Dongsuk Kum

Economic Analysis of the Dynamic Charging Electric Vehicle

A wireless charging or inductive charging electric vehicle (EV) is a type of EVs with a battery that is charged from a charging infrastructure, using a wireless power transfer technology. Wireless charging EVs are classified as stationary or dynamic charging EVs. Stationary charging EVs charge wirelessly when they are parked, and dynamic charging EVs can charge while they are in motion. The online electric vehicle developed at the Korea Advanced Institute of Science and Technology is an example of a commercially available dynamic charging transportation system. Numerous studies have reported that one of the benefits of dynamic charging is that it allows smaller and lighter batteries to be used, due to frequent charging using the charging infrastructure embedded under roads. In this paper, we quantitatively analyze the benefits of dynamic charging with an economic model of battery size and charging infrastructure allocation, using a mathematical optimization model. Particularly, we analyze by how much battery size can be reduced and what the cost saving of reducing the battery size is with the model. We also show that the dynamic charging can be beneficial to battery life.

Comprehensive Design Methodology of Input- and Output-Split Hybrid Electric Vehicles: In Search of Optimal Configuration

Despite high potentials of power-split hybrid electric vehicles (PS-HEV), their design and control problems are nontrivial. For instance, there exist 24 ways of connecting four components (two electric machines, an engine, and a vehicle wheel) with a planetary gear (PG), and more than thousand ways with two PGs. Furthermore, when PG and final drive ratios are considered design variables, finding an optimal design that fulfills both high fuel economy and short acceleration time is a challenge. In this paper, a systematic configuration searching methodology is proposed to find an optimal single PG PS-HEV configuration for both performance metrics. First, by identifying all the possible single PG configurations and reorganizing them into a compound lever design space, the performance metrics are explored in the continuous design space. Then, the designs are mapped onto the “fuel economy - acceleration performance” plane to solve the multiobjective configuration selection problem. Thus, a highly promising configuration (“o6”), which outperforms Prius design in the acceleration performance, is selected among Pareto Frontier. A case study has been conducted on a sport utility vehicle specification. The study illustrates that the performance metrics of candidate configurations change significantly, and thus, selecting a proper configuration is crucial to evoke full potential of the given powertrain components.

Synthesis of multiple model switching controllers using H∞ theory for systems with large uncertainties

Abstract The switching based control is an effective way to handle systems with large and fast varying uncertainties. Many conventional methods, however, do not use robust controllers to stabilize each model, and thus need a large number of controllers to ensure the stability of systems with large uncertainties. Under the structure of supervisory control, this paper presents a robust multiple model switching control (RMMSC) method using H ∞ control for MIMO linear systems with large parametric uncertainty. The proposed method uses H ∞ controller to maximize the stability region of each model with uncertainties, and thus to significantly reduce the number of candidate controllers. The candidate controllers are designed by using linear matrix inequality (LMI), and an exponentially decaying switch index is proposed to select the proper controller by estimating the system norm of each model uncertainty. Using such switch index, the closed-loop control system is then equivalent to a switching system with smaller uncertainty whose system norm can be predefined by designers. The robust stability under arbitrary switching conditions is proven by the small gain theorem; and for slow switching conditions, the acceptable dwell time for stability is analyzed and presented. The effectiveness of this method is demonstrated with the second order inertial system with large model uncertainties.

Extended Single Particle Model of Li-Ion Batteries Towards High Current Applications

Single particle models (SPM) are usually limited to low currents, which is a serious constrain for the usage of such models into vehicular battery management systems. The present study develops a physics-based ordinary differential equation (ODE) model, which is called extended single particle model (ESPM). In order to maintain the physical significance of the ODE model, a first-principle electrochemical partial differential equations (PDE) model is directly converted into an ODE model using volume-average method. The simulation results show that the ESPM model achieves an accuracy improvement of at least 14% when compared to the standard SPM for various levels of current inputs with only slight increase in computation time. The developed model paves the way for further improvements towards high-current, electrochemical ODE models with high physical significance and low computation burden.

Systematic Configuration Selection Methodology of Power-Split Hybrid Electric Vehicles With a Single Planetary Gear

Nowadays, power-split hybrid electric vehicles (PS-HEV) are very popular mainly thanks to the success of Toyota Prius. Despite their superior performance, the design and control of PS-HEVs are non-trivial due to the large number of design candidates and the complex control problems. For instance, there exist twelve ways to connect the four components (two motor/generators, an engine, and a driving wheel) with a single planetary gear-set (PG), and the number increases to 1152 possible configurations when using two PGs. Moreover, if we consider the final drive (FD) and PG ratios as design variables, finding the best design becomes intractable. In this study, we introduce a simple yet powerful way to find the optimal designs of single PG PS-HEVs. The suggested method consists of two parts — full-load analysis and light-load analysis. The full-load analysis computes 0–100kph times to evaluate acceleration performance of all designs using instantaneous optimization approach. The light-load analysis evaluates the fuel economy of selected designs (designs with acceptable acceleration performance) using equivalent consumption minimization strategy (ECMS). Note that the sun-to-ring (SR) gear ratio and the FD ratio are considered design variables, and thus one can see how fuel economy and acceleration performance of each configuration vary with SR and FD ratios. Based on these analyses, the optimal design that balances full-load and light-load performances can be selected.Copyright

Feasibility Assessment and Design Optimization of a Clutchless Multimode Parallel Hybrid Electric Powertrain

The fuel economy (FE) of the parallel hybrid electric vehicle (HEV) suffers due to significant energy losses from the wet clutches of the automatic transmission and limited engine operations. To overcome these limitations, a novel clutchless multimode parallel HEV (CMP-HEV) was proposed previously, but its operational feasibility was not proven. In this study, we propose a systematic methodology that can assess the feasibility of the CMP-HEV and efficiently optimize FE and acceleration performance of the design, simultaneously. Using the dynamic models, we assess the feasibility over the entire design spaces under two conditions: full acceleration (for drivability) and moderate acceleration (for fuel efficiency). An instantaneous optimization method is used to rapidly evaluate the full acceleration performance, and the designs with poor acceleration are considered infeasible. For the fuel efficiency, the feasibility is determined by the violation of electric motor constraints under the assumption of efficient engine operations. The feasible design sets from the assessment are used for the design optimization. Dynamic programming is applied over only the feasible design space, and the Pareto front is obtained to show the tradeoff between FE and acceleration performance. The selected final design is found superior to a comparable parallel HEV in both FE and acceleration performance perspectives.

Threat prediction algorithm based on local path candidates and surrounding vehicle trajectory predictions for automated driving vehicles

Among others, a reliable threat prediction algorithm is one of the key enabling technologies for the commercialization of the automated driving systems and other driver assistance systems. Previous algorithms that use Time-to-Collision (TTC) as a measure of threat tend to assume constant state and constant input; e.g. constant yaw rate and constant acceleration. Although the predictability of these algorithms is acceptable within a one second time horizon, it becomes invalid for predictions over one second because yaw rate and acceleration are highly unlikely to be constant. Therefore, in this paper, we propose a threat prediction algorithm that can accurately predict TTC over a longer time horizon based on future trajectory predictions of a surrounding vehicle. First, a comprehensive set of local path candidates is generated along the curvilinear coordinates using a quintic (5th order) polynomial with respect to the arc-length corresponding to the different lateral offsets. Trajectory prediction of a surrounding vehicle is accomplished by introducing target lane detection, which is estimated according to the amount of difference between the current motion and the centerline of the driving lane. Based on these future vehicle trajectories, TTC is computed by comparing the entrance and exit time of two vehicles into and out of the conflict area where the occupied spaces of two vehicles overlap. Finally, in order to provide threat assessment results, the inverse TTC values obtained above are plotted on a 2-dimensional trajectory plane where each set of the tangential acceleration and the initial yaw acceleration values represents each local path candidate. Thus, these threat assessment results can be directly utilized to determine a driving strategy of autonomous vehicles.

Efficient and accurate computation of model predictive control using pseudospectral discretization

The model predictive control (MPC) is implemented by repeatedly solving an open loop optimal control problem (OCP). For the real-time implementation, the OCP is often discretized with evenly spaced time grids. This evenly spaced discretization, however, is accurate only if sufficiently small sampling time is used, which leads to heavy computational load. This paper presents a method to efficiently and accurately compute the continuous-time MPC problem based on the pseudospectral discretization, which utilizes unevenly spaced collocation points. The predictive horizon is virtually doubled by augmenting a mirrored horizon such that denser collocation points can be used towards the current time step, and sparser points can be used towards the end time of predictive horizon. Then, both state and control variables are approximated by Lagrange polynomials at only a half of LGL (Legendre-Gauss-Lobatto) collocation points. This implies that high accuracy can be achieved with a much less number of collocation points, which results in much reduced computational load. Examples are used to demonstrate its advantages over the evenly spaced discretization.

The impact of inhomogeneous particle size distribution on Li-ion cell performance under galvanostatic and transient loads

Although small particle size and homogeneous particle size distribution are well-known for desired features, the electrode of Li-ion cells in reality consists of inhomogeneous particle sizes. Since large particle size can deteriorate the cell performance, it is essential to understand the electro- and physicochemical processes within the inhomogeneous electrode including large particles. In this paper, the effect of large particles within inhomogeneous electrode on the cell performance is investigated by using extended P2D model. On the basis of three particle sizes, inhomogeneous electrode is represented. The volume fraction distribution of these three particle sizes is varied creating four different electrode compositions. For a fair comparison, the sum of these volume fractions is fixed guaranteeing the same amount of active material within the electrode. A comparative study of four electrode compositions is carried out in order to investigate the impact of large particle portion on the power and energy performance under galvanostatic and transient loads. Simulation results show that higher portion of large particle size leads to unequal Li-ion distribution along the electrode leading to losses in useable power and energy.

Design Optimization of the OLEV System Considering Battery Lifetime

We present recent developments in the wireless powered On-Line Electric Vehicle (OLEV) developed by the Korea Advanced Institute of Technology (KAIST). Using advanced wireless power transfer technology, a battery in the vehicle can be charged remotely from the wireless power transmitters embedded in the road. However, the battery lifetime varies depending on the charge and discharge cycles, which are decided by installation of the wireless power transmitters. In this paper, we briefly introduce the system design issues of the OLEV and identify relation between the OLEV system design and the batterys lifetime. We then present the battery state model and the battery lifetime prediction model under given set of wireless power transmitters. In proposed battery lifetime prediction model, we apply a fatigue aging approach of the battery. The optimization problem is set to find economical allocation of the wireless power transmitters. The particle swarm optimization (PSO) algorithm is implemented as the solution approach for the optimization problem. The KAIST campus shuttle system is used as numerical case result and comparison with previous approach of the OLEV system design is presented.