Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

This paper proposes a new fast charging strategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost

Hybrid energy storage systems via power electronic converters

In recent years, many research lines have focused their efforts on improving energy efficiency and developing renewable energy sources. In this context, the use of energy storage systems is on the rise, as they can contribute to the integration of renewables to the main electrical grid. However, energy storage systems are divided into high energy or high power devices. Due to the lack of a solution covering both aspects, researchers are forced to find alternatives. The hybridization of different energy storage technologies is presented as a suitable solution for this problem, since it combines high power and high energy within the same system. The main goal of this thesis is the design and implementation of a hybrid energy storage system (HESS), capable of improving the performance provided by a single storage technology. As a first step in this direction, this document reviews and classifies the most relevant HESS topologies found in the literature. This allows a better understanding of the drawbacks and benefits of each configuration. To ensure the optimal use of this HESS, it is essential to design a suitable energy management strategy and a proper power electronic converter control. To this end, the control structure has been analyzed from different approaches. On the one hand there would be the classic multilevel control structure, which usually consists of three levels among which are the operating constraints, the power sharing and at the lowest level the control of the converter. On the other hand there would be the single level control structure in which both, the power distribution and the control of the converter, are integrated within the same level by using modern MPC control algorithms. Finally, three different case studies are presented to show the practical application of the developed control strategies together with the main conclusions of the thesis.Azken urteetan, ikerketa-lerro askok eraginkortasun energetikoa hobetzeko eta energia berriztagarriak garatzeko ahaleginak egin dituzte. Testuinguru honetan, energia metatze sistemen erabilera geroz eta handiagoa da, berriztagarrien integrazioa sare elektrikoarekin erraztu dezaketelako. Hala ere, energia altuko edo potentzia altuko metatze sistemak bakarrik aukeratu daitezke. Horregatik, ikertzaileek alternatiba berriak bilatzera behartuta daude. Energia metatze sistema desberdinen hibridazioa, arazo horri irtenbidea ematen dio. Honekin, potentzia eta energia maila altuak sistema bakar batetan batu daitezke. Tesi honen helburu nagusia, energia metatze sistema hibrido (HESS sigla, ingelesetik Hybrid Energy Storage System) bat diseinatzea eta inplementatzea da. Sistema honek, teknologia bakar batek eskaintzen duen errendimendua hobetzeko gai izan beharko luke. Lehen urratsa bezala, dokumentu honek literaturan aurkitutako topologia hibrido garrantzitsuenak laburbildu eta batzen ditu. Honi esker, konfigurazio bakoitzaren abantaila eta desabantailak hobeto ulertzea ahal da. HESS honen erabilera optimoa bermatzeko, ezinbestekoa da energia kudeatzeko estrategia on bat diseinatzea bihurgailu elektronikoaren kontrol egokiarekin batera. Horretarako, kontrol egitura ikuspegi desberdinetatik aztertuko da. Alde batetik, maila anitzeko kontrol egitura klasikoa egongo litzateke, normalean hiru mailaz osatua dagoena. Goi mailan funtzionamendu eta segurtasun mugak egongo lirateke, erdiko mailan potentzia banaketa, eta azkenik bihurgailuaren maila baxuko kontrola. Bestalde, maila bakarreko kontrol egitura egongo litzateke non mugak, potentzia banaketa eta bihurgailuaren kontrola maila berean integratzen dira kontrol iragarleko algoritmoen bidez (MPC). Azkenik, hiru kasu desberdin aurkezten dira garatutako kontrolen aplikazio praktikoa erakusteko tesiaren ondorio nagusiekin batera.En los últimos años, numerosas líneas de investigación han centrado sus esfuerzos en mejorar la eficiencia energética junto con el desarrollo de fuentes de generación renovables. En este contexto, el uso de sistemas de almacenamiento de energía está al alza, ya que estos pueden contribuir a la integración de las renovables en la red eléctrica convencional. Sin embargo, la necesidad de elegir entre dispositivos de alta energía o alta potencia, obliga a los investigadores a buscar otras alternativas. La hibridación de diferentes sistemas de almacenamiento se presenta como una solución apropiada para este problema, ya que combina alta energía y alta potencia dentro de un mismo sistema. El objetivo principal de esta tesis es el diseño e implementación de un sistema híbrido de almacenamiento de energía (sigla HESS, del inglés Hybrid Energy Storage System), capaz de mejorar las prestaciones que proporcionaría el uso de una única tecnología. Como primer paso en esta dirección, en este documento resume y clasifica las topologías de hibridación más relevantes encontradas en la literatura. Esto permite una mejor comprensión de los beneficios e inconvenientes de cada configuración. Para garantizar el uso óptimo de dicho HESS, es esencial diseñar una estrategia adecuada de gestión de energía junto con un control óptimo del convertidor electrónico de potencia. Para lograr este fin, la estructura de control ha sido analizada desde diferentes enfoques. Por un lado se encontraría la estructura de control multinivel clásica, la cual generalmente consta de tres niveles. En el nivel más alto se encontrarían las restricciones operativas y de seguridad, en el intermedio se encontraría la división de potencia, y por último el control de nivel bajo del convertidor. Por otro lado, se encontraría la estructura de control de un único nivel, en la que tanto las restricciones, el reparto de potencia y el control del convertidor se integran dentro del mismo nivel mediante algoritmos de control predictivo (MPC). Finalmente, se presentan tres casos de estudio para mostrar la aplicación práctica de las estrategias de control desarrolladas junto con las principales conclusiones de la tesis

Model Predictive Control Strategies for Advanced Battery Management Systems

Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications where Lithium-ion (Li-ion) batteries are employed. From a manufacturer point of view, the optimal design and management of such electrochemical accumulators are important aspects for ensuring safe and profitable operations. The adoption of mathematical models can support the achievement of the best performance, while saving time and money. In the literature, all the models used to describe the behavior of a Li-ion battery belong to one of the two following families: (i) Equivalent Circuit Models (ECMs), and (ii) Electrochemical Models (EMs). While the former family represents the battery dynamics by means of electrical circuits, the latter resorts to first principles laws of modeling. As a first contribution, this Thesis provides a thorough investigation of the pseudo-two-dimensional (P2D) Li-ion battery EM. In particular, the objectives are to provide: (i) a detailed description of the model formulation, (ii) the Li-ION SIMulation BAttery (LIONSIMBA) toolbox as a finite volume Matlab implementation of the P2D model, for design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed tool with respect to the COMSOL MultiPhysics commercial software and the Newman's DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, and a battery pack of series connected cells. The second contribution is related to the development of several charging strategies for Advanced Battery Management Systems (ABMSs), where predictive approaches are employed to attain optimal control. Model Predictive Control (MPC) refers to a particular family of control algorithms that, according to a mathematical model, predicts the future behavior of a plant, while considering inputs and outputs constraints. According to this paradigm, in this Thesis different ABMSs strategies have been developed, and their effectiveness shown through simulations. Due to the complexity of the P2D model, its inclusion within an MPC context could prevent the online application of the control algorithm. For this reason, different approximations of the P2D dynamics are proposed and their MPC formulations carefully explained. In particular, finite step response, autoregressive exogenous, piecewise affine, and linear time varying approximations are presented. For all the aforementioned reformulations, the closed-loop performance are evaluated considering the P2D implementation of LIONSIMBA as the real plant. The closed-loop simulations highlight the suitability of the MPC paradigm to be employed for the development of the future ABMSs. In fact, its ability to predict the future behavior of the cell while considering operating constraints can help in preventing possible safety issues and improving the charging performance. Finally, the reliability and efficiency of the proposed Matlab toolbox in simulating the P2D dynamics, support the idea that LIONSIMBA can significantly contribute in the advance of the battery field.Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications where Lithium-ion (Li-ion) batteries are employed. From a manufacturer point of view, the optimal design and management of such electrochemical accumulators are important aspects for ensuring safe and profitable operations. The adoption of mathematical models can support the achievement of the best performance, while saving time and money. In the literature, all the models used to describe the behavior of a Li-ion battery belong to one of the two following families: (i) Equivalent Circuit Models (ECMs), and (ii) Electrochemical Models (EMs). While the former family represents the battery dynamics by means of electrical circuits, the latter resorts to first principles laws of modeling. As a first contribution, this Thesis provides a thorough investigation of the pseudo-two-dimensional (P2D) Li-ion battery EM. In particular, the objectives are to provide: (i) a detailed description of the model formulation, (ii) the Li-ION SIMulation BAttery (LIONSIMBA) toolbox as a finite volume Matlab implementation of the P2D model, for design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed tool with respect to the COMSOL MultiPhysics commercial software and the Newman's DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, and a battery pack of series connected cells. The second contribution is related to the development of several charging strategies for Advanced Battery Management Systems (ABMSs), where predictive approaches are employed to attain optimal control. Model Predictive Control (MPC) refers to a particular family of control algorithms that, according to a mathematical model, predicts the future behavior of a plant, while considering inputs and outputs constraints. According to this paradigm, in this Thesis different ABMSs strategies have been developed, and their effectiveness shown through simulations. Due to the complexity of the P2D model, its inclusion within an MPC context could prevent the online application of the control algorithm. For this reason, different approximations of the P2D dynamics are proposed and their MPC formulations carefully explained. In particular, finite step response, autoregressive exogenous, piecewise affine, and linear time varying approximations are presented. For all the aforementioned reformulations, the closed-loop performance are evaluated considering the P2D implementation of LIONSIMBA as the real plant. The closed-loop simulations highlight the suitability of the MPC paradigm to be employed for the development of the future ABMSs. In fact, its ability to predict the future behavior of the cell while considering operating constraints can help in preventing possible safety issues and improving the charging performance. Finally, the reliability and efficiency of the proposed Matlab toolbox in simulating the P2D dynamics, support the idea that LIONSIMBA can significantly contribute in the advance of the battery field

Development of optimal energy management and sizing strategies for large-scale electrical storage systems supporting renewable energy sources.

284 p.El desarrollo e integración de las fuentes de energía renovable (RES) conducirá a un futuro energético más sostenible. Las plantas renovables deberán mejorar su participación y operación a través de los mercados de electricidad de una manera más controlada y segura. Además, el diseño actual del mercado está cambiando para permitir una participación inclusiva en mercados de flexibilidad. En este contexto, los sistemas de almacenamiento de energía (ESS) se consideran una de las tecnologías flexibles clave que pueden apoyar la operación de las energías renovables, mediante servicios como: 1) control de la potencia generada, 2) mejora de los errores de predicción, y 3) provisión de servicios auxiliares de regulación de frecuencia. Sin embargo, el desarrollo del almacenamiento ha sido frenado también por sus altos costos. Por lo tanto, esta tesis doctoral aborda el tema del ¿Desarrollo de estrategias óptimas de gestión y dimensionamiento de los sistemas de almacenamiento eléctrico a gran escala como apoyo a fuentes de energía renovable¿, con el objetivo de desarrollar una metodología con una perspectiva global, mediante una estrategia de gestión de energía avanzada (EMS) que aborda la gestión de activos (RES + ESS) a largo plazo y por otro lado, el cálculo del dimensionamiento y operación del almacenamiento a corto plazo (en la operación en tiempo real), para asegurar un marco adecuado que permita evaluar la rentabilidad de la integración del almacenamiento en aplicaciones conectadas a la red. La estrategia de gestión de energía propuesta es validada a través de dos casos de estudio: una planta renovable individual (eólica o solar) con almacenamiento, y un porfolio de renovables y almacenamiento

Battery Storage System Optimal Exploitation Through Physics-Based Model Predictive Control

Traditionally, the safe operation of a battery energy storage system (BESS) is achieved by imposing conservative constraints on its DC bus current and voltage. By using a computationally efficient single particle model (SPM), we propose to replace these constraints with the battery internal ion concentrations and electrical potentials in order to avoid these quantities to exceed hazardous limits. Indeed, the in-depth knowledge of the BESS internal states provided by the SPM, enhances the awareness of a control action and allows for a better exploitation of the BESS energy and power capabilities, while maintaining safe operational conditions. The target application is composed by a model predictive control (MPC) applied to a MW-class grid-connected BESS responsible to dispatch the operation of a medium voltage (20 kV) feeder interfacing heterogeneous loads and distributed generation. The performance of the proposed MPC are assessed and compared with respect to a traditional approach constraining the BESS DC bus current and voltage

A Study of Computationally Efficient Advanced Battery Management: Modeling, Identification, Estimation and Control

Lithium-ion batteries (LiBs) are a revolutionary technology for energy storage. They have become a dominant power source for consumer electronics and are rapidly penetrating into the sectors of electrified transportation and renewable energies, due to the high energy/power density, long cycle life and low memory effect. With continuously falling prices, they will become more popular in foreseeable future. LiBs demonstrate complex dynamic behaviors and are vulnerable to a number of operating problems including overcharging, overdischarging and thermal runaway. Hence, battery management systems (BMSs) are needed in practice to extract full potential from them and ensure their operational safety. Recent years have witnessed a growing amount of research on BMSs, which usually involves topics such as dynamic modeling, parameter identification, state estimation, cell balancing, optimal charging, thermal management, and fault detection. A common challenge for them is computational efficiency since BMSs typically run on embedded systems with limited computing and memory capabilities. Inspired by the challenge, this dissertation aims to address a series of problems towards advancing BMSs with low computational complexity but still high performance. Specifically, the efforts will focus on novel battery modeling and parameter identification (Chapters 2 and 3), highly efficient optimal charging control (Chapter 4) and spatio-temporal temperature estimation of LiB packs (Chapter 5). The developed new LiB models and algorithms can hopefully find use in future LiB systems to improve their performance, while offering insights into some key challenges in the field of BMSs. The research will also entail the development of some fundamental technical approaches concerning parameter identification, model predictive control and state estimation, which have a prospect of being applied to dynamic systems in various other problem domains

Control and management of energy storage systems in microgrids

The rate of integration of the renewable energy sources in modern grids have significantly increased in the last decade. These intermittent, non-dispatchable renewable sources, though environment friendly tend to be grid unfriendly. This is precisely due to the issues pertaining to grid congestion, voltage regulation and stability of grids being reported as a result of the incorporation of renewable sources. In this scenario, the use of energy storage systems (ESS ) in electric grids is being widely proposed to overcome these issues. However, integrating energy storage systems alone will not compensate for the issue created by renewable generation. The control and management of the ESS should be done optimally so that their full capabilities are exploited to overcome the issues in the power grids and to ensure their lower cost of investment by prolonging ESS lifetime through minimising degradation. Motivated by this aspect this Ph.D work focusses on developing an efficient, optimal control and management strategy for ESS in a microgrid, especially hybrid ESS. The Ph.D work addresses this issue by proposing a hierarchical control scheme comprising of a lower power management and higher energy management stage with contributions in each stage. In the power management stage this work focusses on improving aspects of real time control of power converters interfacing ESS to grid and the microgrid system as whole. The work proposes control systems with improved dynamic behaviour for power converters based on the reset control framework. In the microgrid control the work presents a primary+secondary control scheme with improved voltage regulation performance under disturbances, using an observer. The real time power splitting strategies among hybrid ESS accounting for the ESS operating efficiencies and degradation mechanisms will also be addressed in the primary+secondary control of power management stage. The design criteria, stability and robustness analysis will be carried out, along with simulation or experimental verifications. In the higher level energy management stage, the contribution of this work involves application of an economic MPC framework for the management of ESS in microgrids. The work specifically addresses the problems of mitigating grid congestion from renewable power feed-in, minimising ESS degradation and maximising self consumption of generated renewable energy using the MPC based energy management system. A survey of the forecasting methods that can be used for MPC will be carried out and a neural network based forecasting unit for time series prediction will be developed. The practical issue of accounting for forecasting error in the decision making of MPC will be addressed and impact of the resulting conservative decision making on the system performance will be analysed. The improvement in performance with the proposed energy management scheme will be demonstrated and quantified.La integración de las fuentes de energía renovables en las redes modernas ha aumentado significativamente en la última década. Estas fuentes renovables, aunque muy convenientes para el medio ambiente son de naturaleza intermitente, y son no panificables, cosa que genera problemas en la red de distribución. Esto se debe precisamente a los problemas relacionados con la congestión de la red y la regulación del voltaje. En este escenario, el uso de sistemas de almacenamiento de energía (ESS) en redes eléctricas está siendo ampliamente propuesto para superar estos problemas. Sin embargo, la integración de sistemas de almacenamiento de energía por sí solos no compensará el problema creado por la generación renovable. El control y la gestión del ESS deben realizarse de manera óptima, de modo que se aprovechen al máximo sus capacidades para superar los problemas en las redes eléctricas, garantizar un coste de inversión razonable y prolongar la vida útil del ESS minimizando su degradación. Motivado por esta problemática, esta tesis doctoral se centra en desarrollar una estrategia de control y gestión eficiente para los ESS integrados en una microrred, especialmente cuando se trata de ESS de naturaleza. El trabajo de doctorado propone un esquema de control jerárquico compuesto por un control de bajo nivel y una parte de gestión de energía operando a más alto nivel. El trabajo realiza aportaciones en los dos campos. En el control de bajo nivel, este trabajo se centra en mejorar aspectos del control en tiempo real de los convertidores que interconectan el ESS con la red y el sistema de micro red en su conjunto. El trabajo propone sistemas de control con comportamiento dinámico mejorado para convertidores de potencia desarrollados en el marco del control de tipo reset. En el control de microrred, el trabajo presenta un esquema de control primario y uno secundario con un rendimiento de regulación de voltaje mejorado bajo perturbaciones, utilizando un observador. Además, el trabajo plantea estrategias de reparto del flujo de potencia entre los diferentes ESS. Durante el diseño de estos algoritmos de control se tienen en cuenta los mecanismos de degradación de los diferentes ESS. Los algoritmos diseñados se validarán mediante simulaciones y trabajos experimentales. En el apartado de gestión de energía, la contribución de este trabajo se centra en la aplicación del un control predictivo económico basado en modelo (EMPC) para la gestión de ESS en microrredes. El trabajo aborda específicamente los problemas de mitigar la congestión de la red a partir de la alimentación de energía renovable, minimizando la degradación de ESS y maximizando el autoconsumo de energía renovable generada. Se ha realizado una revisión de los métodos de predicción del consumo/generación que pueden usarse en el marco del EMPC y se ha desarrollado un mecanismo de predicción basado en el uso de las redes neuronales. Se ha abordado el análisis del efecto del error de predicción sobre el EMPC y el impacto que la toma de decisiones conservadoras produce en el rendimiento del sistema. La mejora en el rendimiento del esquema de gestión energética propuesto se ha cuantificado.La integració de les fonts d'energia renovables a les xarxes modernes ha augmentat significativament en l’última dècada. Aquestes fonts renovables, encara que molt convenients per al medi ambient són de naturalesa intermitent, i són no panificables, cosa que genera problemes a la xarxa de distribució. Això es deu precisament als problemes relacionats amb la congestió de la xarxa i la regulació de la tensió. En aquest escenari, l’ús de sistemes d'emmagatzematge d'energia (ESS) en xarxes elèctriques està sent àmpliament proposat per superar aquests problemes. No obstant això, la integració de sistemes d'emmagatzematge d'energia per si sols no compensarà el problema creat per la generació renovable. El control i la gestió de l'ESS s'han de fer de manera _optima, de manera que s'aprofitin al màxim les seves capacitats per superar els problemes en les xarxes elèctriques, garantir un cost d’inversió raonable i allargar la vida útil de l'ESS minimitzant la seva degradació. Motivat per aquesta problemàtica, aquesta tesi doctoral es centra a desenvolupar una estratègia de control i gestió eficient per als ESS integrats en una microxarxa, especialment quan es tracta d'ESS de natura híbrida. El treball de doctorat proposa un esquema de control jeràrquic compost per un control de baix nivell i una part de gestió d'energia operant a més alt nivell. El treball realitza aportacions en els dos camps. En el control de baix nivell, aquest treball es centra a millorar aspectes del control en temps real dels convertidors que interconnecten el ESS amb la xarxa i el sistema de microxarxa en el seu conjunt. El treball proposa sistemes de control amb comportament dinàmic millorat per a convertidors de potència desenvolupats en el marc del control de tipus reset. En el control de micro-xarxa, el treball presenta un esquema de control primari i un de secundari de regulació de voltatge millorat sota pertorbacions, utilitzant un observador. A més, el treball planteja estratègies de repartiment de el flux de potència entre els diferents ESS. Durant el disseny d'aquests algoritmes de control es tenen en compte els mecanismes de degradació dels diferents ESS. Els algoritmes dissenyats es validaran mitjanant simulacions i treballs experimentals. En l'apartat de gestió d'energia, la contribució d'aquest treball se centra en l’aplicació de l'un control predictiu econòmic basat en model (EMPC) per a la gestió d'ESS en microxarxes. El treball aborda específicament els problemes de mitigar la congestió de la xarxa a partir de l’alimentació d'energia renovable, minimitzant la degradació d'ESS i maximitzant l'autoconsum d'energia renovable generada. S'ha realitzat una revisió dels mètodes de predicció del consum/generació que poden usar-se en el marc de l'EMPC i s'ha desenvolupat un mecanisme de predicció basat en l’ús de les xarxes neuronals. S'ha abordat l’anàlisi de l'efecte de l'error de predicció sobre el EMPC i l'impacte que la presa de decisions conservadores produeix en el rendiment de el sistema. La millora en el rendiment de l'esquema de gestió energètica proposat s'ha quantificat

Development of optimal energy management and sizing strategies for large-scale electrical storage systems supporting renewable energy sources.

284 p.El desarrollo e integración de las fuentes de energía renovable (RES) conducirá a un futuro energético más sostenible. Las plantas renovables deberán mejorar su participación y operación a través de los mercados de electricidad de una manera más controlada y segura. Además, el diseño actual del mercado está cambiando para permitir una participación inclusiva en mercados de flexibilidad. En este contexto, los sistemas de almacenamiento de energía (ESS) se consideran una de las tecnologías flexibles clave que pueden apoyar la operación de las energías renovables, mediante servicios como: 1) control de la potencia generada, 2) mejora de los errores de predicción, y 3) provisión de servicios auxiliares de regulación de frecuencia. Sin embargo, el desarrollo del almacenamiento ha sido frenado también por sus altos costos. Por lo tanto, esta tesis doctoral aborda el tema del ¿Desarrollo de estrategias óptimas de gestión y dimensionamiento de los sistemas de almacenamiento eléctrico a gran escala como apoyo a fuentes de energía renovable¿, con el objetivo de desarrollar una metodología con una perspectiva global, mediante una estrategia de gestión de energía avanzada (EMS) que aborda la gestión de activos (RES + ESS) a largo plazo y por otro lado, el cálculo del dimensionamiento y operación del almacenamiento a corto plazo (en la operación en tiempo real), para asegurar un marco adecuado que permita evaluar la rentabilidad de la integración del almacenamiento en aplicaciones conectadas a la red. La estrategia de gestión de energía propuesta es validada a través de dos casos de estudio: una planta renovable individual (eólica o solar) con almacenamiento, y un porfolio de renovables y almacenamiento

Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time