Hybrid Vehicle Energy Management Research Articles

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Multi-objective optimization-driven machine learning for charging and V2G pattern for plug-in hybrid vehicles: Balancing battery aging and power management

This research study aims to optimize the economic energy management of a plug-in hybrid vehicle by maximizing revenue through vehicle-to-grid interactions during peak hours, while also prioritizing the reduction of battery aging as an additional objective. The study focuses on the Mitsubishi Outlander vehicle in New York City, analyzing charging and V2G times, rates, and ambient temperatures to understand their impact on lithium-ion battery aging. Using a machine learning approach, a deep neural network is trained with time, rate, and temperature inputs to improve accuracy in predicting battery aging effects. A multi-objective optimization algorithm, NSGA-II, is employed to balance the trade-off between maximizing revenue and minimizing battery aging based on the neural network's predictions. The results indicate that the best time for charging is before starting the travel, with a 3.95 % battery aging. Raising the charging amperage and implementing V2G elevate battery aging to 7.83 %. Furthermore, higher ambient temperatures can accelerate battery aging significantly. A temperature rise from 10 °C to 30 °C could potentially result in a 12.23 % increase in battery aging. Through optimum selection of charging and V2G time, rate, and temperature, the study suggests that an income of $1043 and a battery aging rate of 7.2 % can be achieved within one year.

A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

Optimized energy management for plug-in hybrid vehicles with predicted driving cycles1

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study.

A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS.

Research on the complete vehicle control strategy of the composite accumulator hydraulic hybrid power system

The compound accumulator is an energy storage device consisting of a large accumulator and a small accumulator. Compared with the traditional single accumulator hydraulic hybrid vehicle, it has the characteristics and advantages of fast braking response for the small accumulator and more energy recovery for the large accumulator, combining braking characteristics and energy recovery rate. This paper proposes the control strategy for parallel hydraulic hybrid vehicle based on compound accumulator, which can determine the working timing for large and small accumulators according to the specific driving conditions, thereby improving the braking performance and fuel economy for the vehicle. Under NEDC conditions, this paper uses dynamic programming algorithm to find the optimal control sequence for torque distribution and energy management of the hydraulic hybrid vehicle with composite accumulator, obtains the control rules and logic thresholds of the logic threshold control strategy, and obtains the switching rule of the working timing of the composite accumulator. The simulation of specific cycle conditions is carried out to verify the rationality of vehicle control strategy and to explore the energy-saving mechanism of parallel hydraulic hybrid power system based on composite accumulator. The NEDC urban cycle test was carried out, and the comparison between the test and simulation results showed that the hydraulic hybrid operation mode, large and small accumulator pressure, and engine fuel consumption during the test were basically consistent with the simulation results and verified the control effect of the vehicle control strategy and the energy-saving mechanism of the parallel hydraulic hybrid system based on the composite accumulator.

Real-time energy management strategy for fuel cell/battery vehicle based on speed prediction DP solver model predictive control

Fuel cell hybrid electric vehicles (FCHEVs) are widely considered to be an ideal substitute for the traditional vehicles. However, their high costs of operation make them uncompetitive in the current automotive market. To maximize the economy of fuel cell/battery-based hybrid electric vehicles, herein, integrating with speed prediction model, a dynamic programming solver model predictive control (DPS-MPC) strategy is applied for the hybrid vehicle energy management. The main principle of the proposed DPS-MPC is to obtain the manipulated variable that minimizes the vehicle operating cost by using the DP solver according to the information of state variables and disturbance variable. Assisted by the predictive vehicle speed, the future demand power of the vehicle is acquired by vehicle dynamic model. The demand power imports into the energy management system response prediction model for improving the control performance by considering more exact disturbance. Additionally, a comparative study is conducted to verify the improvement of economic performance by proposed strategy. The results indicate that DPS-MPC strategy can respectively reduce the operating cost by 21 % in average versus a normal MPC strategy. Moreover, the average computing time per step is 9.27 ms, less than the sampling time 1 s. In order to verify the real-time performance of the DPS-MPC strategy, a hardware in the loop test is conducted.

Control of Fuel Lithium Battery Hybrid Vehicle Power Supply System and Vehicle Energy Management Strategy

<p>The main research object of this article is the increasing number of new energy vehicles, focusing on the energy management issues of new energy vehicles. Firstly, a real car model was established: an engine mathematical model, an electric motor mathematical model, and a power battery mathematical model. Then, in order to achieve optimal management of vehicle energy management, an algorithm model for vehicle energy dynamic planning was established with the goal of minimizing energy consumption. Finally, simulation experiments were conducted to verify the performance improvement of the proposed algorithm in vehicle energy management.</p> <p> </p>

Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives

As a result of global actions for decarbonization, the rapid development of electrified vehicles (EV), including plug-in hybrid vehicles (PHEV) leads to increasing demands for maximizing battery useful life since recycling EV batteries would bring new environmental issues. PHEVs can have a higher fuel economy while maintaining the battery state-of-charge (SoC) through the equivalent consumption minimization strategy (ECMS); however, it is still a great challenge to maximize battery life through PHEV control since it is hard to balance diverse time scale control objectives (fuel economy, SoC control and the battery life). To this end, this paper proposes a Cuboid Equivalent Consumption Minimization Strategy (C-ECMS) for multimode PHEVs. A new concept of the “Hamiltonian matrix” is introduced by adding an additional control degree-of-freedom to the conventional Hamiltonian vector to enable optimal dual motor control in the multi-mode PHEVs. Then, an aging factor (AF) is introduced in associated with the equivalent factor (EF) to generate three Hamiltonian matrices that establish a cuboid knowledge base for the optimal control considering diverse time scale objectives. Experiments under five different driving cycles are conducted to study 1) the impact of Hamiltonian matrix dimensions on SoC control accuracy and 2) the impact of EF and AF settings on the Pareto Frontier considering fuel economy, SoC accuracy, and battery aging. The unified setting for C-ECMS is obtained based on the experimental study, and the result is demonstrated by a comparison study with the rule-based strategy and the standard ECMS implemented in the same PHEV. The results show that the proposed C-ECMS outperforms the standard ECMS control and achieves more accurate SoC sustaining (0.4% of SoC error), less fuel consumption (8.4% improvement), and less battery capacity loss (10.4% improvement).

Integrated DC/DC Converter Topology Study for Fuel Cell Hybrid Vehicles with Two Energy Sources

Conventional hybrid vehicles with two energy sources require two separate on-board DC/DC converters to connect the battery and the fuel cell, which have the disadvantages of large size, high cost, high losses and few applicable operating conditions. To address this situation, this paper proposes an optimized on-board integrated DC/DC converter with a non-isolated multi-port scheme that integrates a unidirectional port for the fuel cell and a bidirectional port for the battery and load. This can achieve a combined energy supply and recovery with a single integrated converter, effectively overcoming the above disadvantages. The optimized converter topology is relatively simple, and the magnetic losses of the transformer are removed. Furthermore, the switched capacitor is introduced as a voltage doubling unit to achieve high-gain output, so the fuel cell and battery voltage demand levels are reduced under the same load conditions. In addition, it has superior performance in system energy management for hybrid vehicles, which can distribute power and switch operating states by controlling the on/off of switching devices to make it suitable for five driving conditions. This paper discusses in detail the operating principles of the converter and analyzes its steady-state performance under five operating modes, derives its dynamic model, and proposes a proportional-integral control scheme. Finally, the simulation model of the topology is built by Matlab/Simulink software to verify the converter operation in each driving state, and the simulation experimental results verify the applicability of the proposed integrated DC/DC converter topology.

Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

The present study investigates an energy management strategy based on reinforcement learning for series-parallel hybrid vehicles. Hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

Dynamic programming (DP) is frequently used to obtain the optimal solution to the hybrid electric vehicle (HEV) energy management. The trade-off between the accuracy and the computational effort is the biggest problem for the DP method. The closed-form solution to the DP is proposed to solve this problem. Firstly, the affine linear model of the engine fuel rate is obtained based on engine test data. The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery. Then, the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split. The results show that this method can greatly reduce the computing burden by 93% while ensuring near-optimal fuel economy compared with the conventional DP method.

Recent Progress in Learning Algorithms Applied in Energy Management of Hybrid Vehicles: A Comprehensive Review

Recent Progress in Learning Algorithms Applied in Energy Management of Hybrid Vehicles: A Comprehensive Review

Engine RPM and Battery SOC Activation Optimization in Hybrid Vehicle Energy Management System Utilizing BPNN - Genetic Algorithm and BPNN – Particle Swarm Optimization

The energy used in the hybrid vehicle needs to be regulated to gain further mileage and lower fuel consumption. It is achieved by selecting the correct levels of hybrid energy management system (EMS) parameters (i.e., vehicle speed, engine RPM, and activation State of Charge (SOC) of battery). This study focused on the modeling and optimization of Sepuluh Nopember Institute of Technology (ITS)’s series plug-in hybrid electric vehicle (PHEV) car mileage and fuel consumption by comparing the backpropagation neural network (BPNN) method – genetic algorithm (GA) and BPNN – particle swarm optimization (PSO). The BPNN was used to model the character of ITS’s series PHEV EMS and predict mileage and fuel consumption. The BPNN’s model obtained the best EMS parameters, most extended mileage, and minimum fuel consumption. The result of the validation experiment showed that both the integration of BPNN - GA and BPNN - PSO were able to predict and optimize the multi-objective characteristic with the same results.

Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles

Nowadays, the automotive market has showed great interest in the diffusion of Hybrid Electric Vehicles (HEVs). Despite their low emissions and energy consumptions, if compared with traditional fossil fuel vehicles, their architecture is much more complex and presents critical issues in relation to the combined use of the internal combustion engine (ICE), the electric machine and the battery pack. The aim of this paper is to investigate lithium-ion battery usage when coupled with an optimization-based strategy in terms of the overall energy management for a specific hybrid vehicle. A mathematical model for the power train of a Peugeot 508 RXH was implemented. A rule-based energy management system (EMS) was developed and optimized using real data from the driving cycles of two different paths located in Messina. A mathematical model of the battery was implemented to evaluate the variation of its voltage and state of charge (SOC) during the execution of driving cycles. Similarly, a mathematical model was implemented to analyze the state of health (SOH) of the battery after the application of electrical loads. It was thus possible to consider the impact of the energy management system not only on fuel consumption but also on the battery pack aging. Three different scenarios, in terms of battery usage at the starting SOC values (low, medium, and maximum level) were simulated. The results of these simulations highlight the degradation and aging of the studied battery in terms of the chosen parameters of the rule-based optimized EMS.

Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle.

Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.

Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression

Electric vehicles, including plug-in hybrids, are important for achieving net-zero emission and will dominate road transportation in the future. Energy management, which optimizes the onboard energy usage, is a critical functionality of electric vehicles. It is usually developed following the model-based routine, which is conventionally costly and time-consuming and is hard to meet the increasing market competition in the digital era. To reduce the development workload for the energy management controller, this paper studies an innovative transfer learning routine. A new transferable representation control model is proposed by incorporating two promising artificial intelligence technologies, adaptive neural fuzzy inference system and Gaussian process regression, where the former applies k-fold cross valudation to build a neural fuzzy system for real-time implementation of offline optimization result, and the later connects the neural fuzzy system with a ‘deeper’ architecture to transfer the offline optimization knowledge learnt at source domain to new target domains. By introducing a concept of control utility that evaluates vehicle energy efficiency with a penalty on usage of battery energy, experimental evaluations based on the hardware-in-the-loop testing platform are conducted. Competitive real-time control ultility values (as much as 90% of offline benchmarking results) can be achieved by the proposed control method. They are over 27% higher than that achieved by the neural-network-based model.

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.

Real-Time Implementation of Improved Power Frequency Approach Based Energy Management of Fuel Cell Hybrid Vehicle Considering Storage Limitations

Real-Time Implementation of Improved Power Frequency Approach Based Energy Management of Fuel Cell Hybrid Vehicle Considering Storage Limitations

Computational Reduction of Optimal Hybrid Vehicle Energy Management

Pontryagin's Minimum Principle is a way of solving hybrid powertrain optimal energy management. This letter presents an improvement of a classical implementation. The core of this improvement consists in relaxing the tolerance on some intermediate steps of the algorithm in order to reduce the number of iterations and thereby reducing the number of operations required to compute an optimal solution. This letter describes both a classical implementation of Pontryagin's Minimum Principle as well as the improved version. Numerical simulations are conducted on an academic example to demonstrate the benefits of the proposed approach.