Correctional DP-Based Energy Management Strategy of Plug-In Hybrid Electric Bus for City-Bus Route

Chao, Yang; Zhang, Yahui; Zhang, Lipeng; Song, Jian

doi:10.1109/tvt.2014.2352357

Cited by 218 publications

(70 citation statements)

References 23 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, it must be pointed out that personal driving styles also influence energy consumption of each vehicles (Tang et al, 2015a;Tang et al, 2015c;Li et al, 2015;Li et al, 2016;Chen et al, 2016). However, current DVMT models cannot be used describe this influence.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

Jiang

Dong

et al. 2016

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

In this paper, we study battery capacity design for battery electric vehicles (BEVs). The core of such design problems is to find a good tradeoff between minimizing the capacity to reduce financial costs of drivers and increasing the capacity to satisfy daily travel demands. The major difficulty of such design problems lies in modeling the diversity of daily travel demands. Based on massive trip records of taxi drivers in Beijing, we find that the daily vehicle miles traveled (DVMT) of a driver (e.g., a taxi driver) may change significantly in different days. This investigation triggers us to propose a mixture distribution model to describe the diversity in DVMT for various driver in different days, rather than the widely employed single distribution model. To demonstrate the merit of this new model, we consider value-at-risk and mean-variance battery capacity design problems for BEV, with respect to conventional single and new mixture distribution models of DVMT. Testing results indicate that the mixture distribution model better leads to better solutions to satisfy various drivers.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Based on GPS data, vehicles' charging patterns and VMT between two consecutive charging were obtained and studied in Wu et al (2015b); . Recently, the relationship between energy consumption and driving styles also received increasing interests (Tang et al, 2015a;Tang et al, 2015c;Li et al, 2015;Li et al, 2016;Chen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

Jiang

Dong

et al. 2016

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…Moreover, according to the configuration of the studied PHEB, the rotational speed of the wheel will be governed by the engine and motor, as well as the AMT and final drive. The relationship between them is described as [24] ω…”

Section: Battery Modelmentioning

confidence: 99%

“…However, it is not applicable for online applications, as future driving conditions are unknown. Therefore, it is usually deployed as a benchmark or is integrated with other methods to realize systematic optimization [23,24]. Moreover, many real-time control strategies have also been given more attention, such as adaptive Pontryagin's minimum principle (A-PMP) [25], adaptive equivalent consumption minimization strategy (A-ECMS) [26], model predictive control (MPC) [27,28], stochastic dynamic programming (SDP) [29], and reinforcement learning-based real-time energy management [30].…”

mentioning

confidence: 99%

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

View full text Add to dashboard Cite

The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin's minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.Processes 2019, 7, 477 2 of 23 on a single-objective (or multiobjective) optimization problem of driveline matching, component sizing, topology design, or the EMS [8][9][10][11]. For the predefined single-shaft coaxial parallel plug-in hybrid electric bus (PHEB) in our research, component sizing and the energy management control are extremely significant to fuel economy. To guarantee the optimality of dynamic performance and fuel economy, the driveline design and the EMS should be simultaneously considered, as they are strongly coupled [12]. Previous work has disposed of the combined optimization problem in a bilevel manner, where the outer loop is for the former and the inner loop is for the latter [8,13]. Many optimization algorithms have also been extensively applied to solve this problem, such as a genetic algorithm (GA) [4,14], particle swarm optimization (PSO) [11,15], and simulated annealing (SA) [9,16]. Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another categ...

show abstract

“…Therefore, it is worthwhile to study the improvement of the drivability and ride comfort on the premise of ensuring fuel economy. In [35,36], an insightful method that simultaneously optimizes the power split and the gear-shifting is proposed. However, due to the closely interactive relationship between power split and gear-shifting, it would break the optimal decisions and worsen the vehicle fuel economy considerably if any changes happen to the optimal gear-shifting to obtain good drivability.…”

Section: Introductionmentioning

confidence: 99%

Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability

2018

View full text Add to dashboard Cite

Plug-in hybrid electric buses (PHEBs) is some of the most promising products to address air pollution and the energy crisis. Considering the switching between different working modes often bring about sudden changes of the torque and the speed of different power sources, which may lead to the instability of the power output and affect the driving performance and ride comfort, it is of great significance to develop a real-time optimal energy management strategy for PHEBs to achieve the optimization of fuel economy and drivability. In this study, the proposed strategy includes an offline part and an online part. In the offline part, firstly, the energy conversion coefficient s(t) is optimized by linear weight particle swarm optimization algorithm (LinWPSO), then, the optimization results of s(t) are converted into a 2-dimensional look-up table. Secondly, combined with three typical driving cycle conditions, the gear-shifting correction and mode switching boundary parameters that affect the drivability of the vehicle are extracted by dynamic programming (DP) algorithm. In the online part, combined with the s(t), the gear-shifting correction and mode switching boundary parameters which are obtained through offline optimization, the real-time energy management strategy is proposed to solve the trade-off problem between minimizing the fuel consumption and improving the drivability and riding comfort. Finally, the proposed strategy is verified with simulation, the results show that the proposed strategy can guarantee the engine and the electric motor (EM) work in the high-efficiency area with optimal energy distribution while keeping drivability in the variation of driving circle. The overall performance is improved by 18.54% compared with the rule-based control strategy. The proposed strategy may provide theoretical support for the optimal control of PHEB.

show abstract

Correctional DP-Based Energy Management Strategy of Plug-In Hybrid Electric Bus for City-Bus Route

Cited by 218 publications

References 23 publications

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability

Contact Info

Product

Resources

About