Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles

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.

Exploiting driving history for optimising the Energy Management in plug-in Hybrid Electric Vehicles

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

In this paper, a novel velocity predicted method based on Wavelet Transform (WT) and Radial Basis Function Neural Network (RBF-NN) is introduced to achieve the predictive energy management for plug- in hybrid electric vehicles (PHEVs). Three cases including RBF-NN, 1 RBF-NN with WT and 2 RBF-NN with WT, are taken into account for comparison. The corresponding model predictive contorl (MPC) algorithm is employed to testify the improvement of fuel economy based on the predicted velocity profile. Simulation results show that the combined algorithm of 1 RBF-NN and WT can attain satisfactory performance, which increases the prediction accuracy by 7.31% compared with that by other two cases. In addition, the computational time of the proposed predictive method is 2.8649 ms, justifying that it can be efficiently employed in the practical application.

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy — Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy — Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC’. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed.

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Energy management of Plug-In Hybrid Electric Vehicles in renewable-based energy hubs

The multisource electromechanical coupling renders the energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy-saving improvement of PHEVs. To address these issues, a novel learning-based model predictive control (LMPC) strategy is developed for a serial–parallel PHEV with the reinforced optimal control effect in real-time applications. Rather than employing the velocity-prediction-based MPC methods favored in the literature, an original reference-tracking-based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in the LMPC-based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance.

<div class="section abstract"><div class="htmlview paragraph">Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data. The K-means clustering method was employed to evaluate the sensitivity of new factors to the energy consumption of PHEV, in which the sensitivity was defined through the Pearson correlation coefficient and covariance. Moreover, an optimal energy management strategy based on the sensitivity results and constrained optimization functions was used to optimize the speed-curve of PHEV. Furthermore, the 100 simulation tests based on MATLAB/Simulink platform were carried out to validate the feasibility of the proposed approach. The results indicate that 60% of the factors extracted from this paper show a strong sensitivity to the energy consumption of PHEV and the optimization method costs less computational time, leading to a better energy consumption performance. The proposed methodology is valuable for the research on real-time energy management of electric vehicles and speed control of intelligence vehicles under real-world road conditions.</div></div>

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle.

The development of intelligent transportation technology provides a great opportunity for energy efficiency improvement of electrified vehicles. However, for plug-in hybrid vehicles, eco-driving control usually involves three problems, including speed planning, SOC planning, and energy management. Solving the above three problems requires considering not only the fuel economy but also the computational efficiency. To this end, this paper proposes a hierarchical control strategy to improve driving comfort and fuel economy simultaneously for a PHEV. Specifically, three main contributions are presented to distinguish our efforts from the existing research. First, in the control framework, the traffic light information is utilized to calculate optimal driving speed by minimizing a multi-objective function. Then, the SOC planning problem is solved by convex optimization, while the fuel consumption is minimized by a predictive equivalent consumption minimization strategy. Second, the speed trajectories and fuel consumptions in the other two traffic scenarios with different traffic light SPaT (Signal Phasing and Timing) are presented to validate the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are analyzed and discussed.

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 onboard 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 s 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.