Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

2010 ◽  
Author(s):  
Simona Onori ◽  
Lorenzo Serrao ◽  
Giorgio Rizzoni
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
State Of Charge ◽  
Management Problem ◽  
Equivalence Factor ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy ◽  
Adaptation Law

This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.

scholarly journals Robust proportional ECMS control of a parallel hybrid electric vehicle

2016 ◽  
Vol 231 (1) ◽  
pp. 99-119 ◽  
Author(s):  
Wisdom Enang ◽  
Chris Bannister
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
State Of Charge ◽  
Fuel Savings ◽  
Equivalence Factor ◽  
Battery State Of Charge ◽  
Hybrid Electric

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

scholarly journals Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987499 ◽  
Author(s):  
Yang Li ◽  
Xiaohong Jiao
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Mode Switching ◽  
Energy Management Strategy ◽  
Engine Torque ◽  
Equivalence Factor ◽  
Equivalent Consumption Minimization Strategy

To improve the real-time capability, adaptivity, and efficiency of the energy management strategy in the actual driving cycle, a real-time energy management strategy is investigated for commute hybrid electric vehicles, which integrates mode switching with variable threshold and adaptive equivalent consumption minimization strategy. The proposed strategy includes offline and online parts. In the offline part based on the historical traffic data on the route of the commute vehicle, particle swarm optimization is applied to optimize all the thresholds of mode switching, equivalence factor of the equivalent consumption minimization strategy, and the engine torque and speed at the engine-alone propelling mode so as to establish their mappings on the battery state of charge and power demand. In the online part, the established mappings are involved in the energy management supervisor to generate timely appropriate mode switching signals, and an adaptive equivalence factor for instantaneous optimization equivalent consumption minimization strategy and the optimal engine torque and speed at engine-alone propelling mode. To fully demonstrate the effectiveness of the proposed strategy, the simulation results and comparison with some other strategies and the benchmark dynamic programming strategy are presented by implementing the strategies on the GT-SUITE test platform. The comparison result indicates that the control effect of the proposed energy management strategy is much nearer to that of the benchmark dynamic programming than those of other strategies (the rule-based control, the conventional equivalent consumption minimization strategy, the adaptive equivalent consumption minimization strategy, the rule-based-equivalent consumption minimization strategy, and the stochastic dynamic programming strategy) with the respective improvement in fuel efficiency by 25.9%, 13.25%, 4.6%, 1.32%, and 1.13%.

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