Reference State Of Charge Research Articles

Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km.

Energy management strategies based on soft actor critic reinforcement learning with a proper reward function design based on battery state of charge constraints

The environmental concerns have pushed our society to find solutions to reduce the emission of transportation systems. One promising solution is the fuel cell hybrid electric vehicle (FCHEV), with a proton exchange membrane fuel cell (PEMFC) and a lithium-ion battery. The presence of two different energy sources requires a smart energy management strategy (EMS) which must not only optimize energy consumption (conventional approaches), but also mitigate component’s degradation (New approaches : Health-conscious strategies). For that, a wide range of techniques has been proposed in the literature, and the most recent techniques are based on machine learning. Reinforcement learning (RL) is usually the most chosen strategy, but as in the optimization techniques, a strong design of the objective function (called reward function in RL) is required. The main challenge in the reward function is to encourage a proper battery state of charge (SOC) management, while reducing the energy consumption and avoiding the factors that accelerate the FC aging. The current advancement in the literature shows satisfactory results, but the reward function design have great improvement potential in the chosen approach. Indeed, most approaches in RL for SOC management with continuous actions are based on the reference SOC principle, which reduces the EMS ability to optimize the other part of the reward function. This paper reveals the challenges associated with the introduction of SOC limits in the reward function and proposes an approach, based on SOC boundaries in the reward function. The contribution allows to better consider the parts to optimize, as it gives more freedom than previous reference SOC techniques all reducing the consumption by 12.9%.

Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

A predictive energy management strategy for plug-in hybrid electric vehicles using real-time traffic based reference SOC planning

The fuel economy of plug-in hybrid electric vehicles (PHEVs) is strongly affected by the battery state of charge (SOC) depletion pattern. This paper proposes and studies a real-time traffic-based SOC reference planning method. The method uses a dataset to collect and capture real traffic information and then enriches the dataset using a data augmentation method developed in this paper. The augmented dataset is optimized by dynamic programing (DP) algorithm to obtain the optimal reference SOC for model training. The traffic information and optimal reference SOC are processed and used to train a long-short term memory (LSTM) neural network, which is used for online reference SOC planning. Finally, a predictive energy management (PEM) strategy is adopted to follow the SOC reference by optimizing instantaneous power allocation with the predicted velocities. Simulation results show that the proposed method outperforms the linear reference SOC planning method in both smooth and congested traffic scenarios.

Predictive energy management of fuel cell plug-in hybrid electric vehicles: A co-state boundaries-oriented PMP optimization approach

This paper proposes a predictive energy management strategy of FC PHEV based on PMP and co-state boundaries. The model predictive control (MPC) problem is established and transformed as a two-point-boundary-value one by PMP theory, and the physical constraints of FC power, FC power varying rate, and battery current, are merged by methodical derivatives. To gain the accurate co-state boundaries, the Karush-Kuhn-Tucker condition, for the first time, is employed to derive the general expressions, and a correction method is developed to modify the co-state boundaries for effectiveness guarantees. By inputting the feedback SOC, power demand, and the unified constraint, the shrunken enclosed range between the developed co-state boundaries can be determined in real time, thereby benefiting the efficient co-state calibration. Based on the co-state bounds, the concise but highly effective heuristic rules are proposed to calibrate the co-state online iteratively, and an analytical method is proposed to fast find the optimal solution of Hamilton function. The global optimality of the proposed strategy for the addressed MPC problem is also strictly proved. The validations and sensitivity analysis for the initial state of charge (SOC), the SOC reference, the predictive velocity accuracy, and the horizon length, are implemented under simulations, and the hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed strategy under on-board environment. The results yield that, the proposed strategy can implement the timely and high-efficiency co-state updates, the smooth control commands, the expected SOC tracking effects, and improve the fuel economy. Additionally, <0.5 ms per sample is spent for the predictive horizon length 40 under HIL tests, indicating the real-time applicability of proposed strategy.

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

State of charge estimation for lithium-ion battery based on improved online parameters identification and adaptive square root unscented Kalman filter

Accurate state of charge estimation is crucial for the safe and efficient operation of lithium-ion batteries. The Kalman filtering algorithm has been widely used in state-of-charge (SOC) estimation. To solve the problem of filter divergence and sensitivity to noise, the joint SOC estimation method is proposed to achieve accurate and robust estimation of SOC, which is composed of improved variable forgetting factor recursive least square (VFFRLS) and adaptive square root unscented Kalman filter (ASRUKF). The improved VFFRLS method uses the mean square value of the multi-step voltage residual instead of the traditional one-step voltage residual to correct the forgetting factor, and the ASRUKF improves the accuracy and stability of estimation by introducing the square root method and the adaptive method. Estimations under three typical working conditions are quantitatively studied, the experimental results show that the proposed method has good accuracy and stability. The maximum mean absolute error (MAE) is 0.0061, and the corresponding root mean square error (RMSE) is 0.0090. Furthermore, the experimental verification after adding offset errors to measured data shows that the proposed method can still track the reference SOC with satisfactory accuracy in the range of voltage offset error less than ±0.5 % and current offset error less than ±3 %, both for the constant offset error and the random error, and the SOC error is less than 2.5 %.

Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles

The connected vehicle technique has offered great opportunities to improve further plug-in hybrid electric vehicles (PHEVs) fuel economy. In this context, a predictive hierarchical eco-driving control scheme is proposed for connected PHEVs under a car-following scenario containing a cloud-layer speed planner and vehicle-layer energy management. In the cloud layer, based on the traffic data separately processed by the dynamic programming (DP) algorithm and k-means clustering method, the reference state of charge (SOC) model, the energy consumption model, the equivalent factor model and the variable-horizon speed predictor can be constructed, respectively. And for ensuring safety, comfort and fuel economy in the car-following process, the energy and SOC models are used separately as the index and state equation in the data-driven speed planning problem. Then, with the driving pattern recognition technique, the power allocation under the planned speed can be conducted by integrating the adaptive equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC), thus guaranteeing fuel economy, adaptability and near-global optimality with high computational efficiency. Compared with other strategies, the effectiveness and advantages of the proposed scheme are validated in the joint simulation platform of MATLAB/Simulink and GT-SUITE.

Long-term operation of isolated microgrids with renewables and hybrid seasonal-battery storage

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization.

Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results

Plug-in hybrid electric vehicles (PHEVs) have gradually become an important member of new energy vehicles because of the advantages of both electric and hybrid electric vehicles. A fast and effective energy management strategy can significantly improve the fuel-saving performance of vehicles. By observing the dynamic programming (DP) simulation results, it was found that the vehicle is in the charge-depleting mode, the state of charge (SOC) drops to the minimum at the end of the journey, and the SOC decreases linearly with the mileage. As such, this study proposed an improved rule-based (IRB) strategy enlightened by the DP strategy, which is different from previous rule-based (RB) strategies. Introducing the reference SOC curve and SOC adaptive adjustment, the IRB strategy ensures that the SOC decreases linearly with the driving distance, and the SOC drops to the minimum at the end of the journal, similar to the result of the DP strategy. The fuel economy of PHEV in the RB and DP energy management strategies can be considered as their worst-case and best-case scenarios, respectively. The simulation results show that the fuel consumption of the IRB strategy under the China Light-duty Vehicle Test Cycle is 3.16 L/100 km, which is 7.87% less than that of the RB strategy (3.43 L/100 km), and has reached 44.41% of the fuel-saving effect of the DP strategy (2.84 L/100 km).

A novel pseudo-open-circuit voltage modeling method for accurate state-of-charge estimation of LiFePO4 batteries

LiFePO4 batteries are widely used in electric vehicles and energy storage due to their high safety. However, their flat voltage characteristics in the middle state of charge (SOC) range make it difficult to correct SOC estimation errors with a feedback mechanism. This study proposes a pseudo-open circuit voltage (OCV) modeling method to improve the performance of a closed-loop feedback correction. First, the relationship between the derivative of OCV and SOC or the slope of OCV, SOC range, and estimation error is analyzed to select the SOC interval for OCV curve construction. Second, a pseudo-OCV curve is established. An optimization algorithm is developed to identify the parameters of the OCV model based on the selected SOC interval and OCV slope, which establishes a pseudo-OCV curve. Third, the pseudo-OCV is compared with the real OCV to adjust the selected interval and further determine the optimal construction interval, then the curves of the pseudo-OCV and real OCV are stitched together to obtain the optimal OCV curve for global SOC estimation. Finally, SOC estimation is carried out using the constructed full pseudo-OCV and compared with the reference SOC obtained from experiments. The results show that the SOC estimation error at the full SOC range is less than 3 %.

Real‐Time Energy Management Strategy for Fuel Cell Plug‐In Hybrid Electric Bus Using Short‐Term Power Smoothing Prediction and Distance Adaptive State‐of‐Charge Consumption

To improve energy consumption economy and fuel cell life economy, this article proposes a multiobjective real‐time energy management strategy based on short‐term power smoothing prediction and distance adaptive state‐of‐charge (SOC) consumption for fuel cell/battery plug‐in hybrid electric bus. First, a distance adaptive SOC real‐time reference trajectory planning method is proposed by collecting the actual operating condition data of Dalian city buses. Second, the motor power is smoothed using the first‐order exponential smoothing approach, and the long short term memory network is adopted to predict the smoothed motor power. Finally, based on the predicted motor power and a real‐time reference SOC, a model predictive control strategy is designed in the finite prediction horizon, and dynamic programming is used to solve multiobjective cost functions to achieve optimal power allocation. Simulation results show that the proposed strategy has remarkable performance in terms of the total operating cost, SOC sustenance, and fuel cell degradation compared with the rule‐based strategy. In addition, the average time of online energy distribution in each step of the proposed strategy is 43.8 ms, which is much smaller than the simulation step length of 1s.

Integrated energy and thermal management strategy for extended range electric vehicle based on battery temperature and state-of-charge global planning

There is a coupling relationship between energy and thermal management of extended range electric vehicle (EREV), so developing an integrated energy and thermal management strategy (IETMS) is an effective approach to reduce fuel consumption and battery degradation and further improve vehicle driving economy. Global optimization of EREV energy and thermal management is solved by dynamic programming based on adaptive grid optimization (AGO-DP), which enhances optimization performance by narrowing the feasible domain of battery temperature and state-of-charge (SOC). Meanwhile, within the hierarchical model predictive control (MPC) framework, a novel IETMS based on battery temperature and SOC global planning is proposed in this study. According to the road and weather information, the battery temperature and SOC reference trajectories approximating to the global optimum are generated to guide real-time control. Then MPC is formulated to minimize total operating cost by coordinating APU output power and battery cooling power in the short-term prediction horizon. The simulation results show that the proposed IETMS comprehensively optimizes the vehicle economy from the aspects of tracking reference trajectory, reducing battery peak current and improving fuel efficiency. The total operating cost decreases by 1.77%–14.52% compared with other strategies. Moreover, the proposed IETMS also exhibits desirable versatility and robustness under different driving scenarios, achieving 86.60%—92.18% of the optimal performance.

Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning

Vehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.

Noise-immune state of charge estimation for lithium-ion batteries based on optimized dynamic model and improved adaptive unscented Kalman filter under wide temperature range

Due to diverse vehicle driving conditions, it is difficult to ensure that lithium-ion batteries operate at a fixed ambient temperature. Meanwhile, the environmental noise accompanying vehicle driving can also affect the data sampling accuracy of batteries. To achieve a high-robustness state of charge (SOC) estimation for lithium-ion batteries at different ambient temperatures with noise effects, a noise-immune SOC estimation method under a wide ambient temperature range is proposed. First, based on the moving window principle, a high-precision fitting model of the open-circuit voltage is established. Second, based on the Multi-Verse Optimizer, a new dynamic model parameter identification method is proposed, while the complete optimized dynamic battery model is established relying on the data of Dynamic Stress Test at different temperatures. Third, to enhance the SOC estimation accuracy and stability, based on adaptive theory and matrix diagonalization theory, the unscented Kalman filter is improved. Finally, the effectiveness and robustness of the proposed method are validated under two other working conditions with random noise added at various temperatures. Under every set of working conditions at all temperatures, the SOC estimation results can maintain stability after converging to the reference SOC, while root mean square errors and mean absolute errors under all cases do not exceed 1.5 %.

A New HEV Power Distribution Algorithm Using Nonlinear Programming

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

Online state-of-charge estimation refining method for battery energy storage system using historical operating data

In battery energy storage systems (BESS), state-of-charge (SoC) is of great significance to optimize the charge and discharge schedules. Some existing SoC estimators implemented in battery management system (BMS) of BESS may suffer from significant error, which will cause permanent damage to service life or economic loss. This paper identifies the causes of inaccurate SoC in the practical BESS and confirms the result with laboratory test. On this basis, an online method based on historical operating data is proposed to refine real-time SoC estimation from BMS. In the proposed refining method, SoC reference points are initially located from historical time-series data and the maximum available capacity of charge or discharge are further determined with a weighted least squares fitting. Finally, refined SoC estimation result can be determined by enhanced coulomb counting method. The experimental results based on laboratory test data and operation data from a practical BESS prove that the proposed SoC refining approach can effectively provide more accurate estimation.

Optimal economic dispatch policy for prosumer with energy storage considering self-consumption demand

This paper analyzed the effects of self-consumption demand on the joint economic dispatch of prosumers (energy consumers who are also producers), particularly for prosumers with both energy storage and distributed energy sources (DERs). Studies in the existing literature on the economic dispatch scheduling policy of energy storage, mostly from the perspective of electricity merchants, do not address the impacts of self-consumption demand. However, due to the intermittent and high levels of uncertainty regarding DERs generation and the dynamic demand of the prosumer, production and consumption are not always simultaneous; there are two possible scenarios in each period depending on whether DERs generation can meet prosumers' self-consumption or not. Incorporating the self-consumption demand will pose modeling challenges since these two scenarios cannot occur simultaneously in each period, and different scenarios require different decisions for prosumers. This paper analyzed the two scenarios separately to find the optimal storage scheduling strategy, and the results were combined to get the optimal global solution. We focused on prosumers' economic decision-making while considering self-consumption demand and the physical constraints of a battery based on dynamic programming. Our study showed that the feasible state of charge (SOC) range of storage can be segmented into several sub-ranges by SOC reference points under the above two scenarios. As a result, a prosumer's optimal scheduling can be uniquely and conveniently selected based on the sub-ranges within which the current SOC falls. The results, therefore, provided multistage decision-making guidance for prosumers with energy storage.

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

A predictive energy management strategy is proposed for plug-in hybrid electric vehicles, which adopts a new high-precision vehicle speed prediction algorithm and a real-time reference state of charge (SOC) trajectory planning method. The speed prediction algorithm includes a modified Markov model based on vehicle speed features recognized by the self-organizing map neural network method, Fourier approximation, and moving average filtering. The prediction accuracy of the algorithm, measured by the root mean square error, is improved by 32.28% and 23.33%, respectively, over that of the standard Markov model when the prediction time is 5 and 10 s. To approach the optimal management strategy for improved vehicle fuel economy, the radial basis function neural network method is utilized to correlate the 10 s predicted vehicle speed with the trajectory slope of the suboptimal SOC for obtaining the reference SOC trajectory, which is applied to the adaptive equivalent consumption minimization strategy based on SOC feedback. The simulation indicates that the fuel consumption by using the proposed energy management strategy is very close to that by the theoretical suboptimal strategy with the shooting method, which leads to a superior fuel economy among the strategies studied.