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Performance Potential of Regenerative Braking Energy Recovery of Autonomous Electric Vehicles

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  • Control Theory and Applications
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Abstract

Regenerative braking is an important feature to increase the driving range of electric vehicles (EVs). For an autonomous EV, the deceleration profile and portion of regenerative braking torque can be control variables affecting the regenerative braking energy recovery. To design a control algorithm maximizing the energy recovery, knowledge of the maximum performance of the control system and the optimal control inputs for the maximum performance is very useful. This paper presents how to extract the maximum performance and corresponding optimal control variables for maximizing the energy recovery. As an exemplary application of the extracted optimal solutions, a simple braking strategy was designed and validated in a simulation environment. It outperformed the maximum generation torque strategy that is generally considered as the best strategy for maximizing energy recovery.

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Abbreviations

g :

Gravitation acceleration

ρ :

Air density

A f :

Vehicle frontal area

C d :

Aerodynamic drag coefficient

f rr :

Rolling resistance

M v :

Vehicle mass

R w :

Tire rolling radius

N g :

Reduction gear ratio

η g :

Reduction gear efficiency

η m :

Motor efficiency

ω m :

Motor speed

τ m :

Motor torque

P m :

Motor power

P batt :

Battery power

V oc :

Battery open circuit voltage

R batt :

Battery resistance

C batt :

Battery capacity

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Korea

    Yeayoung Park, Seokhyeon Park & Changsun Ahn

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  1. Yeayoung Park
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  2. Seokhyeon Park
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  3. Changsun Ahn
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Corresponding author

Correspondence to Changsun Ahn.

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Conflict of Interest

The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

Yeayoung Park received his B.S. degree in mechanical engineering from Pusan National University, Korea, in 2016. He is currently working toward a Ph.D. degree in mechanical engineering from Pusan National University, Korea. His research interests include modeling, control, and estimation of automotive systems.

Seokhyeon Park received his B.S. and M.S. degrees in mechanical engineering from Pusan National University, Korea, in 2020 and 2022, respectively. He is currently working toward a Ph.D. degree in mechanical engineering from Pusan National University, Korea. His research interests include machine learning and control.

Changsun Ahn received his B.S. and M.S. degrees in mechanical engineering from Seoul National University, Seoul, Korea, in 1999 and 2005, respectively, and a Ph.D. degree in mechanical engineering from the University of Michigan, Ann Arbor, MI, USA, in 2011. He is currently a professor with Pusan National University, Busan, Korea. His research interests include the fields of automotive control/estimation. Recently, he has focused on autonomous vehicle control and hybrid vehicle control.

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This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT (No. NRF-2022R1A2C1004894).

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Park, Y., Park, S. & Ahn, C. Performance Potential of Regenerative Braking Energy Recovery of Autonomous Electric Vehicles. Int. J. Control Autom. Syst. 21, 1442–1454 (2023). https://doi.org/10.1007/s12555-022-0717-0

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  • DOI: https://doi.org/10.1007/s12555-022-0717-0

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