Lars Johannesson

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed. The predictive control was based on information supplied by the vehicle navigation system. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The results indicate that, for an urban route with varying topography, the use of predictive control can significantly reduce the fuel consumption.

Comparison of Three Electrochemical Energy Buffers Applied to a Hybrid Bus Powertrain With Simultaneous Optimal Sizing and Energy Management

This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., a Li-ion battery pack, a supercapacitor pack, and a dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs, in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicles with a heuristic/nonoptimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in the most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in HEV (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the onboard energy carriers affect the results and conclusions.

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route

Optimal Dimensioning and Power Management of a Fuel Cell/Battery Hybrid Bus via Convex Programming

This paper is concerned with the simultaneous optimal component sizing and power management of a fuel cell/battery hybrid bus. Existing studies solve the combined plant/controller optimization problem for fuel cell hybrid vehicles (FCHVs) by using methods with disadvantages of heavy computational burden and/or suboptimality, for which only a single driving profile was often considered. This paper adds three important contributions to the FCHVs-related literature. First, convex programming is extended to rapidly and efficiently optimize both the power management strategy and sizes of the fuel cell system (FCS) and the battery pack in the hybrid bus. The main purpose is to encourage more researchers and engineers in FCHVs field to utilize the new effective tool. Second, the influence of the driving pattern on the optimization result (both the component sizes and hydrogen economy) of the bus is systematically investigated by considering three different bus driving routes, including two standard testing cycles and a realistic bus line cycle with slope information in Gothenburg, Sweden. Finally, the sensitivity of the optimization outcome to the potential price decreases of the FCS and the battery is quantitatively examined.

Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains

The extra degree of freedom offered in hybrid electric vehicles have inspired many researchers to formulate and solve optimal control problems of various kinds. This paper presents an Approximate Dynamic Programming scheme that efficiently solves the optimal power split between the internal combustion engine and the electric machine in parallel hybrid powertrains. Gear switches and switches between hybrid and pure electric mode are formally treated. The scheme combines two ideas to reduce the computational time of the iterations performed in the dynamic programming. First, the value function is approximated using piecewise linear functions on a sparse grid. Secondly, by using model approximation the iterations performed in the dynamic programming are reduced to solving scalar quadratic problems. In the simulations the approximation scheme is able to find a good approximation of the optimal control trajectory.

Engine On/Off Control for Dimensioning Hybrid Electric Powertrains via Convex Optimization

This paper presents a novel heuristic method for optimal control of mixed-integer problems that, for given feasible values of the integer variables, are convex in the rest of the variables. The method is based on Pontryagins maximum principle and allows the problem to be solved using convex optimization techniques. The advantage of this approach is the short computation time for obtaining a solution near the global optimum, which may otherwise need very long computation time when solved by algorithms guaranteeing global optimum, such as dynamic programming (DP). In this paper, the method is applied to the problem of battery dimensioning and power split control of a plug-in hybrid electric vehicle (PHEV), where the only integer variable is the engine on/off control, but the method can be extended to problems with more integer variables. The studied vehicle is a city bus, which is driven along a perfectly known bus line with a fixed charging infrastructure. The bus can charge either at standstill or while driving along a tramline (slide in). The problem is approached in two different scenarios: First, only the optimal power split control is obtained for several fixed battery sizes; and second, both battery size and power split control are optimized simultaneously. Optimizations are performed over four different bus lines and two different battery types, giving solutions that are very close to the global optimum obtained by DP.

A Novel Algorithm for Predictive Control of Parallel Hybrid Powertrains based on Dynamic Programming

Abstract A novel algorithm for predictive control of parallel hybrid vehicle powertrains is presented. The algorithm uses information from GPS and digital maps to schedule the use of the energy buffer along the planned route. The algorithm is based on dynamic programming and achieves close to the theoretical minimal consumption when simulated on measured drive data. For simulated routes with a topographic profile that contains large hills the fuel consumption savings compared to a competitive non predictive controller are 6%. For simulated routes with a more moderate topographic profile the savings are between 2-3% and for routes with completely flat topographic profile the savings are only between 0.5-2%.

Convex modeling of energy buffers in power control applications

This paper describes modeling steps for presenting energy buffers as convex models in power control applications. Except obtaining the optimal control, the paper also shows how convex optimization can be used to simultaneously size the energy buffer while optimally controlling a trajectory following system. The energy buffers are capacitors and batteries with quadratic power losses, while the resulting convex problem is a semidefinite program. The convex modeling steps are described through a problem of optimal buffer sizing and control of a hybrid electric vehicle. The studied vehicle is a city bus driven along a perfectly known bus line. The paper also shows modeling steps for alternative convex models where power losses and power limits of the energy buffer are approximated. The approximated models show significant decrease in computation time without visible impact on the optimal result.

Analytic Solutions to the Dynamic Programming Subproblem in Hybrid Vehicle Energy Management

The computationally demanding dynamic programming (DP) algorithm is frequently used in academic research to solve the energy management problem of a hybrid electric vehicle (HEV). This paper is exclusively focused on how the computational demand of such a computation can be reduced. The main idea is to use a local approximation of the gridded cost-to-go and derive an analytic solution for the optimal torque split decision at each point in the time and state grid. Thereby, it is not necessary to quantize the torque split and identify the optimal decision by interpolating in the cost-to-go. Two different approximations of the cost-to-go are considered in this paper: 1) a local linear approximation and 2) a quadratic spline approximation. The results indicate that computation time can be reduced by orders of magnitude with only a slight degradation in simulated fuel economy. Furthermore, with a spline approximated cost-to-go, it is also possible to significantly reduce the memory storage requirements. A parallel plug-in HEV is considered in this paper, but the method is also applicable to an HEV.

Including a Battery State of Health model in the HEV component sizing and optimal control problem

This paper studies convex optimization and modelling for component sizing and optimal energy management control of hybrid electric vehicles. The novelty in the paper is the modeling steps required to include a battery wear model into the convex optimization problem. The convex modeling steps are described for the example of battery sizing and simultaneous optimal control of a series hybrid electric bus driving along a perfectly known bus line. Using the proposed convex optimization method and battery wear model, the city bus example is used to study a relevant question: is it better to choose one large battery that is sized to survive the entire lifespan of the bus, or is it beneficial with several smaller replaceable batteries which could be operated at higher c-rates?