Implementation of Adaptive Equivalent Consumption Minimization Strategy

The fuel efficiency of plug-in hybrid electric vehicles (PHEVs) is largely influenced by their energy management strategies. This paper presents, for the first time, a dual static adaptive A-ECMS (Adaptive Equivalent Consumption Minimization Strategy) energy management controller designed to address energy management challenges in dual-motor PHEVs. The robustness of this controller was also evaluated under perturbations. A-ECMS (Adaptive Equivalent Consumption Minimization Strategy) is an advanced real-time control strategy for energy management in plug-in hybrid electric vehicles (PHEVs). Building upon the classic ECMS (Equivalent Consumption Minimization Strategy), it enhances adaptability and efficiency by dynamically adjusting to varying driving conditions, ensuring optimal fuel consumption and battery utilization. In the experiments, the performance of the A-ECMS strategy was compared against the ECMS strategy and the RB (Rule-Based) strategy, the latter serving as an ideal optimization benchmark. Additionally, we analyzed the equivalent factors of A-ECMS and examined the impact of initial equivalent factors on the experimental outcomes. The results demonstrated that, compared to the ECMS strategy, the A-ECMS approach enhanced fuel economy by 7.8% and reduced CO2 emissions by 15.14 g/km. To further assess the robustness of this method, 5% random noise was introduced into typical operating conditions. Even under these conditions, the A-ECMS strategy still achieved a 6.9% improvement in fuel economy and a 14.39% reduction in CO2 emissions compared to the ECMS strategy. From an economic perspective, the proposed A-ECMS strategy led to cost savings of 30.17% and 28.56% in two different scenarios relative to the ECMS strategy. These findings highlight the effectiveness of the A-ECMS strategy in enhancing fuel efficiency, reducing CO2 emissions, and lowering operational costs for PHEVs. This study provides valuable insights for future research in energy management strategies for hybrid electric vehicles.

Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance. This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not require prediction of the future demanded power by the driver. Therefore, ECMS-CESO is tractable for real-time operation. Under certain conditions ECMS achieves the maximum fuel economy. The main challenge in employing ECMS is the estimation of the optimal equivalence factor L*. Unfortunately, L* is drive-cycle dependent, i. e., it changes from driver to driver and/or route to route. The lack of knowledge about L* has been a motivation for studying a new class of EM strategies known as Adaptive ECMS (A-ECMS). A-ECMS yields a causal controller that calculates L(t) at each moment t as an estimate of L*. Existing A-ECMS algorithms estimate L*, by heuristic approaches. Here, instead of direct estimation of L*, analytic bounds on L* are determined which are independent of the drive-cycle. Knowledge about the range of L*, can be used to adaptively set L(t) as performed by the ECMS-CESO algorithm. ECMS-CESO also defines soft constraints on the battery state of charge (SOC) and a penalty for exceeding the soft constraints. ECMS-CESO is allowed to exceed a SOC soft constraint when an energy saving opportunity is available. ECMS-CESO is efficient since there is no need for prediction and the intensive calculations for finding the optimal control over the predicted horizon are not required. Simulation results for 3 different HEVs are used to confirm the expected performance of ECMS-CESO. This work also investigates the performance of the model predictive control with respect to the predicated horizon length.

<div class="htmlview paragraph">Hybrid electric vehicles (HEV) and other advanced propulsion technologies offer greater fuel economy and lower pollutant and tailpipe emissions. Some experts regard the HEV technology as a practical, attractive solution to social concerns about fuel economy (and related fossil fuel conservation) and vehicle emissions. [<span class="xref">Maples 98</span>] The technology is practical; however, an HEV is more expensive to produce than a conventional internal combustion engine (ICE). In a world of limited resources and many petroleum users and emissions sources, the policy question is whether the best use of resources is to build HEV, to improve the fuel economy and lower emissions from other sources, or to devote the resources to other environmental projects.</div> <div class="htmlview paragraph">We compare the second generation (designed for the United States) Toyota Prius, to the conventional internal combustion engine Toyota Corolla. We examine both private (vehicle purchase price, maintenance, and fuel) and social (the pollutant [non-methane organic gases, carbon monoxide, nitrogen oxides] and carbon dioxide emissions) costs. For each of the vehicles, we evaluate lifetime vehicle exhaust emissions as well as upstream emissions from producing the fuel.</div> <div class="htmlview paragraph">We find that the second generation Prius is still not cost-effective in improving fuel-economy and lowering emissions. For the Prius to be attractive to U.S. consumers, the price of gasoline would have to be more than three times greater than the present level. To be attractive to regulators, the social value of abating tailpipe emissions would have to be 14 times greater than conventional values. Alternatively, the value of abating greenhouse gas emissions would have to be at least $220 per ton. We judge that any HEV would have a difficult time competing with the Corolla because of the Corolla's already high fuel economy and low pollutant emissions.</div> <div class="htmlview paragraph">We also examine the costs and benefits of making a GM Silverado pickup truck into a gasoline hybrid. Like the analysis of the Prius, we again find that the price of gasoline or social value of abating carbon dioxide would have to be higher than the base case values in order to payback the cost of converting the vehicle to an HEV. Finally, we find that at current gasoline prices and carbon dioxide valuation, a conventional vehicle would need to have a fuel economy of 13 mpg or less in order for a $4,000 cost of converting it to an HEV to get 30% better fuel economy would pay off. We conclude that HEV will not have significant sales unless fuel prices rise several-fold or unless regulators mandate them.</div>

A Predictive Energy Management Strategy for Multi-mode Plug-in Hybrid Electric Vehicle based on Long short-term Memory Neural Network

<div class="htmlview paragraph">Alternative powertrain configurations such as hybrid electric vehicles and fuel cell vehicles seem to be the best response to the need of reduced pollutant emissions, especially in urban areas, and to meet future emission limits, that appear to be very severe targets for traditional technologies.</div> <div class="htmlview paragraph">In hybrid electric vehicles (HEVs) an internal combustion engine is combined with one or more electric machines, with the aim of extending the range of the electric and making the entire vehicle more energy–efficient. The architecture of a hybrid drive train is complex, comprising at least two different energy conversion devices (i.e. internal combustion engine and electric machine) drawing energy from at least two different energy storage devices (i.e. fuel tank and battery). Moreover, complex control strategies must be defined in order to properly control the division of load between the thermal engine and the electric machines.</div> <div class="htmlview paragraph">To meet increasing global competition, which means reducing lead-time and costs for hybrid vehicles design, it is necessary to develop methods that wouldquickly evaluate various design concepts. Even if many computational models are available to simulate the performances of HEVs, there’s still a lack of generalized mathematical analyses.</div> <div class="htmlview paragraph">An analytical model has been developed in order to study power flows in hybrid vehicle drive trains. The mathematical formulation allows a quick analysis of the complete energy cascade from fuel energy to tractive power, comprising electricity generation and consumption and energy recovery from braking. As a result a general formulation of the total tractive energy and of the global efficiency of hybrid electric vehicles in function of the main characteristics of its components is provided.</div> <div class="htmlview paragraph">The goal of the analytical model is to enable a quick exploration of design possibilities of hybrid powertrains. Some results are presented in order to show how the present model allows a quick insight into the effects of the main system characteristics on the power flows and the corresponding component losses of the drive train.</div>

Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.

An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.

A power-split hybrid electric vehicle with a dual-planetary gearset is researched in this paper. Based on the lever analogy method of planetary gearsets, the power-split device is theoretically modeled, and the driveline simulation model is built by using vehicle modeling and simulation toolboxes in MATLAB. Six operation modes of the vehicle are discussed in detail, and the kinematic constraint behavior of power sources are analyzed. To verify the rationality of the modeling, a rule-based control strategy (RB) and an adaptive equivalent consumption minimization strategy (A-ECMS) are designed based on the finite state machine and MATLAB language respectively. In order to demonstrate the superiority of A-ECMS in fuel-saving and to explore the impact of different energy management strategies on emission, fuel economy and emission performance of the vehicle are simulated and analyzed under UDDS driving cycle. The simulation results of the two strategies are compared in the end, shows that the modeling is rational, and compared with RB strategy, A-ECMS ensures charge sustaining better, enables power sources to work in more efficient areas, and improves fuel economy by 8.65%, but significantly increases NO x emissions, which will be the focus of the next research work.

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.

Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

The equivalent factor (EF) of adaptive equivalent consumption minimization strategy (A-ECMS) has a direct impact on the performance of hybrid electric trucks (HETs). Although EF on the state of charge (SoC) can effectively achieve fuel economy and SoC maintenance, battery life issues still need to be considered. Battery replacement costs are extremely high, directly affecting the operational costs of HETs. Thus, A-ECMS with enhanced battery life (A-ECMS-EBL) is proposed. Firstly, the near-optimal boundary of EF is determined to ensure the fuel economy of A-ECMS-EBL by analyzing the working mechanism of the HET powertrain. Secondly, a new EF calculation method is developed to enhance battery life. This method utilizes accelerator pedal opening (APO) feedback to optimize the power distribution between the engine and battery under high load conditions, thereby reducing the ratio of battery output power and number of battery cycle (NBC). Finally, the simulation results show that under typical cycle conditions, the equivalent fuel consumption (EFC) of A-ECMS-EBL increased by only 2.3% compared to the dynamic programming (DP), decreased by 1.1% compared to the A-ECMS, and the NBC significantly decreased by 6.12%. The results indicate that A-ECMS-EBL has significant advantages in improving fuel economy and enhancing battery life.

The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K-means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs.

<div class="section abstract"><div class="htmlview paragraph">The global environmental pollution issue and global warming caused by internal combustion engines (ICE) have prompted automotive manufacturers to pioneer the development of emission-free or pure electric vehicles. The Indian government declared that all ICE cars will be replaced by electric vehicles by 2030. Thus, after 2030, ICE vehicle scrapping will be prevented by retrofitting. Transforming traditional cars into electric vehicles in Indian markets reduces emissions and enhances sustainability. This work aims to transform the Maruti Suzuki Zen petrol car into a fully electric vehicle while keeping its pristine transmission system with an onboard charging system. During the fieldwork, all unnecessary components of the ICE are removed to transform it into an electric vehicle. The E Zen’s maximum speed, gradeability, and driving range on both level and sloping roads were also examined. The performance was assessed using a 72 V, 144 Ah lithium-ion battery pack and an AC induction motor with a peak output of 15 kW. The vehicle’s dynamic performance was evaluated, and the findings indicate that the vehicle’s maximum speed, grading capacity, and driving range are 60 kmph, 12.5%, and 100 km, respectively. The weight of the E Zen gains a mere 6%, which is deemed acceptable according to the ARAI standard for retrofitted vehicles in India. The modified vehicle has a 73.41% lower operational cost than the gasoline-powered car. This technique stops global warming and shields the environment from damaging vehicle emissions.</div><div class="htmlview paragraph">The results were promising, and the proposed system functioned adequately for its intended purpose. This work offers exceptionally viable outcomes by achieving efficiency in both economic and environmental aspects.</div></div>