Research on energy optimization control strategy of the hybrid electric vehicle based on Pontryagin's minimum principle

Wang, Yeqin; Wu, Zhen; Chen, Yuyan; Xia, Aoyun; Guo, Chuangxin; Tang, Zhongyi

doi:10.1016/j.compeleceng.2018.09.018

Cited by 35 publications

(15 citation statements)

References 9 publications

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“…(2) Further research on the SoH estimation of HEVs. In actual operation, due to fuel economy considerations, the energy management strategy of HEVs tends to maintain SoC at 0.5 [24][25][26]. This method has the best estimation effect when the SoC = 0.5 or so, which means only 2-3 fitting curves are needed for HEVs to obtain a very satisfactory SoH estimation effect.…”

Section: Future Discussionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Future Discussionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…The terminal SOC value is calculated by the state equation and the costate equation. The error between the terminal SOC value and the initial SOC value can be calculated, and then the error can be brought within the target range (δ) by readjusting the value of the covariant variable, that is, SOC t f − SOC(t 0 ) ≤ δ [74]. The method of linear interpolation or the Newton method can be used to correct the value.…”

Section: Pontryagin's Minimum Principle (Pmp)mentioning

confidence: 99%

“…Compared with the rule-based strategy, the PMP-based strategy has a significant improvement in fuel economy and the battery SOC at the final moment is unchanged [74]. The PMP-based strategy combining with the intelligent algorithms can be developed to improve the optimal performance of control strategy.…”

Section: Pontryagin's Minimum Principle (Pmp)mentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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“…The rule-based energy management strategy is mature [5,6]; however, it has a lot of restrictions, such as the changes in vehicle parameters, driving conditions and driver habits [7,8], and difficulty achieving significant optimization results. The optimized energy management strategy can achieve better control effects for improving vehicle fuel economy [9], but the real-time performance of the strategy is poor. For example, the global optimization control can optimize the most reasonable energy management strategy under the condition of grasping the overall driving conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

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Research on energy optimization control strategy of the hybrid electric vehicle based on Pontryagin's minimum principle

Cited by 35 publications

References 9 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Combined Prediction for Vehicle Speed with Fixed Route

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