Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus

Zhang, Lipeng; Zhang, Yuanbo; Yang, Chao; Yan, Bingjie; Martínez, Clara Marina

doi:10.1016/j.enconman.2015.12.077

Cited by 170 publications

(86 citation statements)

References 27 publications

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“…The vehicle model was designed in the CarSim software, but the complete powertrain model of the BEV is not included in Carsim, so external models of the motor and battery were added. An internal resistance model was adopted for the battery component [24] as in Figure 2, wherein, the V oc and R int are the open circuit voltage and battery internal resistance, respectively, and the U c and I c are the voltage and current of external load. The efficiency of the battery related to the internal resistance of the battery is considered in the battery model.…”

Section: Model For Bevmentioning

confidence: 99%

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Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Zhang

Zhuan

2019

Symmetry

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This paper studies control strategies for adaptive cruise control (ACC) systems in battery electric vehicles (BEVs). A hierarchical control structure is adopted for the ACC system, and the structure contains an upper controller and a lower controller. This paper focuses on the upper controller. In the upper controller, model predictive control (MPC) is applied for optimizing multiple objectives in the car-following process. In addition, multiple objectives, including safety, tracking, comfort, and energy economy, can be transformed into a symmetric objective function with constraints in MPC. In the objective function, the corresponding weight matrix for the optimization of multiple objectives is implemented in symmetric form to reduce the computational complexity. The weights in the weight matrix are usually set to be constant. However, the motion states of the own vehicle and the front vehicle change with respect to time during a car-following process, resulting in variation of the driving conditions. MPCs with constant weights do not adapt well to changes in driving conditions, which limits the performance of the ACC system. Therefore, a strategy for weight adjustment is proposed in order to improve the tracking performance, in which some weights in MPC can be adjusted according to the relative velocity of two vehicles in real time. The simulation experiments are carried out to demonstrate the effectiveness of the strategy for weight adjustment. Based on achieving the other control objectives, the ACC system with the weight adjustment has better tracking performance than the ACC system with the constant weight. While the tracking is improved, the energy economy is also improved.

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Section: Model For Bevmentioning

confidence: 99%

“…The efficiency of the battery related to the internal resistance of the battery is considered in the battery model. The efficiency of the motor related to the speed and torque of the motor is considered in the motor model [24,25]. The maximum torque and speed characteristics for the motor are shown in Figure 3.…”

Section: Model For Bevmentioning

confidence: 99%

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Zhang

Zhuan

2019

Symmetry

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“…Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.Energies 2018, 11, 2322 2 of 18 be modulated to control the overall braking torque for meeting the requirements [5]. The regenerative braking torque depends on the motor characteristics [6], the charging power capability of the battery [7], and the available tire-road friction [8]; hence, existing research has focused on improving the braking energy recovery efficiency by distributing the torque between regenerative braking and friction braking based on drivers' demands.In engineering practice, several rule-based regenerative braking strategies have been proposed and used in recent years.…”

mentioning

confidence: 99%

“…Energies 2018, 11, 2322 2 of 18 be modulated to control the overall braking torque for meeting the requirements [5]. The regenerative braking torque depends on the motor characteristics [6], the charging power capability of the battery [7], and the available tire-road friction [8]; hence, existing research has focused on improving the braking energy recovery efficiency by distributing the torque between regenerative braking and friction braking based on drivers' demands.…”

mentioning

confidence: 99%

A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

Guoye

Gong

et al. 2018

Energies

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Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative-mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.Energies 2018, 11, 2322 2 of 18 be modulated to control the overall braking torque for meeting the requirements [5]. The regenerative braking torque depends on the motor characteristics [6], the charging power capability of the battery [7], and the available tire-road friction [8]; hence, existing research has focused on improving the braking energy recovery efficiency by distributing the torque between regenerative braking and friction braking based on drivers' demands.In engineering practice, several rule-based regenerative braking strategies have been proposed and used in recent years. For a rear-driven electric truck, a modified control strategy that determines how to distribute the braking force between the front and rear axles was proposed by Zhang to improve the recovery efficiency [9]. Xiao presented an integrated control strategy to coordinate regenerative and friction braking forces to deal with the braking stability and recovery efficiency when a vehicle performed normal deceleration and emergency braking [3]. To regenerate more braking energy and move closer to the ideal braking force distribution curve, a combined braking control strategy was developed for the rear wheel-driven series hybrid electric EV to adjust the proportions of regenerative braking and friction braking [10]. A regenerative braking cooperative control strategy was proposed by Jiweon for hybrid EVs equipped with a hydraulic brake on the rear wheels and an electronic wedge brake on the front wheels [11].Optimization and...

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“…Among those, the hydraulic system is likely to be 33% smaller and 20% lighter than the closest electrical counterparts and is therefore a logical selection for regenerative braking. 4 Previous research presented that the major research topic of improving the energy recovery efficiency in hydraulic systems is system design, 6 structure, [7][8][9][10] and control strategy, [11][12][13] all manners are needed to know the key factors and dynamic characteristics previously to improve the regeneration efficiency, although the modeling uncertainties can be obtained with the appropriate method. 14 Apart from the traditional hydraulic systems, the electro-hydraulic composite braking system (EHCBS), which features in high-power density and energy conversion efficiency, has been proven suitable for heavy duty vehicles.…”

Section: Introductionmentioning

confidence: 99%

Investigation of energy efficiency for electro-hydraulic composite braking system which is based on the regenerated energy

Lin

Chen

et al. 2016

Advances in Mechanical Engineering

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A novel structure of the combined braking system based on the regenerative braking energy has been proposed to achieve simplified structure and energy-saving capability simultaneously, which includes the hydraulic regenerative braking system, electro-hydraulic braking system, and the power coordinate module. Theoretical contributions and managerial implications of the developed system are discussed. The corresponding mathematic models are developed, a fuzzy control method which can fulfill the power coordinate between the high-pressure and low-pressure accumulators is proposed, and the correctness of the model is verified with the utilization of the test bench. The dynamic characteristics and efficiency are further investigated in various parameters based on MATLAB/Simulink, such as the vehicle initial braking speed, the upper pressure, the liquid capacity, and the state-of-charge (SOC) of the regenerative braking accumulator. The results of the simulations which provide strong evidence for the liquid capacity and initial SOC of the regenerative braking accumulator are the key factors that affect the energy recovery efficiency, and the initial braking speed is the key point that affects the total energy. The results can provide analytical references to practical applications.

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Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus

Cited by 170 publications

References 27 publications

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

Investigation of energy efficiency for electro-hydraulic composite braking system which is based on the regenerated energy

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