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Your search keyword '"Equivalent consumption minimisation strategy"' showing total 3 results
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3 results on '"Equivalent consumption minimisation strategy"'

1. Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles.

Author

Liu, Hui, Li, Xunming, Wang, Weida, Han, Lijin, Xin, Huibin, and Xiang, Changle

Subjects
HYBRID electric vehicles, FOUR-wheel driving, PLUG-in hybrid electric vehicles, AUTOMOBILE dynamics
Abstract

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Energy management for Plug-in Hybrid Electric Vehicles using Equivalent Consumption Minimisation Strategy

Author

Vincenzo Marano, Giorgio Rizzoni, and Pinak Tulpule

Subjects
Power management, Optimization, Engineering, business.product_category, Electric vehicles, Fuel economy, Energy management, Distance sensitivity, PHEV, Plug-In Hybrid Electric Vehicle, Fuels, Dynamic programming, Automotive engineering, Equivalent consumption minimisation strategy, Electric vehicle, Battery state of charge, Distance sensitivity, ECMS, Equivalent consumption minimisation strategy, Optimisations, PHEV, Plug-In Hybrid Electric Vehicle, Automobiles, Electric vehicles, Energy management, Fuel economy, Fuels, Lead acid batteries, Optimization, Sensitivity analysis, Battery state of charge, Renewable Energy, Sustainability and the Environment, business.industry, Energy consumption, Lead acid batteries, Fuel Technology, State of charge, Automotive Engineering, Fuel efficiency, business, Sensitivity analysis, Optimisations, Automobiles, Driving cycle, ECMS
Abstract

One strategy to minimise petroleum fuel consumption of a Plug-in Hybrid Electric Vehicle (PHEV) is to attain the lowest admissible battery State of Charge (SOC) at the end of driving cycle while following an optimal SOC profile. The challenge of an optimisation algorithm is to find this optimal profile by using least future information about the power demand. An application of Equivalent Consumption Minimisation Strategy (ECMS) for PHEV is presented in this paper and benchmarked against the dynamic programming (DP) for information requirement and optimality. The optimality is assessed in simulation by considering petroleum fuel economy and deviation of the optimal SOC profile from a reference profile for different driving scenarios and battery sizes. Results show that for longer distances and larger battery sizes, ECMS and DP provide similar fuel economy and SOC profiles. A sensitivity analysis with respect to driving distance is presented at the end of the paper.

Published
2010

3. Real-time power management of parallel full hybrid electric vehicles

Author

Adhikari, Sunil and Adhikari, Sunil

Abstract

Hybrid electric vehicles (HEVs) combine an electric motor/generator (EMG) and an energy storage system (ESS) with the internal combustion engine (ICE) to produce a fuel efficient (and low gaseous emissions) powertrain. The best fuel efficiency of an HEV is realizable only by a proper power management strategy (PMS), which distributes the vehicle power demand between the ICE and the ESS to achieve simultaneous objectives such as minimum overall fuel consumption, proper regulation of battery charge and frequency of engine stop/restart and maximum regeneration of braking energy. Performance of a PMS depends on the configuration of components (i.e., parallel or series), the degree of hybridization (i.e., relative driving capacities of the ICE and the ESS) and the preview length of future driving information. The main foci of this thesis are the PMSs that explore the best fuel saving capability of parallel full HEVs . The research described starts with developing a parallel HEV model and an equal power conventional vehicle of a full size passenger car configured to what is a significant compromise to obtain excellent fuel consumption. The driving environment includes drive cycles such as Australian urban drive cycle (AUDC) to facilitate performance compatible with the Australian driving environment and road gradient to include more realistic and aggressive driving environments than standard fuel consumption test cycles. The ultimate fuel saving capability of the HEV is firstly revealed. Such a solution is possible only with optimal or near-optimal PMSs with the knowledge of the complete drive cycle, in other words, with ``offline'' PMSs. Two well-known offline strategies based on dynamic programming (DP) and equivalent consumption minimization strategy (ECMS) are implemented. This thesis introduces a new offline power management strategy (HCPMS) to solve the problem near-optimally within an acceptable computational time. Main features of the HCPMS are the iterative solut

Published
2010

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