The issues of global warming and depletion of fossil fuels have paved opportunities to electric vehicle (EV). Moreover, the rapid development of power electronics technologies has even realized high energy-efficient vehicles. EV could be the alternative to decrease the global green house gases emission as the energy consumption in the world transportation is high. However, EV faces huge challenges in battery cost since one-third of the EV cost lies on battery. This paper reviews state-of-the-art of the energy sources, storage devices, power converters, low-level control energy management strategies and high supervisor control algorithms used in EV. The comparison on advantages and disadvantages of vehicle technology is highlighted. In addition, the standards and patterns of drive cycles for EV are also outlined. The advancement of power electronics and power processors has enabled sophisticated controls (low-level and high supervisory algorithms) to be implemented in EV to achieve optimum performance as well as the realization of fast-charging stations. The rapid growth of EV has led to the integration of alternative resources to the utility grid and hence smart grid control plays an important role in managing the demand. The awareness of environmental issue and fuel crisis has brought up the sales of EV worldwide.

A review of energy sources and energy management system in electric vehicles

In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

Predictive energy management of a power-split hybrid electric vehicle

Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Control of hybrid electric vehicles

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

A number of strategies for the power management of hybrid electric vehicles (HEVs) are proposed in the literature. A key challenge is to achieve near-optimality while keeping the methodology simple. The Pontryagin's minimum principle (PMP) is suggested as a viable real-time strategy. In this brief, the global optimality of the principle under reasonable assumptions is described from a mathematical viewpoint. Instantaneous optimal control with an appropriate equivalent parameter for battery usage is shown to be possibly a global optimal solution under the assumption that the internal resistance and open-circuit voltage of a battery are independent of the state-of-charge (SOC). This brief also demonstrates that the optimality of the equivalent consumption minimization strategy (ECMS) results from the close relation of ECMS to the optimal-control-theoretic concept of PMP. In static simulation for a power-split hybrid vehicle, the fuel economy of the vehicle using the control algorithm proposed in this brief is found to be very close-typically within 1%-to the fuel economy through global optimal control that is based on dynamic programming (DP).

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Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle

Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over a given driving schedule. Big obstacles to the control design are the model complexity and the necessity of "a priori" knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle that do not require a priori knowledge of the driving cycle. The considered approaches are: a rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the Hinfin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

Energy-management strategy plays a critical role in high fuel economy that modern hybrid electric vehicles can achieve, yet a lack of information about future driving conditions is one of the limitations of fulfilling the maximum fuel economy potential of hybrid vehicles. Today, with wider deployment of vehicle telematic technologies, prediction of future driving conditions, e.g., road grade, is becoming more realistic. This paper evaluates the potential gain in fuel economy if road grade information is integrated into the energy management of hybrid vehicles. Real-world road geometry information is utilized in power-management decisions by using both dynamic programming (DP) and a standard equivalent consumption minimization strategy (ECMS). At the same time, two baseline control strategies with no future information are developed and validated for comparison purposes. Simulation results show that road terrain preview enables fuel savings. The level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.

Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles

Advanced connectivity features in today’s smart vehicles are giving rise to several promising intelligent transportation technologies. Connected vehicle system is one among such technologies, where a set of vehicles can communicate with each other and the infrastructure via communication networks. Connected vehicles have the potential to improve the traffic throughput, minimize the risk of accidents and reduce vehicle energy consumption. Despite these promising features, connected vehicles suffer from the safety and security issues. Especially, vehicle-to-vehicle and vehicle-to-infrastructure communication make the connected vehicles vulnerable to cyber attacks. In order to improve safety and security, advanced vehicular control systems must be designed to be resilient to such cyber attacks. The first step of designing such attack-resilient control system is detection of the occurrence of the cyber attack. In this paper, we address this need and propose a real-time scheme that can potentially 1) detect the occurrence of a particular cyber attack, namely denial of service; and 2) estimate the effect of the attack on the connected vehicle system. The scheme consists of a set of observers, which are designed using sliding mode and adaptive estimation theory. The mathematical convergence properties of the observers are analyzed via Lyapunov’s stability theory. Finally, simulation demonstrates the performance of the approach and the robustness of the scheme under several forms of uncertainties.

Real-Time Detection and Estimation of Denial of Service Attack in Connected Vehicle Systems

In this paper, we develop a fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions. The proposed control strategy is decentralized in nature, as every vehicle evaluates its own strategy using only neighborhood information. Along with the vehicle-to-vehicle communication, we exploit the signal phase and timing information from traffic lights to develop computationally efficient MPC-based strategies that reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance and computational advantages of our proposed method.

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunov’s direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.

https://ecal.berkeley.edu/satadru/papers/Dey_Battery_SOC_Estimation.pdf

Pierluigi Pisu

Papers

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles

Real-Time Detection and Estimation of Denial of Service Attack in Connected Vehicle Systems

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model