Evolutionary Optimization of a Motorcycle Traction Control System Based on Fuzzy Logic

Carrillo, Juan Antonio Cabrera; Castillo, Juan José; Carabias, Enrique; Ortiz, Antonio Moreno

doi:10.1109/tfuzz.2014.2370681

Cited by 19 publications

(5 citation statements)

References 28 publications

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“…In this work, the EMS is developed based on fuzzy logic control (FLC), which has the features of real-time, adaptive and intelligent [32]- [34]. It allows different operators to merge nonlinearities and uncertainties in the best way and incorporate heuristic control in the form of if-then rules.…”

Section: Flc Based On Vectorized Fuzzy Inference Enginementioning

confidence: 99%

Multi-objective Optimal Sizing and Real-time Control of Hybrid Energy Storage Systems for Electric Vehicles

Yu¹,

Cao

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Hybrid energy storage system (HESS) with the combination of lithium-ion batteries and supercapacitors has been recognized as a quite appeal solution to face against the drawbacks such as, high cost, low power density and short cycle life of the battery-only energy storage system, which is the major headache hindering the further penetration of electric vehicles. A properly sized HESS and an implementable real-time energy management system are of great importance to achieve satisfactory driving mileage and battery cycle life, however, the introduced sizing and energy management problems are quite complicated and challenging in practice. This work proposed a Bi-level multi-objective sizing and control framework with the non-dominated sorting genetic algorithm-II and fuzzy logic control (FLC) as key components to obtain an optimal sized HESS and a corresponding optimal real-time FLC based EMS simultaneously. In particular, a vectorized fuzzy inference system which allows large scale fuzzy logic controllers operating in parallel is devised for the first time for such kinds of problems to improve the optimization efficiency. At last, the Pareto optimal solutions of different HESSs incorporating both optimal design and control parameters are obtained and compared to show the achieved enhancements.

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Section: Flc Based On Vectorized Fuzzy Inference Enginementioning

confidence: 99%

Multi-objective Optimal Sizing and Real-time Control of Hybrid Energy Storage Systems for Electric Vehicles

Yu¹,

Cao

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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“…An Extended Kalman Filter (EKF) [5] based on a model that simulates the straight line behavior of the motorcycle is used in order to determine the speed of the vehicle. This model is described next.…”

Section: Estimation Of Road Type and Vehicle Parametersmentioning

confidence: 99%

“…These systems have evolved from their origin, using increasingly sophisticated algorithms and complex control architectures. Fuzzy logic [5,6], sliding control [7][8][9], control by artificial neural networks [10,11] and nonlinear control [12,13] are examples of the most used control methods. These systems try to optimize the longitudinal and lateral force in the tire, obtaining the maximum available force in the wheel-road contact during braking and traction processes.…”

Section: Introductionmentioning

confidence: 99%

Regenerative Intelligent Brake Control for Electric Motorcycles

et al. 2017

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Vehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of polluting gases and noise and the effectiveness of electric motors compared to combustion engines. Some of the disadvantages that electric vehicle manufacturers still have to solve are their low autonomy due to inefficient energy storage systems, vehicle cost, which is still too high, and reducing the recharging time. Current regenerative systems in motorcycles are designed with a low fixed maximum regeneration rate in order not to cause the rear wheel to slip when braking with the regenerative brake no matter what the road condition is. These types of systems do not make use of all the available regeneration power, since more importance is placed on safety when braking. An optimized regenerative braking strategy for two-wheeled vehicles is described is this work. This system is designed to recover the maximum energy in braking processes while maintaining the vehicle's stability. In order to develop the previously described regenerative control, tyre forces, vehicle speed and road adhesion are obtained by means of an estimation algorithm. A based-on-fuzzy-logic algorithm is programmed to carry out an optimized control with this information. This system recuperates maximum braking power without compromising the rear wheel slip and safety. Simulations show that the system optimizes energy regeneration on every surface compared to a constant regeneration strategy.

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“…In Wu, 18 a single‐wheel driving model was established, and simulation study was conducted for optimal feedback tracking control of TCS slip ratio of high‐speed vehicle. Development of a control model of a TCS installed on a motorcycle, regulating the slip in traction and improving dynamic behavior of two‐wheeled vehicles with fuzzy logic, was considered and simulated in Cabrera et al 19 Most of the papers mentioned above, while excellent, their results were not verified and actually validated in the real‐world roads. As for TCS of EVs utilizing back EMF of the motor, to the best of authors' knowledge, there is no published paper dealing with the similar issue and passes the real‐world vilification under extreme road conditions.…”

Section: Introductionmentioning

confidence: 99%

Advanced stabilizing control for electric scooters

Chen

Lin

et al. 2020

Asian Journal of Control

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This paper proposes a novel traction control system (TCS) based on feedback sensing of multi‐state of electric scooters (i.e. vehicle‐road inclination, vehicle yaw rate, vehicle lateral force, vehicle slip, vehicle speed, etc.). The real‐time states of the scooter are measured by a Hall sensor at the rear wheel, a photointerrupter at the front wheel and inertial measurement unit at the center‐of‐gravity of the vehicle. A fuzzy logic based traction control design is developed to against the driving wheel drift or slipping. The controller is realized by an advanced digital signal processor for fast command computation. Our design has been successfully applied in a commercial electric scooter with satisfactory performance for the pre‐specified testing items. Real‐world experiments prove feasibility of the proposed design which show that our design can effectively prevent the electric scooter from slipping while improves riding safety.

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Evolutionary Optimization of a Motorcycle Traction Control System Based on Fuzzy Logic

Cited by 19 publications

References 28 publications

Multi-objective Optimal Sizing and Real-time Control of Hybrid Energy Storage Systems for Electric Vehicles

Multi-objective Optimal Sizing and Real-time Control of Hybrid Energy Storage Systems for Electric Vehicles

Regenerative Intelligent Brake Control for Electric Motorcycles

Advanced stabilizing control for electric scooters

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