Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

Bagwe, Rishikesh Mahesh; Byerly, Andy; Santos, Euzeli Cipriano dos; Miled, Zina Ben

doi:10.3390/en12234472

Cited by 34 publications

(17 citation statements)

References 26 publications

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“…Rule-based strategies (RBS) can be constructed by mathematical models or engineering expertise, which have short computing time, high reliability, and easy implementation. However, despite extensive experimentation and calibration work, their optimality and adaptability may still not be guaranteed [7]- [9]. Optimization-based strategies (OBS) generally calculate (near) optimal results by using a strategy that minimizes the sum of objective functions over time (global optimization) or by instantly minimizing an objective function (local optimization).…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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“…Bagwe et al proposed a method for the optimization of the engine operating zone based on adaptive rule-based energy management. The results showed that it can prevent the engine from operating in highly inefficient zones and reduce the total equivalent fuel consumption of the vehicle [19]. Zhang et al proposed an engine operating zone design method based on a designed optimal operating curve, where the fuel economy can be greatly improved.…”

Section: Introductionmentioning

confidence: 99%

A Robust Design Method for Optimal Engine Operating Zone Design of Plug-in Hybrid Electric Bus

et al. 2022

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The optimal engine operating zone of energy management plays an important role in the fuel economy improvement of Plug-in hybrid electric buses. However, the existing investigations usually design the engine operating zone by experience. This paper proposes a robust design method for the robust and optimal design of the engine operating zone. Firstly, a nonlinear model predictive control (NMPC)-based energy management together with a single-point preview SOC (state of charge) plan method is designed. Then, a Taguchi robust design model is designed to find the optimal engine operating zone by taking the energy management as underlying solving module. Particularly, the noises of driving cycles and stochastic vehicle mass are considered to improve the robust performance of the engine operating zone. Finally, The Monte Carlo Simulation is deployed to verify the robustness and optimal performances of the designed engine operating zone. Simulation results demonstrate that the proposed method is beneficial to the fuel economy improvement, where the fuel economy can be averagely improved by 9.10% compared with the experienced designed engine operating zone, and can be averagely improved by 16.34% compared with the rule-based energy management strategy.

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“…RBSs are easy to implement and require low computational cost, but energy management efficiency is modest. One example of RBS is presented by Bagwe et al [3] and Wu et al [4].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

et al. 2021

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The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

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Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

Cited by 34 publications

References 26 publications

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

A Robust Design Method for Optimal Engine Operating Zone Design of Plug-in Hybrid Electric Bus

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

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