Adaptive Equivalent Consumption Minimization Strategy Research Articles

Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) for the HEVs With a Near-Optimal Equivalent Factor Considering Driving Conditions

The equivalent consumption minimization strategy (ECMS) has been considered as a practical energy management strategy for the hybrid electric vehicles (HEVs) because it can be implemented in real time while providing satisfactory performance. However, it is still challenging to adjust an equivalent factor (EF) to its own optimal value in real time because the EF is fundamentally affected by the current driving condition. Although many adaptive ECMSs (A-ECMSs) have been developed to adjust the EF based on a charge-sustaining condition, they do not adequately respond to a change in the driving conditions. In this study, a novel ECMS for the HEVs is proposed to provide the near-optimal performance by considering actual driving conditions. First, the near-optimal condition for the EF is defined to consider a driving condition. Based on it, an iterative scheme is presented to numerically obtain the near-optimal EF. Then, the convergence analysis of the iterative scheme is conducted with practical considerations to implementing the proposed method into real-world applications. Simulation results show that the proposed strategy has better adaptability to changes in the driving conditions with a smaller loss of optimality than conventional A-ECMS which relies only on the charge-sustaining condition. The proposed strategy is also experimentally validated under real-world driving conditions.

Real-Time Torque-Split Strategy for P0+P4 Mild Hybrid Vehicles With eAWD Capability

The 48-V P0+P4 mild hybrid electric vehicles (MHEVs) have been drawing attention for their potential in fuel economy (FE) improvement and electric all-wheel-drive capability through a dual-motor system. This P0+P4 system, enabled by the addition of an extra P4 motor to an existing P0 MHEV, poses the new control problem of optimal torque-split among three components, namely, an internal combustion engine and P0/P4 motors. This study presents an effective real-time torque-split strategy for a 48-V P0+P4 MHEV. To include realistic operational constraints, we consider the longitudinal load transfer of the vehicle, nonlinear tire effects, and constraints on braking force distribution for vehicle safety. First, an optimal torque-split problem for the considered P0+P4 MHEV is formulated and solved with dynamic programming (DP). Then, based on the DP results, we propose a real-time-implementable torque-split strategy using an approximated adaptive equivalent consumption minimization strategy and a suboptimal braking force distribution strategy. The simulation results reveal that the proposed strategy can achieve about 99.1% and 97.7% of global optimality in terms of FE under training and validation driving cycles compared to the results by the DP. As a comparison, the rule-based strategy, as a benchmark, achieves 93.6% and 94.5% under the same drive cycles.

Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle

Improving the fuel economy and optimizing power allocation of the extender range electric logistics vehicles importantly relies on reasonable energy management strategies and accurate driving pattern recognition. Herein, an adaptive equivalent consumption minimization strategy for extender range electric logistics vehicles is proposed. Firstly, a K-means clustering algorithm has been utilized to classify driving blocks. A novel driving pattern recognition approach is designed by using combination of variational mode decomposition (VMD) and extreme learning machine (ELM). Subsequently, a state-of-charge reference planning approach is proposed based on traffic information with dynamic programing for guiding battery energy allocation. Combining the driving pattern recognition results with the reference state-of-charge value, the proposed method acquires the optimal control action through minimizing the objective function. A hardware-in-the-loop test platform by Model Based Design (MBD) approach is implemented to validate the performance of controller. It has been verified that the proposed method can conserve the equivalent fuel consumption by respectively over 6.04% and 3.79%, and suppress the average battery power transients by over 11.61% and 2.85%, compared with the conventional energy management strategies.

Adaptive equivalent consumption minimization strategy for hybrid electric ship

AbstractIn recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce the fuel consumption and emissions, has become a research hotspot. However, frequent use of the battery will accelerate the aging of the battery, and the replacement of scrapped battery will increase the cost of the ship. Therefore, it is necessary to consider delaying battery aging into the energy control strategy of HES. The equivalent consumption minimization strategy (ECMS) is a feasible energy control strategy because it can be implemented in real time. However, under the condition of uncertain initial state of charge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumption unless the equivalent factor (EF) is optimized in real time. In this paper, an adaptive equivalent consumption minimization strategy (A‐ECMS) is proposed, which extracts the global optimal EF trajectory from the dynamic programming (DP) solution and uses the back propagation (BP) neural network to adjust the EF in real time. A trade‐off between the fuel consumption and battery aging is made in the cost function by introducing a weight coefficient. Finally, the effectiveness and the adaptability of the proposed strategy are verified in MATLAB.

Driving Cycle Recognition Based Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are considered the most practical option for reducing emissions and fuel consumption of conventionally powered vehicles. Energy management strategies (EMSs) are the core technology of HEVs because of decreasing the cost of the system and limiting its negative effects. Equivalent consumption minimization strategy (ECMS) can achieve instantaneous optimal control and has attracted attention in recent years. In this study, an adaptive equivalent consumption minimization strategy (A-ECMS) based on driving cycle recognition is constructed for a parallel HEV. First, select the standard driving cycle and analyze its characteristic parameters. And then training learning vector quantization (LVQ) neural network-based driving cycle recognizer to achieve an average of 98% accuracy. At last, the optimal equivalent factor (EF) is selected for ECMS by recognizing the current driving cycle. It is jointly simulated and analyzed by AVL CRUISE and Matlab/Simulink software under NEDC and CHTL-LT driving cycle. The results show that compared with the logic-based EMS in the NEDC driving cycle the 100 km fuel consumption of A-ECMS decreases by 3.8% and the battery state of charge (SOC) increases by 1.1% in the CHTC-LT driving cycle, so, fuel economy improves by 3.6%, proving the superiority of the A-ECMS.

An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information

Although the energy management strategies at present have achieved a good effect, they still have their limitations, so there is still room for further improvement to improve the fuel economy of hybrid electric vehicles (HEV). This paper proposes an adaptive equivalent consumption minimization strategy (ECMS) based on speed prediction, which can distribute power more reasonably and improve the power balance and fuel economy. The driving speed reflects the operation of the road and driver during the driving process. Under the motor assisted energy management control strategy, knowing all working condition information in advance can improve the battery power use planning to a certain extent and reduce the fuel consumption during the whole driving process by adjusting parameters. In this paper, a novel adaptive adjustment method for the equivalent factor (EF) of the ECMS based on future information is proposed. In this paper, a novel speed-prediction method combined with wavelet packet transformation (WPT) and a radial basis function neural network (RBF-NN) is proposed to realize accurate vehicle speed prediction. Then, the optimal equivalent factor under the state of charge (SOC) constraint is calculated by using the predicted speed. Simulation studies are conducted to verify the effectiveness of the proposed adjustment method for the EF compared to a commonly adjustment method from SOC balance and economic viewpoints.

Optimal adaptive race strategy for a Formula-E car

Appropriately managing battery state-of-charge and temperature while ensuring minimized lap time represents a crucial issue in Formula-E competitions. An open research question might relate to simultaneously guarantee near-optimality in the race strategy solution, computational light-weighting, and effective adaptability with respect to varying and unpredictable race conditions. In this paper, a novel near-optimal real-time capable Formula-E race controller is introduced that takes inspiration from the adaptive equivalent consumption minimization strategy (A-ECMS) approach. A reduced-order Formula-E car plant model is detailed first. The optimal Formula-E race problem subsequently discussed involves controlling at each lap the depletable battery energy, the thermal management mode, and the race mode in order to minimize the overall race time. Moreover, avoiding excessively depleting the battery energy and overheating the battery are considered as constraints for the race optimization problem. Dynamic programing (DP) is implemented first to obtain the global optimal Formula-E race strategy solution in an off-line control approach. The proposed real-time capable A-ECMS based race controller finds then detailed illustration. The flexibility of the introduced A-ECMS Formula-E race controller is guaranteed by optimally calibrating the related equivalence factors to adapt to the current vehicle states (i.e. battery state-of-charge, battery temperature, and lap number). Simulation results for the Marrakesh e-prix considering different race scenarios in terms of battery initial temperature and Safety car entry demonstrate that the estimated race time achieved by the A-ECMS race controller is always near-optimal being 1.7% higher at most compared with the corresponding global optimal benchmark provided by DP.

An adaptive equivalent consumption minimization strategy considering catalyst temperature for plug-in hybrid electric vehicle

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

Impact of cost-optimized dedicated hybrid transmission (DHT) constraints on powertrain optimal control

In the transition from internal combustion engines (ICE) based vehicles to pure battery electric vehicles (BEVs), the powertrain is becoming increasingly electrified (xEV). Based on their capability to recuperate brake energy in the legislative test cycles, intermediate xEV vehicles are classified as mild and strong electrified vehicles. Growing xEV sales currently pave the way for the application of cost-optimized dedicated components. The B segment vehicle analyzed in this work is currently equipped with a conventional coaxial transmission, which can offer multiple power sources (combustion engine, electric machine, or hybrid) in a single shift with an additional clutch. A cost-optimized dedicated hybrid transmission (DHT) is designed by eliminating the launch clutch, synchronizers, and redundant energy travel paths to replace the coaxial transmission. The DHT requires one shift for each energy path to connect the power source to the wheels. The flexible coaxial transmission requires only a low specific number of gear shifts to drive a typical test cycle. However, the DHT has constrained energy paths, leading to a high specific number of gear shifts to drive identical test cycles when applied with the standard Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) optimal control. A one dimensional closed-loop simulation model for the passenger car longitudinal dynamics is used in this analysis. The vehicle velocity and evaluation criteria for the simulation are based on the Worldwide harmonized Light vehicles Test Procedure (WLTP). The loss maps from steady-state test bench measurements for the internal combustion engine (ICE), the electric motor (EM), and the transmission unit are used as inputs in building the simulation model. A drivability penalty approach is introduced in the standard A-ECMS optimal control to reduce the number of gear shifts. The work analyzes the gear shift behavior and the absolute CO2 emission between the cost-optimized DHT and the existing coaxial transmission for the considered vehicle application. Detailed power flow using a Sankey diagram from different onboard vehicle sources for the cost-optimized DHT solution is included in the results section. The developed model is validated with a prototype vehicle environment on the Urban Dynamometer Driving Schedule (UDDS).

Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm

Global optimization plays an important role in the energy management strategies (EMS) of the hybrid electric vehicles (HEV). The fuel consumption of HEV could be reduced significantly with an acceleration of global optimization and application of global result in real-time control. In this paper, a new algorithm called breadth first search (BFS) was first used to realize the global optimization in a parallel mild HEV, which transforms the energy management problem of HEV into optimal path searching. Through simulation and calculation, it was found that the totally identical control strategies and fuel consumption could be obtained with BFS and dynamic programming (DP) respectively, while the calculation time for BFS was just about 50%-60% of that. With BFS results as reference, particle swarm optimization was used to adjust the equivalent factor in real-time and an adaptive equivalent consumption minimization strategy (A-ECMS) based on BFS was proposed. The fuel consumption could be decreased with the proposed A-ECMS by 8–15% in different driving cycles compared with that using rule-based strategies. It is believed that BFS has great potential in the future research on EMS of the HEVs.

Reducing the Computation Effort of a Hybrid Vehicle Predictive Energy Management Strategy

The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406 km length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption.

Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life

Hybrid Electric Vehicles (HEV) form an important category of the automotive segment and most importantly fill the transition between internal combustion engines powered conventional vehicles and electric motor-powered vehicles. One of the main propagandas and sales factor of HEV is improved fuel economy over conventional vehicles. Over the recent years, considering the longevity of HEV usage, battery life evaluation has been brought to the forefront of research along with fuel economy and the key method to slow down the battery aging is through Energy Management Strategy (EMS). This research paper presents the comparative analysis of battery life optimization with different control strategies in a parallel hybrid vehicle. In the available research work, EMS considering battery aging is still lacking. This research work considers the impact of multiple parameters, including temperature and current on battery aging, providing more accurate battery life prediction. Four different control strategies are analyzed including, Thermostat, Fuzzy logic, Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) and Q-learning considering battery aging. Results are compared concerning battery aging and fuel economy. In this research, ECMS results show a 25% improved fuel economy compared to the rule-based strategy. Also, a market cost-analysis is depicted to show the monetary savings for each of the energy management strategy.

Design and Comparative Analyses of Optimal Feedback Controllers for Hybrid Electric Vehicles

This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of a combustion engine and an electric machine in a hybrid electric vehicle (HEV). The objective is to deliver demanded torque and minimize fuel consumption and usage of service brakes, subject to constraints on actuator limits and battery state of charge (SOC). First, we derive a function for calculating maximum deliverable torque that is as close as possible to demanded torque and then reduce the number of control inputs to one while minimizing the usage of service brakes in the case of negative demanded torque. Next, we propose modeling SOC constraints by tangent or logarithm functions that provide an interior point to both the ECMS and the LQT. Then, we show that the resulting objective functions are convex, and we propose sub-optimal analytic solutions for the optimization problem by applying their second order approximation about a given reference. We also consider robustness of the controllers to measurement noise using a simple model of noise. Simulation results of the two controllers are compared, and their effectiveness is discussed.

Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle

The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors.

Improving Ride Comfort and Fuel Economy of Connected Hybrid Electric Vehicles Based on Traffic Signals and Real Road Information

Wireless communication technology has promoted the development of connected hybrid electric vehicles (CHEVs). With traffic signal information, the fuel economy of CHEVs can be improved via optimal speed planning. However, the road environment in most existing studies is unreal and riding comfort is ignored. Therefore, this paper uses the real phase and position information of traffic lights to establish a road model and proposes a multi-objective hierarchical optimal (MOHO) strategy. First, a speed planning module is developed as the upper layer. By integrating speed constraints, slope, and traffic light information, a model predictive control (MPC)-based speed planning strategy (SPS) is developed, which improves riding comfort. Second, an energy management module is developed as the lower layer. An adaptive equivalent consumption minimization strategy (A-ECMS)-based energy management strategy (EMS) is proposed, which achieves the optimal power distribution. The results show that the proposed MOHO strategy can improve riding comfort and fuel economy while avoiding vehicle stopping at the signalized intersection under two different road conditions.

Analytical Solution to Equivalent Consumption Minimization Strategy for Series Hybrid Electric Vehicles

Analytical optimal solution to the energy management of hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin's minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the ones achieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method.

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

Abstract The plug-in hybrid electric vehicles (PHEV) provide a promising solution to increasingly severe tailpipe pollution and make possible the high-efficiency utilization of energy. To strengthen the overall fuel economy of a multi-mode PHEV, this paper proposes a real-time predictive energy management strategy (EMS) based on the Long Short-term Memory (LSTM) neural network. In order to forecast the short-term vehicle velocity with speed of previous time steps, a LSTM network is built and trained using speed profile of multiple representative driving cycles; a model predictive control architecture solved by dynamic programming (DP) in prediction horizon is also structured to realize successive online optimization of power allocation between the internal combustion engine and electric motors. The simulation of proposed strategy, convention adaptive equivalent consumption minimization strategy (A-ECMS) and the offline global optimization are carried out with UDDS and HWFET cycles to investigate the effectiveness and adaptivity of the proposed strategy, the result shows that the near-optimality of fuel economy is realized, and the fuel economy is elevated compared with the A-ECMS, therefor the potential for practical application of the proposed strategy is proved.

A Deep Reinforcement Learning Based Energy Management Strategy for Hybrid Electric Vehicles in Connected Traffic Environment

Abstract This paper proposed a deep reinforcement learning based energy management strategy in the connected traffic environment. At the upper layer, a deep deterministic policy gradient algorithm is used to systematically integrate ego vehicle reference speed planning into the energy management strategy in the car following scenarios. At the bottom layer, a driver model and an adaptive equivalent consumption minimization strategy are implemented to conduct the optimal power split control based on the planned reference speed. As distance headway, fuel consumption, and terrain information are taken into consideration by the deep reinforcement learning controller, the proposed strategy can not only maintain a safe distance headway but also improve the energy efficiency of the entire driving cycle. Finally, the fuel consumption score of the proposed strategy is reduced by 3.5% compared with a car following strategy based on a proportional-integral controller. The simulation results show that the proposed strategy can learn a reasonable car following policy through the training process. In addition, the simulation duration for each reference speed planning step is only about 4.1 ms, which proves the proposed strategy can be applied online.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.