Integrative braking control system for electric vehicles

Liang, Chao; Yao, Liang; Chen, Jian; Chao, LiBo; Guo, Jianhua; Zhang, Yongsheng; Liu, Minghui

doi:10.1109/vppc.2011.6042995

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…Solid blue line I, which is also known as ''Ideal curve,'' indicates the required ratio of front and rear wheels braking force distribution to ensure all wheels lock simultaneously, which provides maximum braking force before any wheel lock. UNECE (United Nations Economic Commission for Europe) Regulations require the adhesion coefficient utilization curve of the rear axle must not be higher than the curve for the front axle, 37,38 which means the real force distribution ratio in vehicle braking system should not be higher than the ideal curve all the time.…”

Section: Stability and Controllability In Brakingmentioning

confidence: 99%

The prediction of braking noise in regenerative braking system using closed-loop coupling disk brake model

Gao

Ruan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Aiming at improving regenerative braking ability in electric vehicles without compromising any safety, two different regenerative braking strategies are proposed in this study. The impact of continuously varying braking force distributions between front/rear wheel and electric/friction corresponding in two different strategies on braking noise are investigated. Based on the closed-loop coupling disk brake model, the relationship between the contact coupling stiffness and the braking force is established by considering the stationary modal test, the nonlinear optimization, and the relationship between brake-line pressure and braking force. The continuously varying braking force is initially transformed to continuously varying contact coupling stiffness, then, the brake noise tendency at each frequency band is calculated in closed-loop coupled model. The predicted result shows good consistency with the result recorded in bench test, verifying the reliability and effectivity of the presented method. The comparison of the two different electric braking strategies shows that the second braking strategy is superior to the first braking strategy in terms of suppressing the brake noise tendency.

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Section: Stability and Controllability In Brakingmentioning

confidence: 99%

The prediction of braking noise in regenerative braking system using closed-loop coupling disk brake model

Gao

Ruan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…If the front/rear wheel braking force distribution ratios always follow this blue curve, known as 'Ideal' braking force distribution ratio, vehicle will make the maximum utilization of road-tyre friction force and ensure the most stability and controllability in braking. For all load conditions, UNECE Regulations demand that the adhesion coefficient utilization curve of the rear axle must not be higher than the curve for the front axle [34,35]. With reference to Fig.6, this means that the force distribution curve should always be lower than the ideal curve.…”

Section: Stability and Controllability In Brakingmentioning

confidence: 99%

The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle

et al. 2016

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As motor-supplied braking torque is applied to the wheels in an entirely different way to hydraulic friction braking systems and it is usually only connected to one axle complicated effects such as wheel slip and locking, vehicle body bounce and braking distance variation will inevitability impact on the performance and safety of braking. The potential for braking energy recovery in typical driving cycles is presented to show its benefit in this study. A general predictive model is designed to analysis the economic and dynamic performance of blended braking systems, satisfying the relevant regulations/laws and critical limitations. Braking strategies for different purposes are proposed to achieve a balance between braking performance, driving comfort and energy recovery rate. Special measures are taken to avoid any effects of motor failure. All strategies are analyzed in detail for various braking events. Advanced driver assistance systems (ADAS), such as ABS and EBD, are properly integrated to work with the regenerative braking system (RBS) harmoniously. Different switching plans during braking are discussed. The braking energy recovery rates and brake force distribution details for different driving cycles are simulated. Results for two of the cycles in an 'Eco' mode are measured on a drive train test rig and found to agree with the simulated results to within approximately 10%. Reliable conclusions can thus be gained on the economic benefit and dynamic braking performance. The strategies proposed in this paper are shown to not only achieve comfortable and safe braking during all driving conditions, but also to significantly reduce cost in both the short and long term.

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“…In terms of front and rear axles, τ mechF + τ reg is often produced over the front axle and τ mechR over the A C C E P T E D M A N U S C R I P T rear one. This distribution is based on the traditional theory of braking force distribution and the Economic Commission for Europe (ECE) regulation (Chu et al, 2011a). The brake control strategy defines the braking force strength to properly reduce the vehicle speed, and the distribution of braking effort between front and rear wheels to guarantee vehicle stability, and defines the strategy to recover as much braking energy as possible.…”

Section: Regenerative Brakingmentioning

confidence: 99%

Electrical vehicle modeling: A fuzzy logic model for regenerative braking

Maia

Silva

Araújo

et al. 2015

Expert Systems with Applications

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Highlights• A fuzzy logic model for modeling regenerative braking systems is presented.• Model output is the ratio of regenerative braking force to the total braking force.• Model can be adapted to be used in traffic simulators like the SUMO simulator.• Real data was gathered from short and long-distance field tests with a Nissan LEAF.• Results were compared with real-world data obtained with a Nissan LEAF in road tests. AbstractR2Q3 This paper presents a fuzzy logic model of regenerative braking (FLmRB) for modeling EVs' regenerative braking systems (RBS). The model has the vehicle's acceleration and jerk, and the road inclination as input variables, and the output of the FLmRB is the regeneration factor, i.e. the ratio of regenerative braking force to total braking force. The regeneration factor expresses the percentage of energy recovered to the battery from braking. The purpose of the FLmRB development is to create realistic EV models using as least as possible manufacturers intellectual property data, and avoiding the use of EV on-board sensors. To tune the model, real data was gathered from short and long-distance field tests with a Nissan LEAF and compared with two types of simulations, one using the proposed FLmRB, and the other considering that all the braking force/energy is converted to electric current and returned back to charge the battery (100% regeneration). The results show that the FLmRB can successfully infer the regenerative braking factor from the measured EV acceleration and jerk, and road inclination, without any knowledge about the EV brake control strategy.

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Integrative braking control system for electric vehicles

Cited by 18 publications

References 7 publications

The prediction of braking noise in regenerative braking system using closed-loop coupling disk brake model

The prediction of braking noise in regenerative braking system using closed-loop coupling disk brake model

The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle

Electrical vehicle modeling: A fuzzy logic model for regenerative braking

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