Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle

Xiong, Rui; Jun, Cao; Yu, Quanqing

doi:10.1016/j.apenergy.2017.11.072

Cited by 434 publications

(152 citation statements)

References 36 publications

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“…Although the problem of driving's terminal time is free can be solved by BVP4C solution, but if the initial evaluated value is not rational, then the results may be not convergence. So, the trajectory's initial weight set W INT (i.e., [1,1,1,3]) should be evaluated, ACT-R cognitive model is the core of the ACT-R initial module, the information of driving region boundary of the vehicle, goal, and constraints can be encoded and changed into some needed information that can generate trajectory and initial weight set W INT , if the constraints are not exit, then the weight value W Default (i.e., [1,1,2,5]) is supposed as the default weight set.…”

Section: Act-r Initialized Behavior Modelingmentioning

confidence: 99%

“…With the rapid development of electric vehicles, the fatal traffic accidents of electric vehicle occur frequently, which makes the traffic safety issues become the focus of attention, where the rear-end accidents accounted for the most of the traffic accidents. So the avoidance of rear-end accidents becomes the present urgent problems [1,2]. Electric vehicle active collision avoidance system is one of the most effective methods to solve the rear-end accidents and it is a driving assistant system that is based on vehicle active safety technology.…”

Section: Introductionmentioning

confidence: 99%

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ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Li¹,

Zhao

Xibo³

et al. 2018

Energies

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Abstract:The active obstacle avoidance system is one of the important components of the electric vehicle active safety system. In order to realize the active obstacle avoidance system driving the vehicle smoothly and without collision in complex road situation, a new dynamical trajectory planning method based on ACT-R (Adaptive Control of Thought-Rational) cognitive model is introduced. Firstly, the ACT-R cognitive architecture is introduced and the trajectory planning method's framework structure based on ACT-R cognitive model is built. Secondly, the modeling method of ACT-R cognitive model is introduced, the main module of ACT-R cognitive model includes the initialized behavior module, trajectory planning module, estimated behavioral module, and weight adjustment behavior module. Finally, the verification of the trajectory planning method is conducted by the simulation and experiment results. The simulation and experiment results showed that the method of AR (ACT-R) is effective and feasible. The AR method is better than the methods that are based on the OC (Optimal Control) and FN (fuzzy neural network fusion); this paper's method has more human behavior characteristics and can meet the demand of different constraints.

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Section: Act-r Initialized Behavior Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Li¹,

Zhao

Xibo³

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…Moreover, because NI-MH batteries have flat and broad charge-discharge voltage plateaus, the OCV curve of NI-MH batteries charging and discharging are very gradual relative to the lithium-ion battery [11]. The independent methods such as the integration of ampere hours, measurement of OCV, use of artificial intelligence often suffer from low accuracies and large accumulated errors [12][13][14]. Because of the need to change battery structure, the new method for estimating SoC based on battery's stable internal pressure is also difficult to achieve practical application [15].…”

Section: Introductionmentioning

confidence: 99%

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

et al. 2018

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This paper discusses a design of a Battery Management System (BMS) solution for extending the life of Nickel-Metal Hydride (NI-MH) battery. Combined with application of electric boat, a State of Charge (SoC) optimal operation range control method based on high precision energy metering and online SoC correction is proposed. Firstly, a power metering scheme is introduced to reduce the original energy measurement error. Secondly, by establishing a model based parameter identification method and combining with Extended Kalman Filter (EKF) method, the estimation accuracy of SoC is guaranteed. Finally, SoC optimal operation range control method is presented to make battery running in the optimal range. After two years of operation, the battery managed by proposed method has much better status, compared to batteries that use AH integral method and fixed SoC operating range. Considering the SoC estimation of NI-MH battery is more difficult becausing special electrical characteristics, proposed method also would have a very good reference value for other types of battery management.

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“…A number of methods to estimate faults have been proposed and studies on this topic are mainly based on process parameter estimations, state estimations, experience knowledge and other methods [9][10][11][12]. Sun et al [13] described a method to diagnose the degradation of lead-acid battery units caused by internal shorts, opening of internal shorts or cell reversal.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

et al. 2018

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Abstract:The battery is a key component and the major fault source in electric vehicles (EVs). Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV) in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

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Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle

Cited by 434 publications

References 36 publications

ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

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