Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology

Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences

Development of hybrid electric vehicles depends on an advanced and efficient energy management strategy (EMS). With online and real-time requirements in mind, this article presents a human-like energy management framework for hybrid electric vehicles according to deep reinforcement learning methods and collected historical driving data. The hybrid powertrain studied has a series-parallel topology, and its control-oriented modeling is founded first. Then, the distinctive deep reinforcement learning (DRL) algorithm, named deep deterministic policy gradient (DDPG), is introduced. To enhance the derived power split controls in the DRL framework, the global optimal control trajectories obtained from dynamic programming (DP) are regarded as expert knowledge to train the DDPG model. This operation guarantees the optimality of the proposed control architecture. Moreover, the collected historical driving data based on experienced drivers are employed to replace the DP-based controls, and thus construct the human-like EMSs. Finally, different categories of experiments are executed to estimate the optimality and adaptability of the proposed human-like EMS. Improvements in fuel economy and convergence rate indicate the effectiveness of the constructed control structure.Comment: 8 pages, 10 figure

Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle

This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time

Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.apenergy.2016.08.085 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.Natural Sciences and Engineering Research Council of Canada (NSERC) || Ontario Research Fun

Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet

An adaptive power split strategy with a load disturbance compensator for fuel cell/supercapacitor powertrains

Electric vehicles powered by fuel cell and supercapacitor hybrid power sources are of great interest. However, the power allocation between each power source is challenging and the DC bus voltage fluctuation is relatively significant in cascaded PI control schemes. This paper develops a power control strategy with an adjustable cut-off frequency, using an artificial potential field, to adaptively split the load current between the fuel cell and the supercapacitor under various load conditions. The adaptive cut-off frequency is calculated by cutting the load frequency spectrum with an allocation ratio that changes with the supercapacitor state of charge. Therefore, the relatively lower frequency portion of the load current is provided by the fuel cell and the supercapacitor handles the higher frequency portion. To enhance the control performance of the DC bus voltage regulation against the load disturbance, a load disturbance compensator is introduced to suppress the DC bus voltage fluctuation when the load variation occurs, which is implemented by a feed-forward controller that can compensate the load current variation in advance. The effectiveness of the proposed strategy is validated by extensive experiments

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

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

The State Response to Climate Change: 50 State Survey

This survey accompanies Global Climate Change and U.S. Law, Second Edition (Michael B. Gerrard and Jody Freeman, eds, 2014). It compiles state legislation, rules and executive orders that specifically address climate change as of the end of April 2014. It also includes a wide variety of state activities that may have an impact on greenhouse gases including legislation related to energy efficiency and renewable energy. The focus of this material is to provide readers with an understanding of the range of state activity that may contribute to greenhouse gas reduction and climate change. Some types of energy efficiency, alternative fuels and renewable energy legislation (such as tax credits for hybrid vehicles) are very similar from state to state; some laws have a short duration and therefore may not be codified (such as temporary tax credits); energy legislation is being enacted at an increasing pace. As a result, not all energy efficiency, alternative fuels and renewable energy legislation and other activity in every state are included in this compilation