Compound-Type Hybrid Energy Storage System and Its Mode Control Strategy for Electric Vehicles

Wang, Bin; Cao, Binggang; Li, Qiyu; Yang, Qi

doi:10.6113/jpe.2015.15.3.849

Cited by 14 publications

(4 citation statements)

References 38 publications

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“…Based on the above reasons, it can be concluded that the state of charge trajectories of the battery and supercapacitor can directly affect the final control effect of the predicted-energy control system [33][34][35]. Therefore, the charging states of the lithium battery and supercapacitor can be elected as the state Figure 3: Supercapacitor model equivalent circuit variables of the system.…”

Section: Energy-predictive Control Strategy Based On a Markov Modelmentioning

confidence: 99%

A Markov Model for Subway Composite Energy Prediction

Wang¹,

Wang²,

Liang³

et al. 2021

Computer Systems Science and Engineering

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Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation, we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running. Compared with traditional predictive control systems, energy efficiency 10.5% higher. This system provides good stability and robustness, satisfactory speed tracking performance and control comfort, and significant suppression of disturbances, making it feasible for practical applications.

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Section: Energy-predictive Control Strategy Based On a Markov Modelmentioning

confidence: 99%

A Markov Model for Subway Composite Energy Prediction

Wang¹,

Wang²,

Liang³

et al. 2021

Computer Systems Science and Engineering

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show abstract

“…HESS's research involves topology design, battery modeling, supercapacitor modeling and SOC estimation, SOH prediction, DC-DC converter control strategies, power distribution strategies, and system stability analysis [ 2 – 5 ]. We conducted in-depth research on the power management system for electric vehicles, and then proposed a simplified cascading HESS topology configuration with the adaptive sliding mode control algorithm [ 6 – 8 ]. Our research has been applied to SUDA brand electric car successfully.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fractional order sliding mode control for Boost converter in the Battery/Supercapacitor HESS

Wang

Zhou

et al. 2018

PLoS ONE

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In this paper, an adaptive fractional order sliding mode control (AFSMC) scheme is designed for the current tracking control of the Boost-type converter in a Battery/Supercapacitor hybrid energy storage system (HESS). In order to stabilize the current, the adaptation rules based on state-observer and Lyapunov function are being designed. A fractional order sliding surface function is defined based on the tracking current error and adaptive rules. Furthermore, through fractional order analysis, the stability of the fractional order control system is proven, and the value of the fractional order (λ) is being investigated. In addition, the effectiveness of the proposed AFSMC strategy is being verified by numerical simulations. The advantages of good transient response and robustness to uncertainty are being indicated by this design, when compared with a conventional integer order sliding mode control system.

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“…The aforementioned energy storage hybridization concept has shown clear benefits in the transportation sector [23] and has resulted in numerous successful EV applications [9,10,[24][25][26]. A parallel battery/ultracapacitor HESS topology with independent load control of individual energy storages should be well suited for energy management control tasks [6,7,11,26,27].…”

Section: Introductionmentioning

confidence: 99%

Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles

et al. 2018

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This contribution outlines the design of electric vehicle direct-current (DC) bus control system supplied by a battery/ultracapacitor hybrid energy storage system, and its coordination with the fully electrified vehicle driveline control system. The control strategy features an upper-level DC bus voltage feedback controller and a direct load compensator for stiff tracking of variable (speed-dependent) voltage target. The inner control level, comprising dedicated battery and ultracapacitor current controllers, is commanded by an intermediate-level control scheme which dynamically distributes the upper-level current command between the ultracapacitor and the battery energy storage systems. The feedback control system is designed and analytical expressions for feedback controller parameters are obtained by using the damping optimum criterion. The proposed methodology is verified by means of simulations and experimentally for different realistic operating regimes, including electric vehicle DC bus load step change, hybrid energy storage system charging/discharging, and electric vehicle driveline subject to New European Driving Cycle (NEDC), Urban Driving Dynamometer Schedule (UDDS), New York Certification Cycle (NYCC) and California Unified Cycle (LA92), as well as for abrupt acceleration/deceleration regimes.

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Compound-Type Hybrid Energy Storage System and Its Mode Control Strategy for Electric Vehicles

Cited by 14 publications

References 38 publications

A Markov Model for Subway Composite Energy Prediction

A Markov Model for Subway Composite Energy Prediction

Adaptive fractional order sliding mode control for Boost converter in the Battery/Supercapacitor HESS

Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles

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