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The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based o...
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... the data transmitted via the CAN bus did not have the same level of precision. Before applying the NN framework to train the optimal setting, the precision of each node training data was adjusted according to transmission accuracy of the CAN bus data, which is listed in Table 2. The detailed design and application procedure used to train the controller based on DP and NN for EREVs are illustrated in Figure 5. ...
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... They reduce the real-time computational cost and achieve energy-saving performance close to global optimization approaches but are highly dependent on environmental information. AI-based approaches are promising energy management solutions for MPS-EVs, which include machine learning and deep learning-based knowledge migration methods [13,14,15] and RL-based active control methods [16,17]. The RL demonstrates impressive capabilities for robust regression analysis and strategy development, and the RL-based EMS has shown substantial potential for real-time optimal control in the energy management problem of MPS-EV [18]. ...
... the motor speed at any given moment. The battery state of charge constraint and penalty function is expressed as: (15) where denotes the at moment ; and are the penalty functions for overcharging and over discharging, which are often set according to the needs of the research. Therefore, the objective function for MPS-EV energy management considering the battery charge state can be defined as: ...
Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.
... The optimal strategy is obtained only for that particular cycle. Therefore, given the complex and variable driving conditions in the real world, such energy management strategies cannot be applied in practice and are often used as references for evaluating and optimizing other energy management strategies [11,12]. With the development of energy management, algorithms such as model predictive control (MPC) obtain locally optimal solutions by continuous roll-forward optimization within the prediction-sight distance, which are neither short-sighted nor sensitive compared to instantaneous optimization algorithms. ...
... The information used for updating the cell state c t , f t , and o t are determined by the gating vectors in Formulas (7)- (10). The cell state and output are updated by Formulas (11) and (12). The cell state is reset or restored by f t and the new state c t is obtained by adding partial information through the input gate i t , while the hidden state h t is controlled and updated by the output gate o t . ...
... The information used for updating the cell state ct, ft, and ot are determined by the gating vectors in Formulas (7)-(10). The cell state and output are updated by Formulas (11) and (12). The cell state is reset or restored by ft and the new state ct is obtained by adding partial information through the input gate it, while the hidden state ht is controlled and updated by the output gate ot. ...
To improve the performance of predictive energy management strategies for hybrid passenger vehicles, this paper proposes an Encoder–Decoder (ED)-based velocity prediction modelling system coupled with driving pattern recognition. Firstly, the driving pattern recognition (DPR) model is established by a K-means clustering algorithm and validated on test data; the driving patterns can be identified as urban, suburban, and highway. Then, by introducing the encoder–decoder structure, a DPR-ED model is designed, which enables the simultaneous input of multiple temporal features to further improve the prediction accuracy and stability. The results show that the root mean square error (RMSE) of the DPR-ED model on the validation set is 1.028 m/s for the long-time sequence prediction, which is 6.6% better than that of the multilayer perceptron (MLP) model. When the two models are applied to the test dataset, the proportion with a low error of 0.1~0.3 m/s is improved by 4% and the large-error proportion is filtered by the DPR-ED model. The DPR-ED model performs 5.2% better than the MLP model with respect to the average prediction accuracy. Meanwhile, the variance is decreased by 15.6%. This novel framework enables the processing of long-time sequences with multiple input dimensions, which improves the prediction accuracy under complicated driving patterns and enhances the generalization-related performance and robustness of the model.
... The optimal range strategies can be significantly improved using machine learning and mathematical optimization methods [16]. For instance, promising results were achieved for neural networks utilizing two parameters: battery SOC and distance to the gas stations [43]. A much more complex solution to the same issue was proposed in the paper [44], where stochastic model predictive control is employed to identify the driver's behavior. ...
Extended-Range Electric Vehicles (EREVs) are intended to improve the range of battery electric vehicles and thus eliminate drivers’ concerns about running out of energy before reaching the desired destination. This paper gives an insight into EREV’s performance operating according to the proposed control strategy over various driving cycles, including the Worldwide Harmonized Light-duty Test Cycle Class 3b (WLTC 3b), Federal Test Procedure (FTP-75), and China Light-Duty Vehicle Test Cycle (CLTC-P). Simulation runs were performed in Matlab-Simulink® for different cases of drive range, electricity mix, and vehicle mass. The control strategy goal was to aim at a specified value of battery state of charge at the targeted range value. The obtained test results included: pure electric drive range, acceleration times, EREV range tests, control strategy range errors, Range Extender (REX) utilization metric and distribution of its engagement instances, fuel consumption, total equivalent CO2 emission, powertrain efficiency, and specific energy consumption. The control strategy operated on average with a range error of −1.04% and a range mean square error of 2.13%. Fuel consumption (in range extension mode) varied between 1.37 dm3/100 km (FTP-75) and 6.85 dm3/100 km (WLTC 3b Extra-High 3). CO2eq emission was 95.3–244.2 g/km for Poland, 31.0–160.5 g/km for EU-27, and 1.2–147.6 g/km for Sweden. This paper is a valuable source of information for scientists and engineers seeking to learn the advantages and shortcomings of EREV drives with a proposed control strategy, based on various sets of results.
... In Xi's research [35], NN was utilized to predict the extender output power for the extended range electric vehicle (EREV). The proposed NN was well trained against the DP optimization results over three artificial driving cycles constructed by repeating US06 two, three, and six times, respectively. ...
... On the contrary, the test vehicle operates in a charge-sustaining mode after arriving at 120-km mark, as the battery SOC has already reached the lower limit. Thus, Xi at el. [35] proposed introducing the electricity consumption per unit distance calculation module, P Cor , to correct the predicted extender power output to ensure the terminal SOC of 0.3. ...
For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper proposes an intelligent equivalent consumption minimum strategy based on dual neural networks and a novel equivalent factor correction, which can adaptively regulate the equivalent factor to achieve the near-optimal fuel economy without the support of the state of charge reference. The Bayesian regularization neural network is constructed to predict the near-optimal equivalent factor online, while the backpropagation neural network is designed to forecast the engine on/off with the aim of improving the quality of equivalent factor prediction. The corresponding neural network training takes advantage of the global optimality of dynamic programming. Besides, the novel equivalent factor correction can guarantee that the electrical energy is gradually consumed along the trip and the terminal battery state of charge satisfies the preset constraints. A series of virtual simulations under a total of nine driving cycles demonstrates that the proposed method can deliver a competitive fuel economy comparing to the optimal solution derived from the dynamic programming, as well as regulating the battery state of charge to reach the desired terminal value at the end of the trip.
... The results showed that the optimized operation points of the engine could reach a high efficiency of 40.2% [73]. In Ref. [78], a neural network controller was designed to train data from two inputs: the battery SOC and distance to the gas stations. Stefano et al. [72] applied stochastic model predictive control to identify the driver behaviour, and the engine-battery power ratio changes according to the driving style and traffic condition. ...
To address the urgent environmental challenges of transportation related air pollution and energy shortage, hybrid electric vehicles (HEV) and battery electric vehicles (BEV) with potential for higher energy efficiency are gaining in popularity and gradually replacing those conventional vehicles. Extended range electric vehicle (EREV) is a subset of these new energy vehicles aiming to gain benefits of both HEVs and BEVs and provide a solution to reducing tailpipe emissions whilst providing satisfactory driving-range compared with tradition internal combustion engine (ICE) vehicle counterparts. This paper systematically introduces the current developments of EREV in terms of the powertrain structure, energy management and vehicle performance, while the two key focus areas of auxiliary power unit (APU) charging control and (hybrid) energy storage system ((H)ESS) power management. A case analysis on a typical EREV is presented to study the application potential. Comprehensive comparisons of popular APU control strategies and different energy storage dispositions are shared, several existing issues are also discussed in this paper. Furthermore, a general manufacturing cost analysis for different types of vehicles is presented, and the outlook of EREV for future research is presented.
... In the interest of simulation accuracy, dynamic programming (DP) is widely recognized as a promising algorithm to find the optimal fuel consumption of APU. In [22], DP algorithm is constructed as a benchmark to compare a Neural network control strategy, considering the vehicle remaining SOC and the distance to charging stations. However, its training driving cycle is NEDC which is no longer suitable for electric vehicles. ...
... Therefore, traffic pattern has a direct influence on the energy consumption and the control strategy operation, where scholars generated several driving cycles and classified them to train the strategy parameters [15,25]. From the recent studies, numerous optimization algorithms relying on forgone driving cycle are proved dependable and effective [22,26]. New and suitable driving cycles should be employed for the accuracy of the energy management study. ...
Urban logistics holds the lifeline of global business and consumption demand. The amount of transport vehicles rises gradually but also leads to graver emission problem. Therefore, electrified vehicles are considered as better options to substitute traditional logistics vehicles. Extended range electric logistics van (ERELV) provides a solution by providing satisfactory driving-range and lower production cost than battery electric logistics van (BELV). This work aims to present a thorough energy consumption and Total cost of ownership (TCO) analysis for an extended range electric van to present its energy potential. Both the ERELV and BELV mathematical model are constructed and compared, and their long-term battery degradation comparison is studied for the first time. Dynamic programming is adopted in the energy management strategy for energy consumption optimization, and the global optimization result reveals the optimal energy consumption of the ERELV. Comparative results demonstrate that the ERELV has a relatively longer drive distance, slower battery aging trend and cheaper TCO (6.6%) when comparing to the BELV. The adoption of ERELV will promisingly reduce operating cost as the alternative transportation solution for urban logistics.
... Results obtained from optimization algorithm-based EMSs are usually used as the benchmark to improve or evaluate other EMSs. [16][17][18] In addition, optimization algorithms such as the genetic algorithm and particle swarm optimization are also often used to optimize the parameters of EMSs to achieve better control effects. [19][20] In some researches, the model predictive control (MPC) is applied in the EMSs of MPSDSs. ...
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex energy management issue of the HCEP is simplified by introducing a simple power allocation method. And, the simplified energy management issue is solved by the Dynamic Programming to obtain the offline optimal working mode sequences of the HCEP. Second, the online working mode decision rules of the HCEP are established according to the obtained working mode sequences. And, the auxiliary rules in the decision rules are further optimized for different types of driving conditions. Then, the principal component analysis and generalized regression neural network are used to construct the driving condition recognizer (DCR) with high prediction accuracy. And, based on the constructed DCR, working mode decision rules, and introduced power allocation method, an online adaptive EMS is developed for the HCEP. Finally, the rationality of the introduced power allocation method and the effectiveness of the developed online adaptive EMS are verified. This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) applied in vehicles. This online adaptive EMS can not only ensure the energy‐saving effect of the HCEP, but also can effectively avoid frequent working mode switching, as well as has adaptive ability to different driving conditions.
... Still, as long as devices and equipment are related to the term smart, their in timber based on Bluetooth low energy (BLE) and a web-based monitoring system. Other extender applications can be found in [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. ...
... Saban et al. [27] developed a sensor node-based network for remote moisture measurement in timber based on Bluetooth low energy (BLE) and a web-based monitoring system. Other extender applications can be found in [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. ...
Today, the world’s electricity consumption is growing rapidly, and therefore energy demands are also increasing. In the past few decades, various measures have been taken to improve equipment and system design to increase production and transmission efficiency and reduce power consumption. This article proposes a novel Internet of Things (IoT)-based temperature control power extender with two working modes of cooling and heating to solve power shortages. The power is turned on or off accurately and in a timely manner through a temperature-sensing element, thereby avoiding unnecessary power consumption to achieve the goal of energy-saving. This can directly power on or off the power extender through the Internet. It can also use a 2.4G Wi-Fi wireless transmission to transmit, for example, real-time temperature information, the switch status and the master–slave mode. Related data can be controlled, collected and uploaded to the cloud. Each proposed power extender’s temperature setting in a large-scale field can be set uniformly, and no staff is wasted to set the temperature separately. Taking a general industrial electric fan as an example, if it is changed to drive with this temperature control extension cable, and assuming that the industrial electric fan is activated for 900 s per hour, its power-saving rate is 74.75%
... The power for IEVs' motion is embodied as the energy flow, the dynamic process of energy conversion, consumption and recovery [12]. Flow and transfer characteristics of energy flow is established as the physical models of motor and battery [13]. The change of vehicle states represented by kinematics and compliance has typical characteristics of substance flow and controlled by the vehicle dynamic control system [14]. ...
Minimum energy consumption with maximum comfort driving experience defines the ideal human mobility. Recent technological advances in most highly automated driving systems on electric vehicles with regenerative braking system not only enhance the safety and comfort level but also present a significant opportunity for automated eco-driving. This research focuses on the longitudinal eco-driving considering the coordinated control for 4WD intelligent electric vehicles. The intelligent electric vehicle framework with 4-wheel hub motors is established and the intention-aware longitudinal automated driving strategy for overall traffic situation levels is proposed. Further, the coordinated control strategy arbitrates the control mode basing on the traffic situation level and distributes braking forces between the electronic hydraulic braking system and the cooperative regenerative auxiliary braking system. The proposed strategy is verified in the co-simulation environment and field test respectively. Test results show optimal control effects in overall traffic situation levels and an enhanced energy recycling efficiency.
... Chen et al. presented a dynamic programming and neural network-based energy management strategy which was evaluated in simulation for known and unknown driving distance and duration conditions [24]. Xi et al. improved on this method and were also able to adjust the strategy for changes in driving distance during the drive [25]. ...
... The dynamic programming algorithm with distance constraint as described in the previous section was implemented on the research vehicle model. A simulation sweep for the UDDS and US06 cycles with varying target distances, d target = [0, 25,50,75,100,150,200,250] km, was conducted. For the UDDS cycle scenario as shown in Figures 17a-c, the algorithm was able to optimize for all values of d target . ...
Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution.
