An Unsupervised Machine-Learning Technique for the Definition of a Rule-Based Control Strategy in a Complex HEV

Finesso, Roberto; Spessa, Ezio; Venditti, Mattia

doi:10.4271/2016-01-1243

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…An optimal rule-based control strategy was developed for non plug-in HEV based on machine learning [18,19]. In the literature [18], hybrid architectures were investigated that minimize the cost, fuel consumption and clustering optimization rule extraction method was proposed to identify the optimal control strategy. However, few works have been reported on rule-based optimal control strategy of PHEV considering the present SOC for the CD mode and CS mode which can be implemented in real time.…”

Section: Vehicle Configuration and Specificationmentioning

confidence: 99%

“…The mode shift boundary was determined by the vehicle speed and wheel power without consideration of the present battery SOC. An optimal rule-based control strategy was developed for non plug-in HEV based on machine learning [18,19]. In the literature [18], hybrid architectures were investigated that minimize the cost, fuel consumption and clustering optimization rule extraction method was proposed to identify the optimal control strategy.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

et al. 2018

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This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9-3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.

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Section: Vehicle Configuration and Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

et al. 2018

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“…The speed correlation between the powertrain (ω pt , ring), the second electric machine (ω em2 , sun) and the engine (ω e , carrier) is obtained using the Willis method for a planetary gear set [40]. The following equation results: ω em2 + τ pg ·ω pt = 1 + τ pg ·ω e (A12) while the torque correlations are determined as follows:…”

Section: Abbreviationsmentioning

confidence: 99%

“…where σ is a severity factor that accounts for severe aging conditions [26,40]. The battery usage (BU), which represents the number of batteries that are required over the lifetime of the vehicle, has been defined as the ratio of the battery life depletion, λ, and the overall battery life, Λ, which has here been set to 20,000 Ah.…”

Section: Appendix Amentioning

confidence: 99%

“…In particular, the fuel and the engine-out NO x mass flow rates were evaluated, for the three considered engines (D0, D1, D2), on the basis of 2D maps as functions of the power and of the speed. The CO 2 emissions have been linearly determined from the fuel consumption [26,40]. The mass of each engine, M e , is a function of the maximum power, P e,max :…”

Section: Appendix Amentioning

confidence: 99%

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Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

et al. 2018

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An optimal design toolbox for hybrid electric vehicles has been developed and applied to three different vehicle segments (a compact vehicle, a small SUV and a medium-size SUV) for two separate power-split hybrid layouts, both equipped with a diesel engine. One layout features a (3gTR) whereas the other lacks of the additional 3-gear transmission. The toolbox combines the optimization of the vehicle design to that of the control strategy and is based on the minimization of the total cost of ownership (TCO) over the vehicle lifetime. The tool still retains the capability of complying with different performance and emission constraints. The identified optimal designs have proved to lead to a reduction of the CO 2 emissions by 50 to 55% and to a reduction of the TCO by 9 to 10% if compared to the conventional vehicle. Such results held for all classes of vehicle. A cost-benefit analysis and a break-even analysis have also been carried out. A mileage of 20,000 km/year over an urban driving scenario has turned out to possibly allow the driver to break even in about four years for the SUVs and in about six years for the compact vehicle. Finally, a linear correlation between the TCO and the specifications of the design components has been detected with a mean percentage error of about 0.1%. Such a correlation can be very helpful for vehicle design tasks.

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Application of Machine Learning Techniques to Hybrid Engine Restart Prediction

Bucknor

Raghavan

2022

Proceedings of the 2022 USCToMM Symposium on Mechanical Systems and Robotics

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An Unsupervised Machine-Learning Technique for the Definition of a Rule-Based Control Strategy in a Complex HEV

Cited by 20 publications

References 30 publications

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

Application of Machine Learning Techniques to Hybrid Engine Restart Prediction

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