Review on Braking Energy Management in Electric Vehicles

Valery Vodovozov, Zoja Raud and Eduard Petlenkov
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Valery Vodovozov: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Zoja Raud: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Eduard Petlenkov: Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia

Energies, 2021, vol. 14, issue 15, 1-26

Abstract: The adoption of electric vehicles promises numerous benefits for modern society. At the same time, there remain significant hurdles to their wide distribution, primarily related to battery-based energy sources. This review concerns the systematization of knowledge in one of the areas of the electric vehicle control, namely, the energy management issues when using braking controllers. The braking process optimization is summarized from two aspects. First, the advantageous solutions are presented that were identified in the field of gradual and urgent braking. Second, several findings discovered in adjacent fields of automation are debated as prospects for their possible application in braking control. Following the specific classification of braking methods, a generalized braking system composition is offered, and all publications are evaluated primarily in terms of their energy recovery abilities as a global target. Then, conventional and intelligent classes of braking controllers are compared. In the first category, classic PID, threshold, and sliding-mode controllers are reviewed in terms of their energy management restrictions. The second group relates to the issues of the tire friction-slip identification and braking torque allocation between the hydraulic and electrical brakes. From this perspective, several intelligent systems are analyzed in detail, especially fuzzy logic, neural network, and their numerous associations.

Keywords: electric vehicles; energy efficiency; regenerative braking; intelligent controllers; fuzzy logic; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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