A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus

Sun, Yu; Zhang, Lipeng; Yan, Bingjie; Yang, Chi; Tang, Gong‐You

doi:10.1016/j.ymssp.2015.08.015

Cited by 77 publications

(34 citation statements)

References 27 publications

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“…Figure 8, we can see that the estimates capture the given actual mass for three different cases. Hence, compared to estimation mechanisms in other existing approaches [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], it is of tremendous benefit to predict the unknown mass, without perceiving the information of a road grade. Furthermore, Figure 9 shows the estimation results of unloaded truck based on two different actual field test datasets.…”

Section: Vehicle Mass Estimation Using the Characteristics Of Engine mentioning

confidence: 99%

“…To further improve the accuracy and robustness of the mass estimation, a torque observer was also applied into the system [7]. Moreover, in the literature [8][9][10][11][12][13], many have studied the method, simultaneously estimating the mass and road grade. Furthermore, reference [14] used an extended KF to estimate both mass and road grade.…”

Section: Introductionmentioning

confidence: 99%

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A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

Jung

Choi

2020

Energies

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This paper proposes a new mass estimation for a vehicle system, utilizing the characteristics of engine torque local convex minimum, where the mass can be estimated based on the driving forces and the longitudinal accelerations only. Fundamentally, this approach generally requires no other information about an aerodynamic effect, a road grade, or a rolling friction, which is usually demanded by the existing well-known longitudinal dynamics and adaptive filter-based estimation methods. The effectiveness of the proposed approach was evaluated and validated by both TruckSim/Simulink co-simulation and actual field test data. It is found that the proposed estimation technique is more favorable for a situation where the vehicle is exposed to low-speed regions. In addition to this new mass estimation strategy, other new and current existing methods were explored and are reviewed here. Moreover, this study suggested a guideline for a hybrid-type mass estimation strategy to predict a mass by combining a new method with an existing one for every speed.

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Section: Vehicle Mass Estimation Using the Characteristics Of Engine mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

Jung

Choi

2020

Energies

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“…Therefore, similar to the design principle of the transmission system and the traditional city buses are judged the maximum speed, climbing ability, the ability to accelerate according to these three indicators. Reference of traditional city bus standard, determine the hybrid electric bus and the dynamic indicators and economic indicators of the following: a top speed of 70km/h, or 0 to 50 km/h speed up 30 s or less time, maximum gradability is more than or equal to 20% [1], [7].…”

Section: The Establishment Of Simulation Modelmentioning

confidence: 99%

Simulating Study on Drive System Performance for Hybrid Electric Bus Based on ADVISOR

Wang

Zhu

et al. 2017

MATEC Web Conf.

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Abstract.Hybrid electric bus has a number of advantages when compared with ordinary passenger cars, but in the dynamic matching and the vehicle performance are difficult to detect thus limits its development process. In this paper, combined with the actual models, the hybrid electric bus module parameters were modified in the software of ADVISOR (Advanced Vehicle Simulator), main including: module of the vehicle, the wheel module, motor module, a battery module and engine module, three kinds of bus models for A, B and C were established, and the related performance that need to be analyzed was set up, such as acceleration, gradability, emissions and energy utilization and so on, in order to ensure the vehicle running in the same environment and convenient for comparison, a fixed vehicle driving cycles was chose, then the simulation results was analyzed, and the various performance was compared with the dynamic indicators and economic indicators which determined by referencing of traditional city bus standard and each other, and finally, the performance optimal model of B was chose out which can meet the demand, its related performance parameters of the simulation results are as follows: the best gradability is 26%, maximum speed is 72.7km/h, maximum acceleration is 1.7m/s2, 0~50km/h acceleration time is 9.5s and fuel consumption is 25L/km.

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“…Many scholars simultaneously estimate the mass and road grade [11][12][13][14][15]. Vahidi [16,17] designed the RLS with multiple forgetting factors to account for different rates of change of different parameters, thus enabling the simultaneous estimation of the time-varying grade and piece-wise constant mass.…”

Section: Introductionmentioning

confidence: 99%

The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics

2018

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Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

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A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus

Cited by 77 publications

References 27 publications

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

Simulating Study on Drive System Performance for Hybrid Electric Bus Based on ADVISOR

The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics

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