive maintenance, and adaptive charging strategies are among the many benefits of intelligent control topology [34, 35]. These systems can dynamically adjust power distribution, manage battery health, and optimize charging based on real-time data and user behavior. Furthermore, intelligent control facilitates seamless communication between vehicles and infrastructure, enabling smart grid integration and grid-friendly charging. This not only enhances the overall efficiency of EVs but also contributes to grid stability and resilience. The future of electric vehicles is intricately tied to the evolution of intelligent control systems, promising a smarter, more sustainable, and user-friendly transportation landscape [36, 37].

Data Availability

The data used to support the findings of this study have not been made available.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Omar E. M. Youssef, Mohamed G. Hussien, and Abd El-Wahab Hassan conceptualized the study, proposed the methodology, investigated the study, performed data curation, visualized the study, provided software, performed formal analysis, validated the study, wrote the original draft, and reviewed and edited the manuscript.

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Copyright © 2024 Omar E. M. Youssef et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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International Transactions on Electrical Energy Systems

A Robust Regenerative-Braking Control of Induction Motors for EVs Applications

Abstract

1. Introduction

2. Mechanical System

3. Proposed Regenerative-Braking Control System

3.1. Calculation of Reference D-Axis Voltage (uds(λ),ref)

3.2. Calculation of Reference Q-Axis Voltage (uqs(λ),ref)

4. Results and Discussion

5. Conclusion

6. Future Perspectives

Data Availability

Conflicts of Interest

Authors’ Contributions

References

Copyright

3.1. Calculation of Reference D-Axis Voltage (u_ds(λ),ref)

3.2. Calculation of Reference Q-Axis Voltage (u_qs(λ),ref)