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.

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.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Traffic Data ◽  
Energy Management Strategy ◽  
Short Term ◽  
Low Level ◽  
Hybrid Electric

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

scholarly journals Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5538
Author(s):  
Bảo-Huy Nguyễn ◽  
João Pedro F. Trovão ◽  
Ronan German ◽  
Alain Bouscayrol
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Engine Fuel ◽  
Linear Quadratic ◽  
Loop Control ◽  
Parallel Hybrid ◽  
Linear Quadratic Regulation ◽  
Hybrid Electric

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

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.

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