Control-based Energy Management Strategy Research Articles

Electric Vehicle Energy Management via Traffic Light Detection and Segmental Velocity Forecasting

Abstract Predictive-based power control has been widely recognized as a promising approach to boost driving range and improve system-level energy efficiency for electric vehicles (EVs), in which vehicle velocity forecasting generally serves as a preliminary input to optimally schedule the operations of varying on-board electrical and thermal systems. A segment-based velocity forecasting approach for individual commuting vehicles developed in this study reveals that it is challenging to forecast the velocity at intersection segments only using the velocity data. To address this challenge, this study seeks to develop a YOLO-V2-based object detection deep network to recognize the traffic lights in advance, and leverage the detected signals to establish a forecasting model that integrates with the probability-based hybrid forecasting approach. The case study results show that the traffic light detection-based forecasting model can significantly improve the forecasting accuracy for intersection segments. Based on the forecasting velocity 5-15 seconds ahead, the effectiveness of model predictive control-based energy management strategy is further evaluated with a liquid-based battery thermal control system. The proposed Battery Thermal Management System (BTMS) model shows promising results in maintaining battery temperature within an appropriate range, thus improving the overall energy efficiency of the EV. Moreover, a traffic light-based real-time energy management framework is developed to directly control the power demand from the Air Conditioning (AC) system.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive (AWD) hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability. Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue. A unique multi-mode coupling (MMC) AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles. Based on the parameters of the benchmarking model of a hybrid vehicle, the best model-predictive control-based energy management strategy is proposed. First, the drive system model was built after the analysis of the MMC-AWD's drive modes. Next, three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed, which was followed by the design of a road driving force observer. Then, the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient. Finally, the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD. The findings indicate that, in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6, the MMC-AWD's capacity to accelerate increases by 5.26% and 7.92%, respectively. When the road adhesion coefficient is 0.8, 0.6, and 0.4, the maximum climbing ability increases by 14.22%, 12.88%, and 4.55%, respectively. As a result, the dynamic performance is greatly enhanced, and the fuel savings rate per 100 km of mileage reaches 12.06%, which is also very economical. The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Optimal Adaptive Fractional Order Integral Sliding Mode Controller-Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

This research presents an optimal energy management system (EMS) for a lithium-ion battery-supercapacitor hybrid storage system used to power an electric vehicle. The storage systems are connected in parallel to the DC bus by bidirectional DC-DC converters and feed a synchronous reluctance motor through an inverter. The proposed energy management strategy is built on the idea to take full benefits of two combined methods: the bald eagle search algorithm and fractional order integral sliding mode control. To evaluate the effectiveness of the suggested optimal energy management strategy, an urban dynamometer driving schedule (UDDS) driving cycle is considered. The obtained results are compared to a classical fractional order integral sliding mode control-based energy management strategy in terms of voltage ripples, overshoots, and battery final state of charge. The ultimate results approve the ability of the proposed energy management system to enhance the power quality and enhance battery power consumption at the same time. Comprehensive processor-in-the-loop (PIL) cosimulations were conducted on the electric vehicle using the C2000 launchxl-f28379d digital signal processing (DSP) board to assess the practicability and effectiveness of the proposed EMS.

A Model Predictive Control-Based Energy Management Strategy for Secure Operations in Shipboard Power Systems

A Model Predictive Control-Based Energy Management Strategy for Secure Operations in Shipboard Power Systems

Lifetime-Optimized Energy Management Strategy for Fuel Cell Unmanned Aircraft Vehicle Hybrid Power System

To improve the durability of the fuel cell unmanned aircraft vehicle (UAV) hybrid power system, a model predictive control based energy management strategy (EMS) with minimum energy loss is presented in this paper. The proposed algorithm can effectively reduce the internal loss and power stress of the hybrid power system, which can help to extend fuel cell lifespan. Specifically, a calculation formula with observable parameters is proposed to predict the internal loss of the battery in both the charging and discharging mode. Besides, to increase the hybrid UAV adaptability under complex flying conditions, the fuel cell operating trajectory is also defined in the algorithm to increase the system dynamic performance and efficiency. To verify the effectiveness of the strategy, the hardware-in-the-loop (HIL) experimental test platform is built. Compared to the conventional equivalent consumption minimum strategy (ECMS), the proposed strategy can effectively alleviate the voltage degradation of the fuel cell and decrease the energy loss of the battery. The proposed strategy can help to contribute to the applications of fuel cell hybrid power system in the aviation fields.

Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition

This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method.

Powertrain Design and Energy Management Strategy Optimization for a Fuel Cell Electric Intercity Coach in an Extremely Cold Mountain Area

Facing the challenge that the single-motor electric drive powertrain cannot meet the continuous uphill requirements in the cold mountainous area of the 2022 Beijing Winter Olympics, the manuscript adopted a dual-motor coupling technology. Then, according to the operating characteristics and performance indicators of the fuel cell (FC)–traction battery hybrid power system, the structure design and parameter matching of the vehicle power system architecture were carried out to improve the vehicle’s dynamic performance. Furthermore, considering the extremely cold conditions in the Winter Olympics competition area and the poor low-temperature tolerance of core components of fuel cell electric vehicles (FCEV) under extremely cold conditions, such as the reduced capacity and service life of traction batteries caused by the rapid deterioration of charging and discharging characteristics, the manuscript proposed a fuzzy logic control-based energy management strategy (EMS) optimization method for the proton exchange membrane fuel cell (PEMFC), to reduce the power fluctuation, hydrogen consumption and battery charging/discharging times, and at the same time, to ensure the hybrid power system meets the varying demand under different conditions. In addition, the performance of the proposed approach was investigated and validated in an intercity coach in real-world driving conditions. The experimental results show that the proposed powertrain with an optimal control strategy successfully alleviated the fluctuation of vehicle power demand, reduced the battery charging/discharging times of traction battery, and improved the energy efficiency by 20.7%. The research results of this manuscript are of great significance for the future promotion and application of fuel cell electric coaches in all climate environments, especially in an extremely cold mountain area.

Receding horizon control based energy management strategy for PHEB using GRU deep learning predictive model

As an important method to achieve clean public transportation, plug-in hybrid electric bus (PHEB) has been gradually utilized in the transport system. The energy consumption performance of PHEB is mainly determined by its energy management strategy (EMS). Aiming at improving fuel economy of PHEB and making better use of battery, a receding horizon control (RHC) based EMS is proposed in this paper. Under the real driving cycles, the gated recurrent units (GRU) based model is utilized to predict the velocity sequence over receding horizon, and two other deep neural network prediction models are introduced for comparison. Moreover, the trip distance-based state of charge (SOC) constraint method is designed. Combining the velocity predictor and the SOC constraints, the power distribution of PHEB is described as a rolling optimization problem in the prediction horizon. The simulation and hardware-in-loop (HIL) experiments demonstrate that this strategy can improve fuel economy while ensuring the rational battery discharging under different initial SOC and traveling distance.

Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles

The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

Development of Fuzzy-Adaptive Control Based Energy Management Strategy for PEM Fuel Cell Hybrid Tramway System

Currently, the implementation of hybrid proton-exchange membrane fuel cell (PEMFC)-battery-supercapacitor systems for hybrid tramways to replace conventional internal combustion engines and reduce greenhouse gas emissions has triggered an upward trend in developing energy management strategies (EMSs) to effectively deploy this integration. For this purpose, this paper introduces a comprehensive EMS consisting of high-level and low-level controls to achieve appropriate power distribution and stabilize the operating voltage of the powertrain. In the high-level control, a fuzzy logic technique and adaptive control loop are proposed to determine the reference power for energy sources under different working conditions. Meanwhile, the low-level control aims to generate a pulse-width-modulation (PWM) signal for DC/DC converter, associated with each electric source, to regulate the device’s output performance and guarantee the DC bus voltage. Comparisons between the proposed strategy with available approaches are conducted to verify the effectiveness of the proposed EMS through MATLAB/Simulink environment. The simulation results confirm that the proposed EMS not only sufficiently ensures powers distribution even when the abrupt changes of load or high peak power, but also enhance the efficiency of the PEMFC, in which the PEMFC stack efficiency can be exhibited up to 53% with hydrogen consumption less than 21.4%. Moreover, the DC bus voltage can be regulated with a small ripple of around 1%.

Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship

An improved fuzzy-based energy management strategy (EMS) is proposed for a tourist ship used hybrid power system with multiple power sources consisting of fuel cell(FC)/photovoltaic cell(PV)/battery(BAT)/super-capacitor(SC). The power demand from propeller and user terminal is afforded by the power sources connecting to power converters. To obtain more superior performance of the power system, the maximum power point tracking (MPPT) algorithm is employed to optimize the PV. Meanwhile, the improved fuzzy logic control based on dynamic programming (DP) associated with wavelet analysis and PI control are employed to achieve the output power optimal distribution and online control. In particular, the MPPT algorithm can improve the utilization of solar energy, and the SC can well absorb the high frequency power and reduce the fluctuation of the battery and FC that exhibits the potential of their lifetime extension. The FC outputs the high and stable power satisfying the ship's power demand even under the extreme work conditions. The developed model is able to illustrate well in the operation process of the hybrid power system governed by the proposed EMS. In addition, compared with the rule-based strategy, the improved fuzzy-based EMS can reduce 14.39% hydrogen consumption and keep the consistency of battery SOC. • Hybrid power system consists of fuel cell(FC)/PV/battery/SC used for tourist ship. • Electrical model of the hybrid power system with transient responses is presented. • Improved fuzzy-based energy management for the hybrid power system is proposed. • Fuzzy logic control based on dynamic programming and wavelet analysis is designed.

Shifting strategy and energy management of a two-motor drive powertrain for extended-range electric buses

This paper studies the shifting strategy and energy management of a two-motor drive powertrain for extended-range electric buses. To do this, a multibody dynamic model is first established for transient investigation. A novel shift schedule is second proposed by integrating a priority factor for the engaging gear into the equivalent motor efficiency. According to vehicle speed and power, the shift schedule results in two possible cases: single-gear and double-gear change. Optimal shifting strategies are third recommended to eliminate the torque interruption under those possible gear changes. To optimize the torque allocation to two motors and minimize fuel consumption, a model predictive control-based energy management strategy is then developed. Finally, this paper simulates shift quality and energy economy in comparison with a conventional one-motor drive powertrain. The results show that torque interruptions generate large jerks in the one-motor configuration. Conversely, the two-motor configuration achieves great shift quality by eliminating the torque interruptions mostly during single-gear upshift or entirely under double-gear downshift. The two-motor drive powertrain also improves energy economy significantly by 9.1% compared to the conventional configuration.

A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle

A scalable, causal, adaptive optimal control-based energy management strategy for the fuel cell hybrid train is designed. As learned from the results of offline Pontryagin’s minimum principle (PMP)-based strategies, the convexity of the specific consumption curve is emphasized to improve the fuel economy. More important is that the dependency of the co-state on the state of charge (SoC) of batteries and the average fuel cell power is identified the first time. With the help of using the optimal control theory in a reverse way, a quantitative analytical formula is derived to determine the co-state based on the SoC and the average fuel cell power. The accuracy of the estimates, and the effectiveness of this strategy, under different weather, driving, and aging conditions, is validated by comparison to the results of offline PMP-based strategies. Thereby, a maximal deviation of the co-state average value compared to the offline results is 1.8%. An excellent fuel economy under a typical driving cycle of regional railway transports in Berlin, with only 0.03% more consumption for both summer and winter conditions, compared to the results of offline PMP, is resulted. Due to the model-based characteristics, the strategy can be scaled or transferred to other configuration systems or driving conditions without the loss of effectiveness.

Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles

Fuel cell hybrid construction vehicles are attractive for future fuel cell applications. Model predictive control as an energy management strategy has been applied to various hybrid electric vehicles. In this paper, a hierarchical model predictive control-based energy management strategy for fuel cell hybrid construction vehicles is explored. The hierarchical model predictive control framework consists of economic and control levels. The economic level considers economic costs, including of hydrogen consumption and components use cost. Real-time optimization is implemented at the economic level to identify the optimal reference trajectory. The control layer is a model predictive control that controls the system to follow the reference trajectory. A prediction methodology-based on wavelet transformation and Levenberg-Marquardt optimised neural network is proposed for the complex load prediction of fuel cell hybrid construction vehicle. The simulations performed with cyclic loading show that the accuracy of the proposed prediction methodology is satisfactory, and demonstrate the superiority of the proposed energy management strategy. The proposed hierarchical model predictive control-based energy management strategy considering economy and controllability is important for commercial application in hybrid electric vehicles.

Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway

In this paper, a tramway's system model is established to evaluate its longitudinal dynamics performance, where the model includes multiple masses point connecting together powered by the catenary and a hybrid energy-storage system which consists of a battery pack and ultracapacitor pack. To explore an optimal or appropriate real-time power distribution among these energy sources, a dual fuzzy logic control-based energy management strategy combined with wavelet transform and multi-objective optimization is proposed. The control parameters of the dual fuzzy logic controller are adjusted using a multi-objective particle swarm optimization algorithm while considering the daily operating cost of the tramway. To verify the effectiveness and validity of the proposed model and energy management strategy, its simulation results are further compared and verified with the field experimental results of a real tramway and a real driving cycle in China.

Modeling and energy management of a photovoltaic‐fuel cell‐battery hybrid electric vehicle

AbstractAutomobile power systems are increasingly in need of renewable and clean energy sources such as solar energy and fuel cells in the context of global warming. This article investigates the feasibility of a photovoltaic‐fuel cell‐battery hybrid electric vehicle (PVFCHEV) via a model‐based approach and delivers two major original contributions. First, a completed PVFCHEV system, which consists of the electric power system, the control system, and the vehicle powertrain system, is modeled in detail in Matlab/Simulink environment. Second, a fuzzy logic control based energy management strategy (EMS) is developed, aiming to maximize solar energy utilization under the fast‐changing power demand, the variance of solar energy, and different levels of battery state‐of‐charge. The simulation results of the functional test, including idling, accelerating, and cruising at different solar radiations and battery states, reveal that the designed vehicular power system and the EMS work reasonably well. Then, with the simulation of a particular driving cycle test in three typical scenarios (high, low, and zero solar radiances with different battery SOC), the power system proves to work effectively and adaptively in different situations under the regulation of the EMS and demonstrates better fuel economy than using a widely‐adopted power‐following control strategy.

Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems

Electrification is a clear trend for both commercial and military ship development. Shipboard load fluctuations, such as propulsion-load fluctuations and pulse power loads, can significantly affect power system reliability. In order to address this issue, this paper explores a real-time model predictive control based energy management strategy for load fluctuation mitigation in all-electric ships. A battery combined with ultra-capacitor hybrid energy storage system (HESS) is used as a buffer to compensate load fluctuations from the shipboard network. In order to implement the proposed real-time MPC-based energy management strategy on a physical testbed, three special efforts have been made to enable real-time implementation: a specially tailored problem formulation, an efficient optimization algorithm and a multi-core hardware implementation. Given the multi-frequency characteristics of load fluctuations, a filter-based power split strategy is developed as a baseline control to evaluate the proposed MPC. Compared to the filter-based strategy, the experimental results show that the proposed real-time MPC achieves superior performance in terms of enhanced system reliability, improved HESS efficiency, long self-sustained time, and extended battery life. The bus voltage variation and hybrid energy storage losses can be reduced by up to 38% and 65%, respectively.

Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction

A major advantage of plug-in hybrid electric vehicles is their high fuel economy, which is closely related to their energy management strategy and driving cycles. In this study, an improved velocity prediction method is formulated based on the Markov model and a back propagation neural network. The root mean square error of the predicted velocity for the New European Driving Cycle is 0.1511 m/s when the prediction time is 3 s. Moreover, a vehicle test for the velocity prediction algorithm is implemented on a hybrid electric bus, which verifies the reliability and real-time performance. On this basis, a model predictive control-based energy management strategy incorporating the velocity prediction is proposed. In order to lessen the computation and memory burden and constrain the battery's state of charge, a state of charge-based adaptive equivalent consumption minimization strategy is applied to the predictive control-based energy management strategy. By simulation, the proposed velocity prediction-based energy management strategy improves fuel economy by 3.11% and 7.93% for a plug-in hybrid electric commercial vehicle in the New European Driving Cycle, while 2.96% and 11.02% in the Worldwide Harmonized Light Vehicles Test Procedures, when compared with the adaptive equivalent consumption minimization strategy and equivalent consumption minimization strategy, respectively.

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

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.